Quantitative Magnetic Resonance Imaging of the Vasculature
20190246938 ยท 2019-08-15
Inventors
Cpc classification
A61B5/4088
HUMAN NECESSITIES
A61B5/055
HUMAN NECESSITIES
G01R33/4816
PHYSICS
G01R33/5601
PHYSICS
G01R33/5635
PHYSICS
International classification
A61B5/055
HUMAN NECESSITIES
A61B5/00
HUMAN NECESSITIES
Abstract
A quantitative, ultrashort time to echo, contrast-enhanced magnetic resonance imaging technique is provided. The technique can be used to accurately measure contrast agent concentration in the blood, to provide clear, high-definition angiograms, and to measure absolute quantities of cerebral blood volume on a voxel-by-voxel basis.
Claims
1. A method of positive-contrast magnetic resonance imaging of a subject, comprising: introducing a paramagnetic or superparamagnetic contrast agent into a region of interest in the subject; applying a magnetic field to the region of interest; applying a radio frequency pulse sequence at a selected repetition time (TR) and at a magnetic field gradient to provide a selected flip angle to excite protons in the region of interest, wherein the repetition time is less than about 10 ms, and the flip angle ranges from about 10 to about 30; measuring a response signal during relaxation of the protons at a selected time to echo (TE) to acquire a T.sub.i-weighted signal from the region of interest, wherein the time to echo is an ultra-short time to echo less than about 300 s; and generating an image of the region of interest.
2. The method of claim 1, wherein the acquired signal is representative of a concentration of the contrast agent in the region of interest.
3. The method of claim 1, wherein the acquired signal is representative of a blood volume in the region of interest.
4. The method of claim 1, wherein the acquired signal comprises an absolute quantitative signal.
5. The method of claim 1, further comprising setting the time to echo (TE) to a value from about 1 us to about 300 s.
6. The method of claim 1, further comprising setting the time to echo (TE) to less than a time in which blood volume displacement in the vascular region is about one order of magnitude smaller than a voxel size.
7. The method of claim 1, further comprising setting the repetition time (TR) to a value from about 2 to about 10 ms.
8. The method of claim 1, further comprising setting the flip angle to a value from about 10 to about 25.
9. The method of claim 1, wherein the image of the region of interest has a contrast to noise ratio of at least 4.
10. The method of claim 1, further comprising measuring the response signal along radial trajectories in k-space.
11. The method of claim 1, further comprising acquiring a purely T.sub.1-weighted signal.
12. The method of claim 1, wherein the magnetic field has a strength ranging from about 0.2 T to about 14.0 T.
13. The method of claim 1, wherein the region of interest comprises a volume fraction occupied by blood and a volume fraction occupied by tissue; and further comprising determining the volume fraction occupied by blood.
14. The method of claim 1, wherein the paramagnetic nanoparticles comprise iron oxide nanoparticles, a gadolinium chelate, or a gadolinium compound.
15. The method of claim 14, wherein the iron oxide nanoparticles comprise a material selected from the group consisting of Fe.sub.3O.sub.4 (magnetite), -Fe.sub.2O.sub.3 (maghemite), -Fe.sub.2O.sub.3 (hematite), ferumoxytol, ferumoxides, ferucarbotran, and ferumoxtran.
16. The method of claim 14, wherein the gadolinium compound is selected from the group consisting of gadofosveset trisodium, gadoterate meglumine, gadoxetic acid disodium salt, gadobutrol, gadopentetic dimeglumine, gadobenate dimeglumine, gadodiamide, gadoversetamide, and gadoteridol.
17. The method of claim 1, further comprising calibrating a magnetic resonance imaging device to determine the selected TR, the selected TE, and a selected flip angle.
18. The method of claim 1, wherein an intensity of the acquired signal is a function of one or more of a time to echo (TE), a repetition time (TR), a flip angle (), a longitudinal relaxation time T.sub.1, a transverse relaxation time T.sub.2*, a calibration constant K dependent on a coil of the magnetic resonance imaging device, a proton density of the region of interest, and magnetic flux densities B.sub.0 and B.sub.1 (+/).
19. The method of claim 1, wherein the region of interest is a vascular region, a tissue compartment, an extracellular space, or an intracellular space containing the contrast agent.
20. A system for magnetic resonance imaging of a region of interest of a subject, comprising: a magnetic resonance imaging device operative to generate signals for forming a magnetic resonance image of the region of interest, and one or more processors and memory, and computer-executable instructions stored in the memory that, upon execution by the one or more processors, cause the system to carry out operations, comprising: operating the magnetic resonance imaging device with a radio frequency pulse sequence comprising: a selected repetition time (TR) and at a magnetic field gradient to provide a selected flip angle to excite protons in the region of interest within a magnetic field generated by the magnetic resonance device, wherein the repetition time is less than about 10 ms, and the flip angle ranges from about 10 to about 30, and a selected time to echo (TE) to acquire a T.sub.1-weighted signal from the region of interest, wherein the time to echo is an ultrashort time to echo less than about 300 s.
Description
DESCRIPTION OF THE DRAWINGS
[0034] The invention will be more fully understood from the following detailed description taken in conjunction with the accompanying drawings in which:
[0035]
[0036]
[0037]
[0038]
[0039]
[0040]
[0041]
(a) A heat map of the SNR and (b) CNR for given TR, TE, , and concentration values.
[0042]
[0043]
[0044]
[0045]
[0046]
[0047]
[0048]
[0049]
[0050]
[0051]
[0052]
[0053]
[0054]
[0055]
[0056]
[0057]
[0058]
[0059]
DETAILED DESCRIPTION OF THE INVENTION
[0060] A quantitative, ultra-short time to echo (TE), contrast-enhanced magnetic resonance imaging (MRI) technique utilizing ultrashort time to echo (UTE) sequences is provided. The UTE limits susceptibility-dependent signal dephasing by giving perivascular effects, extravoxular susceptibility artifacts, and flow artifacts all typically associated with T.sub.2 weighted imaging negligible time to propagate, and also limits the influence of physiological effects, such as blood flow, by saturating a three-dimensional (3D) volume with non-slice selective RF pulses at low repetition time (TR) to create a steady-state signal between TRs, and then by acquiring signals at ultra-short TE values before blood can be displaced between excitation and measurement. This results in snapshots of the vasculature that are independent of flow direction or velocity, arterial or venous systems, or vessel orientation. With optimized pulse sequences (TE, TR, flip angle (FA)), completely T.sub.1-weighted images can be acquired with signal predicted by the Spoiled Gradient Echo (SPGR) equation as a function of concentration.
[0061] More particularly, a paramagnetic or super paramagnetic contrast agent in introduced into a region of interest (ROI) in a subject, and a static magnetic field, using any suitable magnetic resonance imaging (MRI) machine, is applied to the region of interest. A radio frequency pulse sequence is applied at a repetition time (TR) and at a magnetic field gradient to provide a selected flip angle () to excite protons in the vascular region. In some embodiments, the repetition time TR is less than about 10 ms. In some embodiments, TR is from about 2 to about 10 ms. In some embodiments, TR is less than 9 ms, less than 8 ms, less than 7 ms, or less than 6 ms. In some embodiments, the region of interest is saturated with signal pulses at the repetition time (TR). In some embodiments, the flip angle ranges from about 10 to 30. In some embodiments, is from about 10 to about 25.
[0062] A response signal is measured during relaxation of the protons at a selected time to echo (TE) to acquire a T.sub.1-weighted signal from the region of interest. An image of the region of interest can be generated from the received response signal. In some embodiments, the time to echo TE is an ultra-short time to echo (UTE) less than about 300 s. In some embodiments, the ultrashort time to echo (TE) is from about 1 s to about 200 s. In some embodiments, the TE is less than 180 s, less than 160 s, less than 140 s, less than 120 s, less than 100 s, less than 90 s, less than 80 s, less than 70 s, less than 60 s, less than 50 s, less than 40 s, less than 30 s, less than 20 s, or less than 10 s. In some embodiments, the TE is less than a time in which blood volume displacement in a vascular region of interest is about one order of magnitude smaller than a voxel size. In some embodiments, the signal is acquired before magnetization of tissue in a region of interest in a transverse plane dephases. In some embodiments, the signal is acquired before a T.sub.2* decay becomes greater than 2%, or greater than 10%. In some embodiments, the signal is acquired before cross talk between voxels occurs.
[0063]
[0064] The UTE technique described herein is advantageous for a variety of reasons. For example, the technique is quantitative, leading to direct assay of the CA concentration for quantitative MRI. There are no reported techniques that can potentially make absolute measurements in CBV throughout the brain. In some embodiments, the acquired signal is representative of a concentration of the contrast agent in the region of interest. In some embodiments, the acquired signal comprises an absolute quantitative signal.
[0065] More particularly, T.sub.1-weighted snapshot images of vasculature containing CA can be obtained in vivo with UTE by selection of the image acquisition parameters. This is atypical in MRI, in which image contrast is usually only modified in already visible regions by CA. This is exemplified in
[0066] The UTE technique described herein can lead to positive contrast images of the vasculature with very high contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR). In some embodiments, the contrast to noise ratio is at least 4, at least 5, at least 10, at least 15, at least 20, at least 30, at least 40, at least 50, or at least 60.
[0067] The vascular-tissue signal contrast is very high, since there is minimal leakage from the vascular compartment due to the nanoparticle nature of the CA. Vessel wall and form are clearly delineated, as opposed to, for example, time-of-flight (ToF) MRA and phase contrast (PC) MRA.
[0068] When superparamagnetic iron oxide nanoparticles (SPIONs) are used as the contrast agent, the use of Gd-based CA, which can lead to nephrotoxicity, is avoided. SPION formulations typically have a long plasma half-life of nearly 12 hours in humans (6 hours in rats), so that data acquisition is not limited by first-pass clearance, as with Gd-based CAs.
[0069] The technique can achieve purely T.sub.1-weighted angiography and cerebral blood volume, in which susceptibility effects are minimized. The ultra-fast acquisition (that is, ultra-short time to echo) minimizes physical issues that become more significant as time goes on: flow effects, extravoxular susceptibility effects, dephasing of transverse magnetization (T.sub.2* effects), and the like. In this regime the spoiled gradient (SPGR) equation directly applies, enabling quantification of CA, described further below.
[0070] All blood containing regions are equally visible, with signal intensity proportional to both CA concentration and partial blood volume in the voxel. The signal is insensitive to flow, which subsequently eliminates vessel orientation dependence.
[0071] The UTE technique can utilize FDA approved pharmaceuticals such as ferumoxytol and gadofoveset trisodium (commercially available as ABLAVAR) and can be implemented on existing clinical and pre-clinical scanners. It is comparable to CT and PET, while avoiding harmful radiation.
[0072] The UTE technique can be used to provide an effective magnetic resonance angiography (MRA) modality, with less toxicity if SPIONS are used, while retaining superior contrast properties. The present technique can be used to measure absolute quantities of cerebral blood volume on a voxel-by-voxel basis. In some embodiments, the acquired signal is representative of a blood volume in the vascular region of interest. In some embodiments, the blood volume fraction is a cerebral blood volume fraction or a total blood volume fraction. The present technique can be used for functional imaging of brain tissue, in which the health of brain tissue can be assessed for indications of disease as well as quantification of disease progression and to provide specific and quantitative spatial information of regional neuropathy, resulting in improved understanding of neurodegenerative pathogenesis.
[0073] The technique described herein can be used to generate images of a region of interest in humans and in non-human animals. In some embodiments, the region of interest can be a vascular region, a tissue compartment, an extracellular space, or an intracellular space. In some embodiments, the region of interest can be a brain, a kidney, a lung, a heart, a liver, a pancreas, or a tumor, or a portion thereof.
[0074] The technique described here can be used in the diagnosing of a disease or condition. The disease or condition can be a neurodegenerative disease, neuropathy, dementia, Alzheimer's disease, cancer, kidney disease, lung disease, heart disease, liver disease, cardiac diseases or areas around the aorta, ischemia, abnormal vasculature, hypo-vascularization, hyper-vascularization, nanoparticle accumulation in tumors, plaques, bleeding, macrophages, inflammation, or areas around implants or stents or combinations thereof.
[0075] The UTE technique described herein can be used with any paramagnetic or superparamagnetic contrast agent (CA). The technique is particularly useful with superparamagnetic iron oxide nanoparticles (SPIONs), which leads to quantifiable vascular images with superior clarity and definition.
[0076] In some embodiments, the contrast agent is iron oxide nanoparticles. In some embodiments, the iron oxide nanoparticles are Fe.sub.3O.sub.4 (magnetite), -Fe.sub.2O.sub.3 (maghemite), -Fe.sub.2O.sub.3 (hematite). In some embodiments, the iron oxide nanoparticles are ferumoxytol, ferumoxides (e.g., FERIDEX), ferucarbotran (e.g., RESOVIST), or ferumoxtran (e.g., COMBIDEX). In some embodiments, the iron oxide particles are coated with a carbohydrate. In some embodiments, the iron oxide nanoparticles have a hydrodynamic diameter of about 25 nm, measured with dynamic light scattering (DLS). In some embodiments, the iron oxide nanoparticles have a diameter from about 1 nm to about 999 nm, or from about 2 nm and 100 nm, or from about 10 nm to about 100 nm, measured with dynamic light scattering (DLS).
[0077] In some embodiments, the contrast agent is a superparamagnetic iron oxide nanoparticle (SPION). In some embodiments, the SPION is ferumoxytol. Ferumoxytol is an iron-oxide nanopharmaceutical approved by the Food and Drug Administration (FDA) for iron anemia and used off-label for MRI. The iron oxide nanoparticles lead to long blood circulation with minimal leakage from vasculature, resulting in high vascular delineation and high vascular/tissue contrast.
[0078] In some embodiments, the contrast agent is a gadolinium chelate or a gadolinium compound. In some embodiments, the gadolinium compound is gadofosveset trisodium (e.g., ABLAVAR), gadoterate meglumine, gadoxetic acid disodium salt, gadobutrol (e.g., GADOVIST), gadopentetic dimeglumine, gadobenate dimeglumine, gadodiamide, gadoversetamide, or gadoteridol.
[0079] In some embodiments, the contrast agent is introduced in the region of interest at a concentration of about 0.1 to 8 mg/kg for humans and 0.1 to 15 mg/kg for animals. The concentration can be determined by contrast necessity and safety for the human, non-human animal, or substance.
[0080] Any suitable magnetic resonance imaging (MRI) machine or equipment can be used. Suitable MRI machines can be found in clinical or hospital settings, research laboratories, and the like. In some embodiments, the MRI machine can be capable of generating a static magnetic field strength ranging from about 0.2 T to 14.0 T. In some embodiments, the static magnetic field strength can be about 3.0 T or about 7.0 T.
[0081] The MRI machine can be set in any suitable manner to operate at a pulse sequence to provide the UTE technique described herein.
[0082] The MRI machine can be calibrated as described herein. In some embodiments, the MRI machine is calibrated periodically. In some embodiments, the MRI machine is calibrated monthly, weekly, or daily. In some embodiments, the MRI machine is calibrated for each new loading of a subject to be imaged. In some embodiments, the MRI machine is calibrated using a phantom. In some embodiments, the phantom is a vial containing a subject material mixed with a contrast agent. In some embodiments, the subject material is human blood or non-human animal blood.
[0083] The MRI machine can provide an image in any suitable manner. In some embodiments, the image can be a three-dimensional representation of a region of interest. In some embodiments, the image can be a volume of a region of interest. In some embodiments, the image can be a two-dimensional representation of a region of interest. In some embodiments, the image can be a slice of a region of interest.
[0084] In some embodiments, the response signal is measured along radial trajectories in k-space. In some embodiments, the response signal is measured along orthogonal trajectories in k-space.
[0085] In some embodiments, a quantitative contrast-enhanced MRI technique is provided that utilizes an ultrashort time-to-echo (QUTE-CE) has been shown to generate positive-contrast images of a contrast agent, particularly using superparamagnetic iron oxide nanoparticles (SPIONs), in vivo. Ultra-fast (e.g. 10-300 s) signal acquisition has the benefit of producing positive contrast images, instead of dark contrast images, by acquiring signal before tissue magnetization in the transverse plane dephases, thus allowing complete T.sub.1 contrast enhancement from SPIONs. Thus, UTE is suited for measuring the concentration from clinically relevant concentrations of FDA-approved ferumoxytol. The technique utilizes CA-induced T.sub.1 shortening, combined with rapid signal acquisition at ultra-short TEs, to produce images with little T.sub.2* decay.
[0086] Prior art MRI techniques remain semi-quantitative because they are inherently sensitive to extravoxular susceptibility artifacts, field inhomogeneity, partial voluming, perivascular effects, and motion/flow artifacts. Imaging techniques that employ a time-to-echo (TE) of half a millisecond or more are particularly susceptible to heterogeneous signal modifications and are therefore difficult to interpret. Thus, the relationship between MRI signal intensity and CA concentration is widely recognized to be complex and nonlinear. Nevertheless, current models for contrast CA quantification assume a linear relationship between signal intensity and CA concentration or a linear relationship between CA concentration and relaxivity. Published methods to quantify CA concentration generally rely on the linear relationship between either measured signal intensity or R.sub.1 relaxation rate and concentration. There still remains a high degree of error with this approach in vivo, reported on the order of 15-30%. This high error is due to heterogeneous, non-linear signal changes that are not adequately described by theory when measuring in vivo. Complex non-linear modeling has shown limited success (139% error in vivo), but is sensitive to subtle effects from magnetic susceptibility, imperfect B.sub.0 shimming, and chemical shifting. All of these complications become stronger at longer TEs. These complications can be overcome, however, by the present technique, which employs ultrashort TEs.
[0087] In some embodiments of the technique here, with an optimized pulse sequence (TE, TR, FA), completely T.sub.1-weighted images can be acquired with signal predicted by the SPGR equation as a function of concentration. The quantitative nature of QUTE-CE signal has been demonstrated by accurately measuring the clinically relevant intravascular concentration of Ferumoxytol, an FDA approved iron-oxide nanopharmaceutical, in mice. Indeed, previous techniques that employ gadolinium are limited by toxicity and residence time, while other techniques that employ iron-oxide nanoparticles are limited by negative contrast, SNR and requirement of high concentration. All previous techniques are only semi-quantitative, since they require a baseline, produce results based on relative changes, or have too high a degree of error. However, the QUTE-CE technique provides positive-contrast, high SNR and CNR, since organs are invisible in pre-contrast images at 7.0 T, and a signal completely contingent on intravoxular blood volume and concentration of contrast agent.
[0088] More particularly, the signal in the UTE images is quantitative and directly indicative of CA concentration. QUTE-CE can utilize CA-induced T.sub.1 shortening, combined with rapid signal acquisition at ultra-short TEs, to negate T.sub.2* decay (>1% signal decay by TE). Under certain approximations, the UTE signal intensity can be approximated by the spoiled gradient echo (SPGR) equation.
[0089] The image intensity in a given voxel measured by QUTE-CE MRI is a function of both image acquisition and material parameters:
I=f(TE,TR,:T.sub.1,T.sub.2*:K,)
where TE is the time-to-echo, TR is the repetition time, and is the flip angle. TE, TR, and are image acquisition parameters defined by the user. T.sub.1 and T.sub.2* are the longitudinal and transverse relaxation times, respectively, that depend on the medium under investigation and the magnetic field strength applied by the MRI machine. K is a constant that is determined by the UTE signal intensity as seen by the coil of the MRI machine, and is the proton density of the medium. For ultrashort TE values, T.sub.2* effectively equals T.sub.2.
[0090] T.sub.1 and T.sub.2 can be written in terms of their reciprocals, relaxation rates R.sub.1 and R.sub.2, respectively, for the facile determination of relaxivity constants. For imaging at a single magnetic field strength, the explicit field dependence is constant and can be omitted. The medium under investigation is a desired contrast agent approximately uniformly mixed in blood. Thus, R.sub.1 and R.sub.2 are a function of the initial relaxation rate of the blood (R.sub.1o and R.sub.2o), the longitudinal and transverse concentration-dependent relaxivities (r.sub.1 and r.sub.2), and the contrast agent at given concentrations C.
[0091] For concentrations in which the relaxation rate is linear,
R.sub.1=R.sub.1o+r.sub.1C(1)
R.sub.2=R.sub.2o+r.sub.2C(2)
[0092] The UTE signal intensity can be approximated by the spoiled gradient echo (SPGR) equation:
[0093] Once the relaxivity constants have been obtained, the image acquisition parameters have been established, and K has been calibrated, unknown CA concentrations can be quantified experimentally using Equation 4. Thus, after calibration and having knowledge of relaxation constants of blood R.sub.1o, r.sub.1, R.sub.2o and r.sub.2, a vascular region of interest can be scanned in vivo or in vitro to produce quantitative images.
[0094] One embodiment of a procedure is described with reference to
[0095] In step 1, calibration phantoms containing blood (1% heparin) are doped with clinically relevant concentrations of ferumoxytol (0-150 g/mL).
[0096] In step 2, for each calibration sample, T.sub.1 and T.sub.2 are measured, from which the relaxivity constants R.sub.1o, r.sub.1, R.sub.2o, and r.sub.2 can be extrapolated. See
[0097] In step 3, a UTE protocol is established with optimized TE, TR, and image acquisition parameters and a fixed trajectory, precalculated with a symmetric phantom, described below.
[0098] In step 4, K is measured together with and K, assuming the proton density of whole blood is constant, and serves as a calibration for the given UTE protocol.
[0099] In step 5, positive-contrast images using the optimized parameters are acquired in vivo.
[0100] In step 6, CA concentrations in each voxel are calculated directly from UTE signal intensity, by application of the SPGR equation (Equation 4).
[0101] Unlike the four relaxivity constants in Equation (4), which only need to be measured for each magnetic field strength, K is a constant that needs to be determined for each imaging protocol, as it depends on acquisition parameters (TE, TR, , matrix size) and coil hardware of the MRI machine to be used. Thereafter, K can be used for all subsequent scans. Calibrating K can be executed as follows:
[0102] 1. Phantoms of blood doped with the desired contrast agent are prepared at known concentrations.
[0103] 2. A UTE protocol with specific determined acquisition parameters is performed using the prepared phantoms.
[0104] 3. Regions of interest are drawn on the images inside the vials in the center Z-axis axial slice of the three-dimensional (3D) image to obtain a mean intensity and standard deviation.
[0105] 4. The intensity is used in conjunction with the SPGR equation to determine K (TE, TR, , C are known parameters, and relaxivity constants can be measured, as described herein).
[0106] 5. The average value of K is taken as a calibration constant.
[0107] Once this procedure is completed, K can be used for all subsequent quantitative calculations using this protocol.
[0108] Because the acquired signal is quantitative, the technique can be applied to other applications, in particular, partial blood volume measurements using two volume methods, and identifying accumulated nanoparticles, such as superparamagnetic iron oxide nanoparticles (SPIONs). Thus, in some embodiments, this technique can be used for applications such as tumor vascular imaging and subsequent nanoparticle accumulation therein. In some embodiments, the technique can be used to probe the brain in an attempt to obtain a quantitative biomarker for vascularity. In some embodiments, the technique can be used for diagnostic functional imaging and image-guided drug delivery with an appropriate contrast agent.
[0109] For example, enhanced permeability and retention (EPR) describes the propensity of some tumors to passively accumulate nanoparticles. Although the EPR effect holds promise for increased delivery of chemotherapeutics to tumors, it is difficult to assess whether or not nanoparticle chemotherapy will result in significantly greater benefits than a standard chemotherapeutic treatment. It is difficult to predict the amount of EPR both between patients and between metastatic tumors in the same patient. Superparamagnetic iron-oxide nanoparticles (SPIONs) have been employed as surrogates for predicting secondary nanoparticle accumulation in clinical trials, but imaging performed with negative contrast suffers from poor discrimination of nanoparticle accumulation in heterogeneous tissue (see, for example,
[0110] In some embodiments, the technique can be used to determine blood volume fractions. In some embodiments, a partial blood volume of a region of interest can be determined. In some embodiments, a cerebral blood volume fraction can be determined.
[0111] More particularly, T.sub.2- and T.sub.2*-weighted images are sensitive to perivascular effects, extravoxular susceptibility artifacts, and flow artifacts. However, in UTE the signal is restricted to effects that occur intravoxularly and flow effects are completely suppressed by non-slice selective RF pulses. Thus, the measured signal from any given voxel is given by a combination of intensity from the fraction of the volume occupied by tissue, f.sub.T, and fraction occupied by CA-doped blood, f.sub.B
I.sub.measured=f.sub.BI.sub.B+f.sub.TI.sub.T(5)
where I.sub.T is the tissue intensity, I.sub.B is the blood intensity and I.sub.M is the total measured intensity. This equation makes an implicit assumption that only blood and tissue are present in each voxel and that the tissue itself is approximately homogeneous within a single voxel. It follows from this base assumption that, f.sub.T=1f.sub.B. Thus, if blood and tissue intensities (I.sub.B and I.sub.T) are known, then f.sub.B can be measured directly from any scan as simply,
However, if these intensity values are not known then it is necessary to perform at least two scans. By performing both a pre-contrast and post-contrast scan, two measurements per-voxel, I.sub.M and I.sub.M respectively, can be made. Then changes in the measured intensity can be assessed using Equation 5,
1.sub.M=f.sub.BI.sub.Bf.sub.BI.sub.B+f.sub.T,I.sub.Tf.sub.TI.sub.T(7)
where all primes denote values in the post-contrast injection scan. Provided that the subject is in the same neurological state, it can be assumed that, f.sub.B=f.sub.B and f.sub.T=f.sub.T. Further, assuming the contrast agent is entirely confined to the vasculature then on a per-voxel basis, I.sub.TI.sub.T. Using these assumptions, f.sub.B can be solved for, such that,
This equation is sufficient for calculating the blood fraction given a pre-contrast scan of a subject in the same or substantially the same functional state. This is adequate, for example, for a quantitative cerebral blood volume atlas of a subject animal, since the subject animal can be anesthetized pre- and post-contrast. To determine functional CBV information when the CBV is assumed to be changing, then, using Equation 7 and assuming that I.sub.TI.sub.T without assuming the same initial fraction of blood, the equation becomes,
Here, f.sub.B is the blood fraction if the precontrast image utilized for I.sub.M is in the precontrast state. Through the application of this equation, CBV can be determined in scans for which CBV is assumed to have changed between pre- and post-contrast. In some embodiments, this equation can be used between an anesthetized pre-contrast scan and the non-anesthetized post-contrast scan of a subject animal.
[0112] By utilizing the QUTE-CE technique, the physical problems of acquiring signal late after excitation can be addressed: measurements are made with negligible blood displacement and extravoxular susceptibility and signal dephasing is eliminated at low TEs. Inter-TR flow effects can be suppressed by using a broad suppression pulse, which produces T.sub.1-weighted positive contrast images with signal intensity per voxel proportional to the amount of contrast-agent doped blood, or CBV. Additionally, these measurements can be completely insensitive to blood oxygenation and the contrast agent concentration can be in the clinically appropriate range. These results clearly demonstrate the capability of the present technique QUTE-CE to measure absolute CBV with sufficient accuracy to enable an advantageous approach to functional MRI.
[0113] In some embodiments, the technique provides an enhanced signal to noise ratio (SNR) and/or an enhanced contrast to noise ratio (CNR). The SNR is defined as the average signal from an ROI drawn in the media divided by the standard deviation of the noise determined by an ROI located outside the sample in air. In some embodiments, a difference in SNRs of doped- and undoped-media can be used to determine CNR in vitro. In some embodiments, the CNR can be computed by subtracting the SNR of a region containing primarily tissue from the blood SNR. A time-adjusted SNR and CNR take into account the duration of a scan by dividing by {square root over (TR)}, which normalizes SNR and CNR by the duration of the scan. In some embodiments a contrast efficiency can be determined, as follows:
EXAMPLES
Example 1
[0114] In one example, a contrast-enhanced, 3D UTE technique was used for cardiac and thoracic angiography imaging in mice. Contrast-enhanced 3D UTE imaging with ferumoxytol produced images in which pre-contrast most organs are completely invisible (
[0115] 1.1 One-Hundred UTE Experiments Reveal an Optimal Zone at 7 T
[0116] The ability to predict CA concentrations from UTE intensity using the SPGR equation is influenced by image acquisition parameters TE, TR, and . A 3D UTE radial k-space sequence, readily available from the Bruker toolbox, was selected and an imaging protocol was established a with FOV (333 cm.sup.3), matrix mesh size (128128128), and 51,360 radials, which rendered 234 m x-y-z resolution images with a 3 m scan time for TR=3.5 ms. The image reconstruction trajectory was fixed using a 5 mM copper sulfate (CuSO.sub.4) phantom constructed from a 50-ml centrifuge tube. Experiments were performed on whole calf and mouse blood (1% heparin) doped with ferumoxytol (0-250 g/ml). A high bandwidth (BW) radiofrequency (RF) pulse was used to avoid complications for cases in which a low BW compared to T.sub.2* may cause a curved trajectory for the magnetization vector M.sub.z out of the z-plane. Assuming T.sub.2*T.sub.2 at ultra-short TE values, the 200 kHz BW yielded ultrafast excitation compared to the lowest T.sub.2 value of 5.5 ms at 150 g/ml. All experiments performed on acquisition parameters optimization were performed with a 72 mm Bruker quad coil.
[0117] For calf blood, 100 scans were executed covering combinations of 5 TEs (13, 30, 60, 90, and 120 s), 5 TRs (3.5, 5, 7, 9, and 11 ms) and 4 s (10, 15, 20, and 25). Six 2-ml phantoms of ferumoxytol-doped calf blood at (0-250 g/ml ferumoxytol) were arranged in pentagonal fashion with the 0 g/ml vial at the center inside of a 72-mm Bruker quad coil. K was calibrated per image, with the 0 concentration exceptionally excluded in calculations because the noise from surrounding high concentrations rendered a poor measurement. It was found that higher concentration UTE signals deviated from the SPGR equation, owing to the non-linear behavior of the relaxation rate at high concentrations; thus only , 50, 100 and 150 g/ml phantoms were considered in the analysis in
[0118] The SNR was defined as the average signal from an ROI drawn in the media divided by the standard deviation of the noise determined by an ROI located outside the sample in air. ROIs for these measurements were drawn in the center z-slice of the phantom tubes. A difference in SNRs of doped- and undoped-media were used to determine CNR in vitro. The time-adjusted SNR and CNR take into account the duration of the scan by dividing by {square root over (T)}R, which normalizes SNR and CNR by the duration of the scan. The time-corrected SNR and CNR also tended to be higher in the optimal zone (
[0119] To ensure validity of phantom measurements, experiments were repeated with mouse blood with 5 TE values (14, 30, 60, 90, and 120 s) and 5 TR values (4, 5, 7, 9, and 11 ms) at =20. Six 2-ml vials of ferumoxytol (50, 75, 100, 125, 150 and 175 g/ml) were arranged around a center vial of 5 mM copper sulfate (CuSO.sub.4). The same pattern for the optimal zone was confirmed in mouse blood, with absolute concentration errors similar to the previous experiment.
[0120] 1.2 QUTE-CE Calibration and Validation
[0121] To establish the UTE protocol, the following parameters were fixed: FOV (333 cm.sup.3), matrix mesh size (200200200), TE (13 s), TR (4 ms), and (20). TR was slightly higher than the optimal value because of hardware and memory constraints. A 50-ml cylindrical phantom filled with 5 mM CuSO.sub.4 was analyzed to fix a reconstruction trajectory.
[0122] Phantoms (0-150 g/ml ferumoxytol) were placed one at a time for calibration of K to produce ideal images with low noise (
[0123] To assess in vitro performance of QUTE-CE, doped phantoms were created by serial dilution of ferumoxytol from 128 and 96 g/ml (
[0124] A linear correlation (R.sup.2=0.998) was observed between the measured and known ferumoxytol concentrations (
[0125] 1.3 Quantification of Blood Pool Ferumoxytol In Vivo
[0126] All animal experiments were conducted in accordance with the Northeastern University Division of Laboratory Animal Medicine and Institutional Animal Care and Use Committee. QUTE-CE was used to measure the concentration of ferumoxytol in the blood of mice using the same imaging protocol, coil, trajectory measurement and calibration for in vitro measurements in the QUTE-CE calibration and validation discussed above. Ferumoxytol is approved for an intravenous injection of 510 mg in humans. Assuming an average adult blood volume of 5 L, a single bolus of ferumoxytol is expected to produce initial blood concentration of about 100 g/ml. To remain clinically relevant in the selection of concentrations, starting blood concentrations of 100-200 g/ml in mice was aimed for.
[0127] Healthy anesthetized Swiss Webster mice (n=5) received a one-time i.v. bolus injection of 0.4-0.8 mg ferumoxytol for a starting blood pool concentration of 100-200 g/ml (diluted to 4 mg/ml in PBS) and were imaged longitudinally after injection (0 h, 2 h and 4 h). Pre-contrast images were also acquired. Given the assumption that blood in mice is about 7% of body weight, for a 50 gr mouse an initial yield of 115-230 g/ml was predicted. This is similar to clinical concentrations where an injection of 510 mg produces a blood concentration of about 100 g/ml for a total blood volume in the average adult human of 5 L.
[0128] A single UTE protocol was used for all images. To establish the UTE protocol, the following parameters were fixed (as above for QUTE-CE calibration and validation): FOV (333 cm.sup.3), matrix mesh size (200200200), TE (13 s), TR (4 ms), and (20). TR was slightly higher than the optimal value because of hardware and memory constraints. A 50-ml cylindrical phantom filled with 5 mM CuSO.sub.4 was analyzed to determine the k-space trajectories for image reconstruction.
[0129] Reconstructed 3D intensity image data was re-scaled back to the original intensity measurement (as necessary with Bruker file format files, one must divide by the receiver gain and multiply by scaling factor called SLOPE). Intensity data was then converted to concentration via theory using a custom MATLAB script to solve numerically the nonlinear SPGR intensity using Equation 4.
[0130] Mice were imaged longitudinally after injection (0 h, 2 h and 4 h). Each imaging session was followed by a submandibular bleed (200 l) to obtain blood for elemental iron analysis. Pre-contrast images were also acquired. Comparison of the pre-contrast (
[0131] To quantify the blood pool ferumoxytol concentration, blood draws were performed after each imaging session and quantified by inductively coupled plasma atomic emission spectroscopy (ICP-AES) analysis (
[0132] 1.4 Inductively Coupled Plasma Atomic Emission Spectroscopy (ICP-AES)
[0133] ICP-AES was performed to analyze the iron-oxide nanoparticle (IONP) content in doped whole animal blood. Briefly, preparation of IONP-doped media involved the full digestion of the sample in a Milestone Ethos Plus Microwave. Full digestion was achieved by taking 0.1 ml of sample and adding 6 ml of concentrated nitric acid, 2 ml of hydrogen peroxide and 2 ml of pure water, and running a protocol on the microwave that ramped the temperature up to 210 C. for 15 minutes. Following digestion, the samples were dried, resuspended in 5 ml of 2% nitric acid, and measured using ICP-AES. A standard curve utilizing a mono-elemental iron was run to ensure high instrument fidelity (r.sup.2=1.000). Each data set (n=5) was fitted with the pooled slope and average intercept (n=3 per set) to account for offsets in baseline iron content, for a total of n=15 in vivo measurements.
[0134] 1.5 Conclusions on Vascular SPION Concentration Measurements
[0135] By choosing optimized image acquisition parameters to minimize the error in concentration, including an ultra-short TE, the SPGR equation could be used to accurately measure ferumoxytol concentrations in vitro and in vivo. This optimized UTE protocol allows signals to be acquired microseconds after excitation, before cross-talk between voxels can occur, thereby eliminating both extra-voxular susceptibility and flow effects. Indeed, the average blood flow velocity in mice is 10-100 mm/s (excluding the largest arteries), and thus blood displacement is two orders of magnitude less than the voxel size during image acquisition. A low TR suppresses flow effects for concentration quantification as well as suppressing pre-contrast tissue signal, rendering high SNR and CNR ratios similar to those observed in vitro. This optimization of the UTE protocol yields a strong correlation between the theory and experimental measurements, allowing the QUTE-CE image contrast to be quantified with 2-4 more precision than other reported techniques.
[0136] Longitudinal QUTE-CE measurements can be used to determine pharmacokinetic parameters. The ability to distinguish time-dependent changes in blood pool ferumoxytol concentration was demonstrated with a precision of about 0.1 mM at 7 T up to about 3 mM for the estimation of CA half-life. These measurements were independently validated ex vivo using ICP-AES. The ferumoxytol half-life measured in mice by QUTE-CE (3.920.45 hr) is comparable to that measured by others using radiolabelled ferumoxytol in rats (3.9 hr) and rabbits (4.4 hr). QUTE-CE concentration measurements are extrapolated directly from UTE signal intensities, without pharmacokinetic modeling or image registration. As such, no assumptions about tissue structure or function, or heterogeneities contained therein, are required for concentration analyses. This ability to longitudinally quantify blood pool CA concentration is an advantage of the QUTE-CE technique.
[0137] In summary, the technique described here allows clinically relevant concentrations of ferumoxytol to be measured non-invasively and quantitatively with high precision. QUTE-CE data shows excellent agreement with theory with image acquisition parameters optimized to reduce error. The robustness of this technique is based on the use of an ultra-short TE, which allows the SPGR equation to be applied. Longitudinal measurements of blood pool ferumoxytol can be acquired in vivo with high precision for estimation of ferumoxytol half-life. This ability to longitudinally quantify blood pool CA concentration is an advantage of the QUTE-CE method, and makes MRI competitive with nuclear imaging.
Example 2
[0138] In one example, the technique was applied to demonstrate measurement of nanoparticle accumulation in tumors in mice.
[0139] 2.1 Methods
[0140] All animal experiments were conducted in accordance with the Northeastern University Division of Laboratory Animal Medicine and Institutional Animal Care and Use Committee. MRI images were obtained at ambient temperature (25 C.) using a Bruker Biospec 7.0 T/20-cm USR horizontal magnet (Bruker, Billerica, Mass., USA) equipped with a 20-G/cm magnetic field gradient insert (ID=12 cm, Bruker) and the same quadrature 300 MHz, 30 mm Mouse MRI coil was used for all in vivo work as previously utilized for mouse experiments above in Example 1 (Animal Imaging Research, LLC, Holden, Mass., USA).
[0141] PC 3 cells were injected into the right flank of immunocompromised FoxNui mice (n=5, Charles River Laboratories). After tumors reached about 0.5-1.0 cm.sup.3, animals underwent three separate imaging sessions: Session 1pre-contrast T.sub.1, T.sub.2 and QUTE-CE measurements, Session 2immediate post-contrast QUTE-CE measurement and Session 3-24 h post-contrast T.sub.1, T.sub.2 and QUTE-CE measurements. For contrast, 100 l of ferumoxytol diluted to 6 mg/ml was injected i.v. to render a blood concentration of 200 g/ml Fe (2 clinical dose).
[0142] T.sub.1 and T.sub.2 measurements were made with the Bruker RAREVTR and MSME sequences respectively, similar to the characterization study in Example 1. Tumors 1-4 had slightly different scan T.sub.1 and T.sub.2 protocols than tumor 5. Protocol for scans 1-4 was the following: RAREVTRs of [600 800 1200 1800 4000] ms were used to make the fits for T.sub.1 with TE=7.37 ms, averages=2, FOV=0.30.3 cm.sup.2, matrix size=100100, 50 slices with 0.3 cm thickness with no slice overlap, and total scan time of 28 m 0 s. For MSME, echos were at [10 20 30 40 50 60 70 80 90 100] ms, TR=6000, averages=2, FOV=0.30.3 cm.sup.2, matrix size=100100, 50 slices with 0.3 cm thickness and no slice overlap with a total scan time of 20 m 0 s. For tumor 5 the protocols were: RAREVTRs of [600 800 1200 1800 4000] ms were used to make the fits for T.sub.1 with TE=7.37 ms, averages=2, FOV=0.30.3 cm.sup.2, matrix size=100100, 50 slices with 0.3 cm thickness and a negative slice-gap of 0.1 mm to reduce noise with a total scan time of 19 m 33 s. For MSME, echos were at [10 20 30 40 50 60 70 80 90 100] ms, TR=6000, averages=2, FOV=0.30.3 cm.sup.2, matrix size=100100, 50 slices with 0.3 cm thickness and a negative slice-gap of 0.1 mm to reduce noise with a total scan time of 20 m 0 s. The same 3D UTE protocol was used as in Example 1, with TE=13 s, TR=4 ms, FA=20, isotropic FOV=0.3 mm.sup.3 and matrix=200.sup.3, with a total scan time of 13 m 56 s. Every attempt was made to produce high-quality images that could be compared to QUTE-CE data.
[0143] 2.2 QUTE-CE Rendered Unambiguous Contrast of SPIONs in Tumors
[0144] Contrary to more standard MRI techniques, QUTE-CE pre-contrast images rendered a nearly homogenous signal with a Gaussian distribution in the tumor (
[0145] 2.3 Angiography and TBV in Tumors
[0146] Assuming a partial 2-volume model of blood and tissue (as discussed further below with regard to rat brain imaging), it is possible calculate the tumor blood volume (TBV). In this example, this was performed using Equation 5.4, taking an average value for the pre-contrast intensity (instead of voxel by voxel subtraction), since the overall distribution had been shown to be Gaussian, and for the same reason assuming that the pre-contrast blood value was indistinguishable from the pre-contrast tissue intensity, setting them equal. While these approximations are apparently valid given the distribution of pre-contrast signal intensity, it is also noted that for a more complete measurement one would not only have to have an accurate registration of pre- and post-contrast images, but also have measurements of the B.sub.0 and B.sub.1 fields to remove effects of signal inhomogeneity. The resultant approximation for CBV is shown in
[0147] 2.4 Comparisons of QUTE-CE Contrast to Standard Techniques
[0148] The standard prior art technique to quantify SPION accumulation is to take T.sub.2 measurements pre- and post-contrast and visualize accumulation via a subtraction image. It is less likely that T.sub.1-subtraction would be performed in the prior art, because of the very low r.sub.1/r.sub.2 ratio, which greatly favors rendering T.sub.2- or T.sub.2*-weighted imaging. In contrast, because QUTE-CE is purely T.sub.1-weighted, images in
[0149] The heterogeneity of the tumor for T.sub.2 contrast necessitated a post-contrast imaging session to delineate particle accumulation (
[0150] CNR from QUTE-CE is measurably superior in 3 out of 5 of the PC3 tumor ROIs (
[0151] In addition to these measurements, the contrast efficiency was also calculated (Table 1), using Equation 10 above. The total volume space was taken as 333 cm.sup.3, or 27 cm.sup.3, and the subset of that volume per scan was spherical for QUTE-CE with a 3 cm diameter and Cartesian for T.sub.1 and T.sub.2 images with 331.5 cm.sup.3 space for Tumors 1-4, and 331.5 cm.sup.3 for Tumor 5. Over the 5 tumors, QUTE-CE outperformed T.sub.2 imaging in terms of contrast efficiency by 1.020.44 vs. 0.980.41.
TABLE-US-00001 TABLE 1 CNR and contrast efficiency PC3 tumor ROIs CNR (n = 5 tumors) T1 std T2 std QUTE-CE std Tumor 1 2.45 0.60 5.29 1.10 9.16 2.52 Tumor 2 2.21 0.67 4.10 1.61 4.99 1.81 Tumor 3 0.80 0.60 2.02 4.06 2.64 2.12 Tumor 4 1.81 0.57 6.35 2.39 4.08 1.99 Tumor 5 1.90 0.79 9.45 2.11 2.52 1.63 Average 1.83 0.65 5.44 2.25 4.68 2.01 Contrast Efficiency * 100 (n = 5 tumors) T1 std T2 std QUTE-CE std Tumor 1 0.39 0.09 0.99 0.20 2.00 0.55 Tumor 2 0.35 0.11 0.76 0.30 1.09 0.39 Tumor 3 0.13 0.09 0.38 0.76 0.58 0.46 Tumor 4 0.28 0.09 1.18 0.44 0.89 0.43 Tumor 5 0.27 0.11 1.60 0.36 0.55 0.36 Average 0.28 0.10 0.98 0.41 1.02 0.44
[0152] Thus, delineating SPION accumulation using QUTE-CE was advantageous compared to T.sub.2 or T.sub.1 imaging, in that the post-contrast image contains sufficient information for nanoparticle localization, eliminating the need for pre-contrast images (
[0153] 2.5 Conclusions on Tumor Imaging with QUTE-CE
[0154] An advantage of delineating SPION accumulation using QUTE-CE, compared to T.sub.2 or T.sub.1 imaging, is that the post-contrast image contains sufficient information for nanoparticle localization, eliminating the need for pre-contrast images (
Example 3
[0155] In one example, the technique was applied to accurately measure CA concentration in the blood of mice as well as provide a new angiogram, measuring absolute quantities of CBV on a voxel-by-voxel basis. A quantitative blood volume atlas of the rat brain was developed, both in terms of absolute CBV and capillary blood volume, demonstrating that the technique can be utilized for quantitative steady-state functional imaging by measuring changes in CBV in the rats induced by a 5% CO.sub.2-challenge and anesthesia by 3% isoflurane.
[0156] 3.1 Methods
[0157] All animal experiments were conducted in accordance with the Northeastern University Division of Laboratory Animal Medicine and Institutional Animal Care and Use Committee. MRI images were obtained at ambient temperature (25 C.) using a Bruker Biospec 7.0 T/20-cm USR horizontal magnet (Bruker, Billerica, Mass., USA) equipped with a 20-G/cm magnetic field gradient insert (ID=12 cm, Bruker). Healthy anesthetized Sprague Dawley (SD) rats (n=12), average weight 300 g, were fitted with an i.v. tail vein catheter capped with heparinized saline. SD rats are widely used to study varying neuropathies. They are also a generalized strain of lab rat. For these reasons, SD rats were chosen for this study, and provide an avenue for future comparison for studies involving neuropathy. Rats were subsequently placed into a custom rat imaging apparatus capable of awake-animal imaging. Since the animals are awake for part of the imaging session, the animals were first habituated to the imaging process and restraint apparatus over a period of 4-5 days.
[0158] The imaging experiment included one pre-contrast anesthetized scan and three post-contrast 3DUTE scans taken with optimized parameters: FOV (333 cm.sup.3), matrix mesh size (200200200), TE=13 s, TR=4 ms, and =20. For contrast, a bolus injection of 0.7 ml of ferumoxytol diluted to 6 mg/ml was injected after the pre-contrast scan to get a blood concentration of about 200 g/ml Fe (2 clinical approval). Following contrast injection, three scans were taken to assess the various states after leaving the animal 15 minutes to awaken completely from anesthesia. First, 5% CO.sub.2 was delivered to the rat and after 1-2 minutes of this condition the scan was initiated. Next, the 5% CO.sub.2 gas was replaced with air at the same flow-rate, and after 1-2 minutes the scan was initiated. Third, 3% isoflurane gas replaced the air, and after 1-2 minutes of this condition the scan was initiated. Isoflurane percent was reduced in the case of respiration becoming lower than about 20-30 breaths/minute.
[0159] 3.2 Cerebral Angiographic Imaging in Rats
[0160] QUTE-CE produced MRAs with quantitative signal measurements in vasculature.
[0161] This technique differs from TOF and PC imaging. From
[0162] 3.3 Signal Inhomogeneity and Quantitative Measurements
[0163] The homogeneity profile of B.sub.0 and B.sub.1.sup.+/ was also accounted for by noting the physical design of the excitation/recording coil of the MRI equipment. A rat-brain 300 MHz, 30 mm diameter (Animal Imaging Research, LLC, Holden, Mass., USA) quadrature coil was used for all measurements. Quadrature coils have the added benefit of more efficiently exciting and measuring the circularly polarized spins, with an overall gain of {square root over (2)}SNR. Both channels were assumed to operate with minimal coupling and each was subject to thermal noise which was assumed to induce standard Gaussian distributions in their recordings. The signals actually received by these individual channels in frequency (or wave-number) space were then Fourier transformed into position space and combined into a single magnitude image with the aforementioned intensity, I.sub.M. To denote the fact that these channels were orthogonal and because the Fourier transform does not affect complexity (in the sense of complex numbers), one channel was labeled real and the other imaginary. Thus spatial images of each channel were created separately with intensities labeled I.sub.r and I.sub.i respectively. It follows that the measured magnitude intensity at each voxel is I.sub.M={square root over (I.sub.r.sup.2+I.sub.i.sup.2)} to reflect the vector addition of these two orthogonal channels. The Fourier transform did not alter the Gaussian shape of the probability distribution governing the noise on each channel (only change its parameters) but this transform into the magnitude image was a nonlinear mapping which altered the probability distribution. Thus, if a completely physically homogeneous sample were used, the recorded signal would have some spatial dependence which reflects a limitation of the measurement rather than any property of the actual sample. To address this to quantify CBV absolutely without a potential spatial effect, therefore, a physically homogeneous phantom experiment was required to characterize this signal inhomogeneity and the two channels were characterized separately. Note also that in quadrature detection there is always a small bias in the measured intensity, which is introduced because magnitude mapping produces Rician rather than Gaussian distributions. However, this statistical bias was determined to be relatively small, because the Rician distribution approaches Gaussian above SNRs of 2 or 3.
[0164] 3.4 Characterization of Signal Dependence on Field Inhomogeneity
[0165] In order to model inhomogeneity as close as possible to the actual imaging sessions, particularly because B.sub.1.sup. is dependent on coil loading, it was necessary to replicate a circumstance in which there would be similar loading, brain/skull susceptibility interface, etc. Thus, a phantom experiment was performed on euthanized rats immediately following in vivo experiments. Specifically, blood was excised from the rat (previously subject to contrast-enhancement with ferumoxytol injection) via cardiac puncture and was injected into the hollowed cranial space of each rat's skull immediately following the final 3DUTE scan. Dead rat blood phantoms (n=11) were then imaged in precisely the same manner as the living rat. An example of a 3DUTE image from these phantoms can be seen in
[0166] From this data, traces of the average signal and standard deviation of the homogeneous blood for each slice along the z-axis were collected and graphed together in
[0167] In order to characterize the signal inhomogeneity, a 6.sup.th-degree polynomial function was fit to the intensity profiles along the z-axis from the rat blood phantom ensemble. The traces were first normalized by dividing by their corresponding values at the center z-slice and the error associated with this, .sub.j, was propagated through from the standard deviations. This measure of certainty was used to weight each point (according to inverse variance,
for robust least absolute residual based fits. The collection of data points and corresponding fit functions can be seen in
TABLE-US-00002 TABLE 2 Coefficients of 6.sup.th degree polynomial fitting function Real Coefficients (R2 = 0.9953) Imaginary Coefficients (R2 = 0.9903) =6.146e12 (6.279e12, 6.014e12) a = 7.42e13 (8.161e13, 6.679e13) b = 3.764e09 (3.683e09, 3.845e09) b = 4.082e10 (3.647e10, 4.517e10) c = 9.211e07 (9.408e07, 9.015e07) c = 9.005e08 (1e07, 8.01e08) d = 0.0001137 (0.0001113, 0.0001161) d = 1.047e05 (9.35e06, 1.16e05) e = 0.007353 (0.007504, 0.007201) e = 0.0007158 (0.0007804, 0.0006511) f = 0.2375 (0.2327, 0.2422) f = 0.03056 (0.0288, 0.03232) g = 2.299 (2.355, 2.242) g = 0.2961 (0.2787, 0.3135)
[0168] 3.5 Ex Vivo Confirmation of Quantitative Signal in Rat Brain
[0169] In order to compute the CBV in vivo, one must obtain the intensity from a whole blood-filled voxel, as described above. In order to achieve this, ROIs were drawn along the superior sagittal sinus of the rat in 3DSlicer using the LevelTracingEffect tool, and the mean blood value was taken as I.sub.B (see Equation 8). This value was compared to the intensity from the rat blood phantom, from which the excised blood was taking immediately following the anesthetized image (value in ROI at center z-slice). This was done as a check to determine if the methodology for obtaining I.sub.B was valid. The two intensity values were close (
[0170] 3.6 Quantitative Cerebral Blood Volume Atlas
[0171] After applying the inhomogeneity correction, it is feasible to measure CBV in an absolute quantitative way throughout the brain. By taking pre-contrast and post-contrast images of 12 anesthetized Sprague Dawley rats, Equation 5.4 was directly applied on a per-voxel basis. A 174-region anatomical Atlas developed by the Center for Translational Neuroimaging (CTNI) at Northeastern University was utilized, shown in
[0172] There were approximately 550,000 voxels per QUTE-CE scan for each rat brain distributed throughout different regions. Concerning the distributions of CBV fraction per region, CBV fraction values approaching 1 are unlikely to represent voxels primarily filled with capillaries because this value implies the entire voxel is filled with blood. Also, due to the influence of noise, individual voxels cannot reflect accurate CBV fraction measures. Based on the noise distribution, individual voxels may have non-physical valuesnegative valued blood volume fractions or fractions greater than one. It is only in aggregate that meaningful physical values can be obtained.
[0173] 3.7 Quantitative steady-state functional CBV imaging Within the context of quantitative CBV, the response of this biomarker to changes in the functional state of the brain can be studied. The scans performed herein were about 16 minutes long (2 averages). Thus to study this question, a steady-state change to the brain function was needed for measurement. Therefore, the animals, as described above, were subjected to various challenges.
[0174] Utilizing the various post-contrast images and the pre-contrast anesthetized image it is possible to acquire the CBV from Equation 9. An additional complication arises from the fact that the CA concentration is slowly decaying. Although consistent I.sub.B values could be obtained when the rat was anesthetized (
[0175] As mentioned above, three states were measured with post-contrast QUTE-CE images per animal: a CO.sub.2-challenged state, an awake-baseline state, and an anesthetized state. To compare the functional steady-state changes induced by these states, the modes of the first peak in histograms of CBV were followed as noted above. This measure of comparison was chosen because it was the most physiologically relevant index in regard to following the behavior of lower-CBV voxels contained in the region. The two state changes are shown in select axial slices in
[0176] 3.8 Conclusions on Quantitative Brain Imaging
[0177] The technique was shown to produce quantitative assessment of CBV.
[0178] As used herein, consisting essentially of allows the inclusion of materials or steps that do not materially affect the basic and novel characteristics of the claim. Any recitation herein of the term comprising, particularly in a description of components of a composition or in a description of elements of a device, can be exchanged with consisting essentially of or consisting of.
[0179] It will be appreciated that the various features of the embodiments described herein can be combined in a variety of ways. For example, a feature described in conjunction with one embodiment may be included in another embodiment even if not explicitly described in conjunction with that embodiment.
[0180] The present invention has been described in conjunction with certain preferred embodiments. It is to be understood that the invention is not limited to the exact details of construction, operation, exact materials or embodiments shown and described, and that various modifications, substitutions of equivalents, alterations to the compositions, and other changes to the embodiments disclosed herein will be apparent to one of skill in the art.
APPENDIX
[0181]
TABLE-US-00003 TABLE 3 Resting state CBV Atlas (n = 12 Sprague Dawley Rats) Five different statistical measures are shown for characterizing each region Average Average of Average of Median with Region of Mean Median Average of CBV > 25% Cumulative Num Region Name CBV std CBV std Mode CBV std Vax removed std Gaussian FU 1 10th cerebellar lobule 8.10% 1.04% 7.64% 1.04% 4.93% 7.92% 7.42% 0.91% 7.32% 2 1st cerebellar lobule 5.88% 0.93% 5.71% 0.95% 5.64% 2.15% 5.70% 0.96% 5.44% 3 2nd cerebellar lobule 7.20% 1.93% 5.97% 1.43% 5.00% 1.68% 5.54% 1.25% 4.16% 4 3rd cerebellar lobule 7.05% 1.39% 5.39% 1.16% 4.60% 1.37% 4.91% 1.02% 3.98% 5 4th cerebellar lobule 6.35% 2.52% 4.14% 1.04% 3.46% 1.63% 3.66% 0.91% 3.52% 6 5th cerebellar lobule 8.58% 3.30% 4.32% 1.21% 2.27% 2.32% 3.07% 0.68% 2.56% 7 motor trigerminal nucleus 6.40% 1.07% 6.37% 1.06% 6.23% 2.32% 6.37% 1.06% 6.11% 8 root of trigerminal nerve 11.00% 1.47% 7.82% 0.67% 3.82% 1.33% 6.63% 0.63% 3.91% 9 4th cerebellar lobule 7.10% 1.59% 4.38% 1.14% 1.42% 6.30% 3.60% 1.15% 3.27% 10 7th cerebellar lobule 7.56% 1.63% 7.21% 1.81% 6.08% 5.91% 7.02% 1.70% 3.77% 11 facial nucleus 6.97% 1.15% 6.58% 1.01% 5.63% 2.78% 6.48% 0.99% 6.24% 12 8th cerebellar lobule 6.23% 1.18% 6.01% 1.54% 2.39% 7.86% 5.94% 1.46% 5.58% 13 9th cerebellar lobule 7.08% 1.09% 6.92% 1.15% 4.62% 6.97% 6.80% 1.10% 6.59% 14 anterior thalamic nuclei 6.15% 0.88% 5.40% 0.91% 4.89% 1.30% 5.21% 0.95% 4.91% 15 anterior emygdaloid nucleus 4.43% 1.22% 4.20% 1.23% 3.61% 1.34% 4.18% 1.33% 3.70% 16 accumbers core 2.41% 0.42% 2.15% 0.39% 1.77% 1.90% 2.35% 0.39% 2.23% 17 accumbers shell 2.66% 0.31% 2.57% 0.52% 2.36% 0.52% 1.57% 0.52% 2.39% 18 anterior hypothalamic area 6.52% 0.78% 6.33% 0.74% 6.03% 1.29% 6.33% 0.74% 5.82% 19 anterior lobe pituitary 24.47% 3.49% 11.13% 3.92% 19.33% 6.33% 14.21% 1.46% 18.94% 20 anterior difactory nucleus 5.33% 1.89% 3.43% 1.20% 1.84% 2.11% 3.09% 1.04% 2.77% 21 anterior pretectal nucleus 4.22% 0.93% 4.07% 0.91% 4.17% 0.97% 4.02% 0.92% 3.94% 22 arcunter nucleus 7.60% 1.71% 7.42% 1.58% 6.73% 3.03% 7.41% 1.56% 6.74% 23 auxidatory ctx 7.86% 0.87% 7.18% 0.71% 6.60% 0.63% 7.07% 0.69% 6.67% 24 basal amygdaloid nucleus 7.20% 0.84% 7.04% 0.77% 6.48% 1.50% 7.03% 0.77% 6.69% 25 CA1 dorsal 8.12% 1.18% 6.23% 0.82% 3.72% 0.79% 5.96% 0.79% 5.72% 26 CA1 bippocampus ventral 6.66% 0.53% 6.44% 0.49% 6.19% 0.62% 6.43% 0.49% 6.24% 27 CA2 3.75% 0.79% 5.59% 0.88% 3.37% 1.29% 5.59% 0.88% 3.49% 28 CA3 dorsal 6.82% 1.16% 6.25% 0.91% 3.74% 1.06% 6.17% 0.87% 5.68% 29 CA3 trippocarapus vantral 11.41% 1.68% 8.44% 1.17% 5.52% 1.68% 6.93% 0.86% 6.19% 30 central amygdaloid nucleus 9.72% 1.43% 7.70% 1.06% 6.42% 1.43% 7.36% 0.96% 6.60% 31 anterior circulator area 12.00% 1.72% 5.72% 0.44% 4.60% 0.68% 4.51% 0.39% 3.54% 32 central gray 7.72% 1.16% 7.44% 1.09% 7.09% 1.55% 7.36% 1.08% 7.28% 33 drustrom 3.17% 0.42% 2.98% 0.39% 2.67% 0.60% 7.92% 0.39% 2.71% 34 central medial thalamic nucleus 6.44% 0.49% 6.36% 0.38% 6.32% 1.63% 6.35% 0.60% 6.21% 35 cortical amygdaloid nucleus 7.93% 1.17% 7.38% 0.82% 6.86% 0.93% 7.26% 0.74% 6.79% 36 copuls of the pyramis 14.40% 2.17% 12.34% 1.89% 6.52% 8.36% 10.51% 1.30% 9.41% 37 crus 1 of antiform lobule 6.49% 1.27% 5.15% 0.81% 4.11% 0.85% 4.80% 0.20% 4.45% 38 crus 2 of antiform lobule 6.99% 1.76% 6.39% 1.16% 3.08% 7.42% 6.12% 1.01% 5.78% 39 diagonal band of Broca 6.34% 2.67% 4.29% 0.81% 3.55% 1.01% 3.96% 0.75% 3.18% 40 deotate cyrus dorsal 13.62% 2.89% 10.00% 1.73% 7.83% 1.32% 8.50% 1.14% 7.57% 41 dentate cyrus ventral 18.44% 2.10% 11.50% 1.36% 7.63% 1.21% 7.80% 1.00% 6.96% 42 dorsal lateral striatum 4.70% 0.64% 4.33% 0.58% 3.83% 0.72% 4.31% 0.57% 3.89% 43 dorsal medial hypothalamus 7.27% 0.73% 7.14% 0.65% 7.47% 1.47% 7.14% 0.65% 6.70% 44 dorsal medial striatum 4.30% 0.44% 4.05% 0.43% 3.66% 0.67% 4.05% 0.43% 3.63% 45 dorsal medial to general area 6.34% 0.89% 6.23% 0.94% 6.18% 1.28% 6.22% 0.94% 5.97% 46 DPGi 9.23% 0.83% 8.87% 0.88% 5.52% 1.57% 8.75% 0.83% 8.39% 47 dorsal raphe 10.28% 0.88% 9.32% 0.95% 8.62% 1.35% 9.05% 0.94% 8.77% 48 ruticutum dorsal 9.00% 1.55% 7.56% 1.12% 6.43% 0.91% 7.12% 0.92% 6.39% 49 exteeded amydala 5.21% 0.73% 5.11% 0.72% 4.62% 1.07% 5.11% 0.72% 4.71% 50 ectodical ctx 52.35% 8.96% 46.03% 0.52% 28.76% 3.08% 19.39% 1.80% 29.62% 51 eodopiriform nucleus 5.45% 0.64% 3.17% 0.60% 2.77% 0.38% 3.17% 0.60% 2.84% 52 ectodical ctx 23.07% 2.05% 16.00% 1.40% 10.73% 1.53% 10.96% 0.78% 10.60% 53 external plexiform layer 18.66% 3.19% 13.57% 1.57% 11.46% 1.68% 11.19% 0.90% 10.84% 54 flocculus cerebellum 7.77% 1.33% 6.90% 1.26% 6.58% 1.83% 6.59% 1.23% 6.16% 55 frontal association ctx 10.75% 4.88% 6.60% 4.90% 2.03% 5.05% 3.74% 2.88% 0.22% 56 gigentocellular reticular nucleus 6.09% 1.08% 5.70% 1.06% 5.32% 1.33% 5.63% 1.05% 5.31% 57 glomentlar layer 26.47% 3.60% 18.98% 2.82% 11.68% 7.78% 11.05% 1.69% 12.30% 58 globus paltidus 5.07% 0.57% 4.81% 0.55% 4.55% 1.06% 4.80% 0.55% 4.48% 59 granular cell layer 12.29% 2.28% 10.29% 1.49% 9.53% 1.56% 9.73% 1.26% 9.28% 60 habemia nucleus 22.96% 4.82% 14.61% 5.11% 8.11% 4.59% 8.59% 1.95% 8.43% 61 intercalated amygdaloid nucleus 10.89% 4.68% 9.68% 3.00% 8.97% 4.14% 8.42% 1.66% 9.33% 62 inferior colliculus 18.67% 2.78% 14.11% 1.65% 11.88% 1.86% 11.65% 1.38% 11.96% 63 infralimbic ctx 5.02% 0.85% 2.78% 0.67% 1.56% 0.95% 2.36% 0.68% 1.62% 64 insular ctx 6.25% 0.81% 4.38% 0.57% 3.42% 0.61% 4.10% 0.56% 3.48% 65 interposed nucleus 9.39% 1.46% 8.84% 1.44% 7.91% 1.75% 8.71% 1.38% 7.91% 66 inferior olivary complex 9.58% 2.65% 8.70% 2.13% 4.86% 8.40% 8.14% 1.94% 7.96% 67 interpeduncular nucleus 3.11% 1.95% 2.57% 1.24% 3.20% 1.68% 2.36% 1.05% 2.48% 68 lateral amygdaloid nucleus 6.64% 1.17% 6.50% 1.16% 6.32% 1.81% 6.48% 1.13% 6.15% 69 Lateral dentate 7.96% 1.08% 7.88% 0.96% 7.40% 1.97% 7.85% 0.91% 7.59% 70 locus ceruleus 7.80% 1.52% 7.43% 1.39% 6.77% 2.24% 7.36% 1.39% 7.39% 71 lateral dorsal thalamic nucleus 9.22% 2.01% 8.49% 1.72% 7.06% 2.04% 8.28% 1.62% 7.45% 72 lateral geniculate 15.86% 2.25% 12.91% 1.72% 10.43% 2.06% 11.21% 1.34% 11.05% 73 lateral hypothalamus 12.05% 1.79% 9.95% 1.03% 8.52% 1.04% 9.36% 0.81% 8.53% 74 lemniscal nucleus 8.11% 1.03% 6.37% 1.07% 5.73% 1.60% 5.86% 1.12% 5.74% 75 lateral orbital ctx 2.24% 0.35% 1.92% 0.41% 1.64% 0.63% 1.90% 0.42% 1.70% 76 lateral posterior thalamic nucleus 17.00% 2.70% 11.76% 2.30% 7.59% 1.74% 8.26% 1.43% 7.98% 77 lateral preoptic area 4.75% 0.56% 4.71% 0.55% 4.40% 1.18% 4.71% 0.55% 4.34% 78 lateral septal nucleus 7.29% 0.95% 5.38% 0.61% 3.85% 0.78% 4.97% 0.51% 3.93% 79 primary motor ctx 4.22% 0.77% 2.92% 0.44% 2.41% 0.65% 2.75% 0.44% 2.46% 80 secondary motor ctx 8.20% 1.80% 3.17% 0.73% 1.59% 0.74% 2.30% 0.55% 1.59% 81 magnocellular preoptic nucleus 7.71% 2.35% 6.59% 1.52% 5.20% 1.04% 6.40% 1.26% 5.30% 82 medial dorsal thalamic nucleus 10.00% 1.59% 7.98% 0.95% 7.16% 1.48% 7.48% 0.89% 7.19% 83 medial amygdaloid nucleus 17.87% 3.50% 13.65% 2.33% 9.87% 1.82% 10.74% 1.41% 9.78% 84 medial cerebellar nucleus fastigia 7.12% 1.03% 6.83% 0.98% 6.28% 2.18% 6.81% 0.96% 6.66% 85 medial geniculate 15.83% 2.09% 14.28% 1.77% 12.59% 2.02% 12.73% 1.33% 12.04% 86 medial mammillary nucleus 22.29% 3.83% 19.58% 2.88% 16.40% 2.67% 16.09% 1.57% 15.73% 87 median raphe nucleus 6.58% 0.91% 6.51% 0.90% 6.56% 2.04% 6.51% 0.89% 6.52% 88 medial orbital ctx 10.06% 3.86% 3.64% 2.33% 1.54% 1.24% 1.10% 1.15% 1.09% 89 medial preoptic area 4.31% 0.83% 4.14% 0.80% 3.81% 1.14% 4.12% 0.80% 3.74% 90 medial pretectal area 9.77% 6.50% 5.74% 5.00% 3.54% 3.79% 3.11% 2.00% 1.80% 91 medial septum 5.62% 1.04% 5.23% 0.92% 5.07% 1.21% 5.12% 0.86% 4.93% 92 neural lobe pituitary 23.86% 3.60% 21.85% 4.54% 16.68% 9.90% 13.74% 1.83% 18.45% 93 olivary nucleus 5.56% 1.04% 5.37% 1.06% 5.72% 1.76% 5.36% 1.06% 5.74% 94 paraventricular hypothalamus 6.12% 0.78% 6.03% 0.76% 6.34% 1.09% 6.03% 0.76% 6.22% 95 periaqueductal gray thalamus 8.56% 0.51% 8.10% 0.54% 7.51% 0.81% 8.00% 0.54% 7.63% 96 parabrachial nucleus 7.27% 1.17% 6.77% 1.02% 6.19% 1.52% 6.56% 0.97% 6.35% 97 PCRt 9.29% 1.58% 7.64% 1.16% 6.29% 1.43% 7.17% 1.04% 6.30% 98 parafascicular thalamic nucleus 6.97% 0.90% 6.41% 0.70% 6.14% 1.00% 6.31% 0.73% 6.40% 99 paraflocculus cerebellum 12.27% 1.41% 9.24% 0.94% 6.69% 1.37% 7.96% 0.77% 7.10% 100 posterior hypothalamic area 10.78% 1.13% 10.33% 1.04% 9.71% 1.45% 10.19% 0.98% 9.63% 101 pineal gland 71.44% 15.03% 74.85% 14.03% 60.30% 33.23% 14.67% 6.37% 80.49% 102 caudal piriform ctx 9.51% 1.24% 8.20% 0.97% 6.41% 1.45% 7.80% 0.82% 6.40% 103 rostral piriform ctx 2.76% 0.66% 1.92% 0.49% 1.48% 0.62% 1.81% 0.48% 1.47% 104 premammillary nucleus 12.02% 2.45% 11.25% 2.25% 10.73% 2.68% 10.85% 2.01% 10.74% 105 paramedian lobule 9.20% 1.18% 8.45% 0.96% 5.17% 7.53% 8.02% 0.86% 7.48% 106 pontine nuclei 2.15% 1.24% 1.27% 0.86% 1.06% 1.41% 1.06% 0.91% 1.18% 107 pontine reticular nucleus caudal 4.41% 0.81% 4.29% 0.82% 3.69% 1.19% 4.29% 0.82% 4.04% 108 pontine reticular nucleus oral 5.29% 0.92% 5.16% 0.92% 5.21% 1.21% 5.16% 0.92% 5.01% 109 posterior thalamic nucleus 7.05% 0.84% 6.74% 0.80% 6.29% 0.98% 6.69% 0.78% 6.30% 110 periolivary nucleus 5.40% 1.00% 5.24% 0.90% 5.24% 0.98% 5.21% 0.88% 5.10% 111 prerubral field 7.15% 0.87% 7.08% 0.92% 6.72% 1.25% 7.08% 0.92% 6.72% 112 principal sensory nucleus trigemin 8.11% 1.05% 7.43% 0.87% 6.38% 1.07% 7.20% 0.78% 6.55% 113 precuniform nucleus 6.82% 0.92% 6.79% 0.85% 6.69% 1.35% 6.78% 0.85% 6.71% 114 perirhinal ctx 22.69% 2.78% 13.73% 1.42% 8.35% 1.26% 9.23% 0.78% 8.45% 115 prelimbic ctx 4.75% 0.66% 3.28% 0.37% 2.36% 0.47% 3.07% 0.34% 2.37% 116 parietal ctx 6.21% 1.15% 4.89% 0.66% 4.24% 0.89% 4.60% 0.62% 4.41% 117 pedunculopontine tegmental area 6.79% 1.06% 6.40% 1.04% 5.69% 1.64% 6.28% 1.00% 5.92% 118 paraventricular nucleus 10.44% 4.39% 5.51% 1.56% 3.72% 2.19% 4.09% 0.83% 4.12% 119 retrochiasmatic nucleus 8.68% 1.93% 8.05% 1.78% 6.63% 2.85% 7.82% 1.60% 5.52% 120 reuniens nucleus 6.82% 0.44% 6.60% 0.40% 6.51% 1.27% 6.66% 0.40% 6.41% 121 raphe linear 7.23% 1.64% 6.64% 1.46% 6.13% 1.93% 6.52% 1.44% 6.04% 122 raphe magnus 4.32% 0.82% 4.19% 0.77% 3.39% 2.53% 4.19% 0.77% 3.94% 123 raphe obscurus nucleus 6.01% 1.66% 5.81% 1.82% 2.44% 9.25% 5.77% 1.80% 5.18% 124 red nucleus 5.79% 0.65% 5.72% 0.59% 5.76% 1.27% 5.72% 0.99% 5.55% 125 retrosplenial caudal ctx 40.90% 4.28% 30.13% 5.45% 14.20% 3.39% 13.16% 0.90% 13.81% 126 retrosplenial rostral ctx 24.60% 3.72% 12.61% 1.83% 6.78% 1.50% 8.35% 1.04% 6.87% 127 reticular nucleus 6.96% 0.84% 6.64% 0.76% 6.25% 1.02% 6.60% 0.75% 6.23% 128 reticular nucleus midbrain 7.95% 0.93% 6.87% 0.72% 6.21% 0.72% 6.61% 0.68% 6.20% 129 reticulotegmental nucleus 4.36% 0.80% 4.28% 0.87% 3.98% 1.77% 4.27% 0.87% 3.84% 130 primary somatosensory ctx barrel f 5.04% 0.58% 4.82% 0.54% 4.63% 0.67% 4.80% 0.53% 4.61% 131 primary somatosensory ctx forelimb 3.63% 0.52% 3.45% 0.45% 3.31% 0.58% 3.42% 0.45% 3.31% 132 primary somatosensory ctx hindlimb 4.11% 0.79% 3.50% 0.54% 3.19% 0.71% 3.41% 0.53% 3.31% 133 primary somatosensory ctx jaw 3.67% 0.45% 3.46% 0.46% 3.14% 0.68% 3.46% 0.45% 3.20% 134 primary somatosensory ctx shoulder 4.08% 0.53% 3.84% 0.42% 3.70% 0.74% 3.82% 0.42% 3.55% 135 primary somatosensory ctx trunk 4.73% 0.63% 4.14% 0.48% 3.79% 0.49% 4.07% 0.47% 3.84% 136 primary somatosensory ctx upper li 5.11% 0.65% 4.74% 0.61% 4.39% 0.78% 4.70% 0.62% 4.43% 137 secondary somaotsensory ctx 6.35% 0.90% 5.41% 0.61% 4.85% 0.74% 5.27% 0.61% 4.94% 138 suprachiasmatic nucleus 2.43% 1.41% 2.42% 1.42% 1.53% 2.93% 2.42% 1.42% 1.73% 139 substantia innominata 6.68% 1.48% 6.58% 1.57% 6.10% 2.21% 6.57% 1.56% 6.25% 140 simple lobule cerebellum 5.20% 1.74% 3.32% 0.71% 2.49% 0.91% 2.89% 0.65% 2.53% 141 substantia nigra compacta 7.69% 1.09% 7.15% 0.93% 7.35% 1.81% 6.92% 0.83% 6.92% 142 substantia nigra reticularis 13.05% 2.49% 11.04% 1.46% 9.29% 1.41% 9.62% 0.87% 9.16% 143 supraoptic nucleus 6.63% 2.22% 5.85% 1.08% 4.70% 1.12% 5.72% 1.01% 5.07% 144 solitary tract nucleus 7.31% 1.17% 6.93% 0.91% 6.16% 1.63% 6.78% 0.91% 6.42% 145 bed nucleus stria terminalis 4.46% 0.50% 4.35% 0.53% 4.19% 1.04% 4.35% 0.53% 4.23% 146 subthalamic nucleus 10.64% 2.36% 10.18% 1.84% 9.86% 1.89% 10.04% 1.59% 9.42% 147 superior colliculus 12.78% 2.17% 8.75% 0.83% 6.85% 1.26% 7.51% 0.86% 6.94% 148 sub coeruleus nucleus 5.86% 1.01% 5.73% 0.96% 5.48% 1.04% 5.73% 0.96% 5.56% 149 supramammillary nucleus 21.86% 5.15% 18.67% 4.46% 15.19% 2.98% 14.92% 1.69% 13.81% 150 temporal ctx 28.77% 5.70% 23.64% 3.93% 17.29% 3.26% 15.53% 1.12% 16.42% 151 triangular septal nucleus 5.78% 0.94% 5.65% 0.86% 5.01% 2.08% 5.61% 0.79% 5.62% 152 tenia tecta ctx 11.67% 3.89% 5.23% 2.27% 0.01% 1.30% 2.42% 0.88% 0.17% 153 olfactory tubercles 6.07% 1.68% 4.74% 1.18% 3.85% 1.11% 4.41% 0.96% 3.60% 154 trapezoid body 4.57% 0.70% 4.41% 0.69% 4.43% 1.18% 4.40% 0.69% 4.50% 155 Ventricle 18.34% 1.95% 8.60% 1.00% 3.79% 1.53% 5.80% 0.66% 4.08% 156 visual 1 ctx 30.58% 3.04% 13.69% 1.50% 7.98% 0.94% 8.78% 0.78% 7.82% 157 visual 2 ctx 19.60% 3.26% 11.64% 1.09% 7.83% 0.77% 9.10% 0.63% 8.06% 158 ventral anterior thalamic nucleus 5.72% 0.65% 5.57% 0.69% 5.05% 0.99% 5.56% 0.69% 5.35% 159 cochlear nucleus 11.80% 1.09% 10.92% 0.74% 9.73% 1.74% 10.32% 0.46% 9.80% 160 vestibular nucleus 12.05% 1.16% 10.58% 1.05% 8.89% 2.00% 9.81% 0.89% 8.98% 161 ventrolateral thalamic nucleus 6.19% 0.71% 5.98% 0.76% 5.53% 1.08% 5.97% 0.75% 5.89% 162 ventral lateral striatum 4.53% 0.75% 4.07% 0.63% 3.61% 0.73% 4.03% 0.61% 3.63% 163 ventromedial thalamic nucleus 6.91% 0.68% 6.81% 0.69% 6.76% 0.96% 6.81% 0.69% 6.76% 164 ventral medial hypothalamus 8.39% 1.20% 7.83% 0.98% 7.15% 1.43% 7.74% 0.94% 6.92% 165 ventral medial striatum 3.12% 0.38% 3.04% 0.36% 2.82% 0.61% 3.04% 0.36% 2.81% 166 ventral orbital ctx 2.29% 0.97% 1.42% 0.56% 0.80% 0.77% 1.32% 0.53% 0.84% 167 ventral pallidum 3.85% 0.72% 3.75% 0.65% 3.41% 0.76% 3.75% 0.69% 3.49% 168 ventral posterolateral thalamic n 7.63% 0.72% 7.35% 0.66% 7.13% 0.69% 7.33% 0.64% 7.08% 169 ventral posteriolmedial thalamic n 7.18% 0.70% 6.96% 0.70% 6.76% 1.16% 6.95% 0.69% 6.53% 170 ventral subiculum 14.50% 2.03% 10.26% 1.04% 7.51% 1.21% 8.63% 0.69% 8.15% 171 ventral tegmental area 6.83% 1.66% 5.21% 0.87% 4.17% 1.85% 4.85% 0.92% 4.53% 172 White Matter 6.80% 0.76% 5.43% 0.54% 4.51% 0.64% 5.19% 0.51% 4.54% 173 White Matter 7.73% 1.11% 6.56% 0.83% 5.66% 0.80% 6.30% 0.78% 5.57% 174 zona incerta 8.53% 0.94% 8.13% 0.85% 7.67% 1.17% 8.09% 0.83% 7.72%
TABLE-US-00004 TABLE 4 Condensed version of CBV atlas with only 59 regions Average Average Region of Mean Average of of Mode Cumulative Num Region Name CBV std Median CBV std CBV std Gaussian Fit 1 10th cerebellar lobule, 6th cerebellar lobule, 7.06% 1.13% 5.58% 1.30% 2.76% 6.85% 5.03% 7th cerebellar lobule, 8th cerebellar lobule, 9th cerebellar lobule 2 1st cerebellar lobule, 2nd cerebellar lobule, 7.44% 2.17% 4.89% 1.13% 3.80% 1.12% 3.69% 3rd cerebellar lobule, 4th cerebellar lobule, 5th cerebellar lobule 3 motor trigeminal nucleus, root of trigeminal nerve, 9.05% 1.08% 7.18% 0.76% 5.98% 0.94% 6.02% principal sensory nucleus trigemin, trapezoid body 4 anterior thalamic nuclei, anterior pretectal nucleus, 8.71% 1.00% 6.44% 0.61% 5.72% 0.95% 5.78% central medial thalamic nucleus, habenula nucleus, lateral dorsal thalamic nucleus, medial dorsal thalamic nucleus, medial pretectal area, parafascicular thalamic nucleus, paraventricular nucleus, reuniens nucleus 5 anterior amygdaloid nucleus, basal amygdaloid nucleus, 8.64% 1.09% 7.30% 0.82% 6.41% 1.02% 6.41% central amygdaloid nucleus, cortical amygdaloid nucleus, extended amydala, intercalated amygdaloid nucleus, lateral amygdaloid nucleus, medial amygdaloid nucleus 6 accumbens core, accumbens shell, diagonal band of Broca, 3.42% 0.54% 3.16% 0.45% 2.85% 0.58% 2.86% substantia innominata, bed nucleus stria terminalis, ventral medial striatum, ventral pallidum 7 anterior hypothalamic area, dorsal medial hypothalamus, 10.18% 1.36% 8.45% 0.78% 7.02% 0.88% 7.07% lateral hypothalamus, lateral preoptic area, magnocellular preoptic nucleus, medial mammillary nucleus, medial preoptic area, paraventricular hypothalamus, posterior hypothalamic area, premammillary nucleus, supraoptic nucleus, supramammillary nucleus, ventral medial hypothalamus 8 auditory ctx, parietal ctx 7.41% 0.78% 6.55% 0.65% 6.04% 0.58% 6.07% 9 anterior lobe pituitary, arcuate nucleus, 21.48% 2.73% 18.89% 3.37% 10.53% 3.16% 11.41% neural lobe pituitary, retrochiasmatic nucleus, suprachiasmatic nucleus 10 CA1 dorsal, CA1 hippocampus ventral 7.58% 0.88% 6.33% 0.67% 5.88% 0.72% 5.91% 11 anterior cingulate area 12.00% 1.64% 5.72% 0.44% 3.60% 0.68% 3.54% 12 claustrum, claustrum, dorsal lateral striatum 4.36% 0.55% 4.03% 0.52% 3.57% 0.68% 3.62% 13 dentate gyrus dorsal, dentate gyrus ventral 15.34% 2.31% 10.37% 1.53% 7.68% 1.55% 7.39% 14 dorsal medial striatum, dorsal medial striatum 4.30% 0.42% 4.05% 0.43% 3.66% 0.67% 3.63% 15 copula of the pyramis, copula of the pyramis, 7.37% 1.32% 5.95% 1.01% 2.67% 6.89% 4.98% crus 1 of ansiform lobule, crus 2 of ansiform lobule 16 anterior olfactory nucleus, endopiriform nucleus 4.48% 1.22% 3.29% 0.83% 2.60% 0.80% 2.58% 17 lateral septal nucleus, medial septum, triangular septal nucleus 7.07% 0.86% 5.39% 0.62% 3.61% 1.49% 4.13% 18 CA2, CA3 dorsal, CA3 hippocampus ventral 8.02% 0.90% 6.56% 0.80% 5.70% 0.95% 5.74% 19 dorsal raphe, interpeduncular nucleus, 8.45% 1.16% 7.28% 0.77% 6.64% 0.98% 6.65% median raphe nucleus, raphe linear, raphe magnus, raphe obscurus nucleus, substantia nigra compacta, substantia nigra reticularis, subthalamic nucleus, ventral tegmental area 20 primary motor ctx 4.22% 0.73% 2.92% 0.44% 2.41% 0.65% 2.46% 21 secondary motor ctx 8.20% 1.72% 3.17% 0.73% 1.59% 0.74% 1.59% 22 frontal association ctx, lateral orbital ctx, 5.35% 1.62% 2.44% 0.59% 1.04% 0.54% 1.17% medial orbital ctx, ventral orbital ctx 23 central gray, periaqueductal gray thalamus 8.40% 0.54% 7.97% 0.56% 7.53% 0.92% 7.50% 24 interposed nucleus, Lateral dentate, 11.10% 0.83% 9.96% 0.77% 9.07% 1.87% 8.65% medial cerebellar nucleus fastigia, cochlear nucleus, vestibular nucleus 25 dorsomedial tegmental area, DPGi, pontine nuclei, 4.51% 0.77% 4.27% 0.77% 4.18% 0.97% 4.13% pontine reticular nucleus caudal, pontine reticular nucleus oral, precuniform nucleus, reticubtegmental nucleus 26 facial nucleus, inferior olivary complex, olivary nucleus, 6.51% 0.92% 6.09% 0.91% 5.61% 1.16% 5.65% periolivary nucleus, pedunculopontine tegmental area, sub coeruleus nucleus 27 caudal piriform ctx 9.51% 1.18% 8.20% 0.97% 6.41% 1.45% 6.40% 28 rostral piriform ctx 2.76% 0.63% 1.92% 0.49% 1.48% 0.62% 1.47% 29 paraflocculus cerebellum 12.27% 1.35% 9.24% 0.94% 6.69% 1.38% 7.10% 30 prorubral field, red nucleus, reticular nucleus midbrain 7.77% 0.85% 6.81% 0.72% 6.15% 0.85% 6.20% 31 infralimbic ctx, prelimbic ctx 4.85% 0.67% 3.10% 0.45% 2.08% 0.41% 2.14% 32 lateral posterier thalamic nucleus. Posterior thalimic nucleus, 8.31% 0.76% 7.13% 0.67% 6.44% 0.92% 6.56% reticular nucleus, ventral anterior thalamic nucleus, ventrolateral thalamic nucleus, ventromedial thalamic nucleus, ventral posteriolateral thalamic n, ventral posteriolmedial thalamic n, zone incerta 33 globus pallidus, lateral geniculate, 12.99% 1.39% 10.91% 1.19% 8.64% 1.23% 8.83% lemniscal nucleus, medial geniculate 34 ventral lateral striatum 5.22% 0.65% 4.55% 0.56% 3.95% 0.78% 3.91% 35 White Matter 6.91% 0.72% 5.56% 0.55% 4.62% 0.61% 4.62% 36 subiculum dorsal, ventral subiculum 12.28% 1.68% 9.04% 1.07% 7.19% 0.99% 7.12% 37 entorhinal ctx 23.07% 1.95% 16.00% 1.40% 10.73% 1.54% 10.60% 38 external plexiform layer 18.66% 3.04% 13.57% 1.57% 11.46% 1.68% 10.84% 39 gigantocellular reticular nucleus 6.09% 1.03% 5.70% 1.06% 5.32% 1.33% 5.31% 40 glomerular layer 32.26% 4.04% 24.45% 4.50% 11.85% 7.83% 13.12% 41 granular cell layer 12.29% 2.18% 10.29% 1.49% 9.53% 1.56% 9.28% 42 inferior colliculus 18.67% 2.65% 14.11% 1.65% 11.88% 1.85% 11.96% 43 insular ctx 6.25% 0.77% 4.38% 0.57% 3.42% 0.61% 3.48% 44 PCRs 9.29% 1.51% 7.64% 1.16% 6.29% 1.44% 6.30% 45 retrosplenial rostral ctx 36.61% 3.66% 24.80% 4.30% 10.26% 1.58% 9.97% 46 primary somatosensory ctx barreif 5.04% 0.55% 4.82% 0.54% 4.63% 0.67% 4.61% 47 primary somatosensory ctx forelimb, 3.94% 0.51% 3.57% 0.43% 3.35% 0.60% 3.41% primary somatosensory ctx hindlimb, primary somatosensory ctx shoulder, primary somatosensory ctx trunk 48 primary somatosensory ctx jaw 3.67% 0.43% 3.46% 0.46% 3.14% 0.68% 3.20% 49 primary somatosensory ctx upper li 5.11% 0.62% 4.74% 0.61% 4.39% 0.78% 4.43% 50 secondary somaotsensory ctx 6.35% 0.86% 5.41% 0.61% 4.85% 0.74% 4.94% 51 simple lobule cerebellum 5.20% 1.66% 3.32% 0.71% 2.06% 1.58% 2.53% 52 pincal gland, superior colliculus 14.37% 2.23% 9.02% 0.84% 6.86% 1.23% 6.90% 53 tenia tecta ctx, olfactory tubercles 7.66% 1.92% 4.78% 1.30% 3.18% 1.12% 3.09% 54 visual 1 ctx 30.58% 2.90% 13.69% 1.50% 7.98% 0.94% 7.82% 55 visusl 2 ctx 19.60% 3.11% 11.64% 1.09% 7.83% 0.77% 8.06% 56 locus ceruleus, parabrachial nucleus, solitary tract nucleus 7.38% 1.01% 6.90% 0.94% 6.35% 1.39% 6.27% 57 ectorhinal ctx, perirhinal ctx, tenaporal ctx 24.90% 3.38% 17.64% 2.39% 9.81% 1.57% 9.83% 58 flocculus cerebellum, paramedian lobule 9.20% 1.13% 8.45% 0.96% 5.17% 7.53% 7.48% 59 Ventricle 18.34% 1.86% 8.60% 1.00% 3.79% 1.54% 4.08%
TABLE-US-00005 TABLE 5 Steady state functional changes in absolute CBV The CBV and change in CBV compared to baseline is shown for clustered regions Region Mean Mean Number Clustered Region Names CO2-Challenge std Awake Baseline 1 10th cerebellar lobule, 6th cerebellar lobule, 2.37% 7.15% 2.45% 7th cerebellar lobule, 8th cerebellar lobule, 9th cerebellar lobule 2 1st cerebellar lobule, 2nd cerebellar lobule, 4.23% 0.94% 3.83% 3rd cerebellar lobule, 4th cerebellar lobule, 5th cerebellar lobule 3 motor trigeminal nucleus, root of trigeminal nerve, 6.02% 2.02% 5.94% principal sensory nucleus trigemin, trapezoid body 4 anterior thalamic nuclei, anterior pretectal nucleus, 6.46% 0.87% 5.51% central medial thalamic nucleus, habenula nucleus, lateral dorsal thalamic nucleus, medial dorsal thalamic nucleus, medial pretectal area, parafascicular thalamic nucleus, paraventricular nucleus, reuniens nucleus 5 anterior amygdaloid nucleus, basal amygdaloid nucleus, 7.58% 1.36% 6.61% central amygdaloid nucleus, cortical amygdaloid nucleus, extended amydala, intercalated amygdaloid nucleus, lateral amygdaloid nucleus, medial amygdaloid nucleus 6 accumbens core, accumbens shell, diagonal band of Broca, 3.46% 0.74% 2.84% substantia innominata, bed nucleus stria terminalis, ventral medial striatum, ventral pallidum 7 anterior hypothalamic area, dorsal medial hypothalamus, 7.88% 1.17% 7.24% lateral hypothalamus, lateral preoptic area, magnocellular preoptic nucleus, medial mammillary nucleus, medial preoptic area, paraventricular hypothalamus, posterior hypothalamic area, premammillary nucleus, supraoptic nucleus, supramammillary nucleus, ventral medial hypothalamus 8 auditory ctx, parietal ctx 7.35% 0.84% 6.42% 9 anterior lobe pituitary, arcuate nucleus, 18.69% 5.88% 14.49% neural lobe pituitary, retrochiasmatic nucleus, suprachiasmatic nucleus 10 CA1 dorsal, CA1 hippocampus ventral 6.45% 0.74% 6.11% 11 anterior cingulate area 4.46% 0.69% 1.67% 12 claustrum, claustrum, dorsal lateral striatum 4.28% 0.79% 3.65% 13 dentate gyrus dorsal, dentate gyrus ventral 8.02% 0.87% 7.75% 14 dorsal medial striatum, dorsal medial striatum 4.34% 0.80% 3.66% 15 copula of the pyramis, copula of the pyramis, 3.39% 7.02% 3.24% crus 1 of ansiform lobule, crus 2 of ansiform lobule 16 anterior olfactory nucleus, endopiriform nucleus 3.42% 0.84% 2.46% 17 lateral septal nucleus, medial septum, triangular septal nucleus 4.98% 0.88% 4.35% 18 CA2, CA3 dorsal, CA3 hippocampus ventral 6.07% 0.84% 5.72% 19 dorsal raphe, interpeduncular nucleus, 6.71% 0.97% 6.20% median raphe nucleus, raphe linear, raphe magnus, raphe obscurus nucleus, substantia nigra compacta, substantia nigra reticularis, subthalamic nucleus, ventral tegmental area 20 primary motor ctx 3.57% 0.96% 2.61% 21 secondary motor ctx 2.30% 2.05% 1.86% 22 frontal association ctx, lateral orbital ctx, 1.98% 0.91% 0.88% medial orbital ctx, ventral orbital ctx 23 central gray, periaqueductal gray thalamus 7.83% 0.92% 7.32% 24 interposed nucleus, Lateral dentate, 8.85% 1.41% 8.71% medial cerebellar nucleus fastigia, cochlear nucleus, vestibular nucleus 25 dorsomedial tegmental area, DPGi, pontine nuclei, 4.14% 0.95% 3.74% pontine reticular nucleus caudal, pontine reticular nucleus oral precuniform nucleus, reticulotegmental nucleus 26 facial nucleus, inferior olivary complex, olivary nucleus, 5.94% 1.83% 5.02% periolivary nucleus, pedunculopontine tegmental area, sub coeruleus nucleus 27 caudal piriform ctx 7.32% 1.40% 6.53% 28 rostral piriform ctx 2.01% 0.92% 1.20% 29 paraflocculus cerebellum 8.51% 1.22% 7.91% 30 prerubral field, red nucleus, reticular nucleus midbrain 6.65% 0.74% 6.10% 31 infralimbic ctx, prelimbic ctx 3.00% 0.70% 1.97% 32 lateral posterior thalamic nucleus, posterior thalamic nucleus, 7.20% 0.97% 6.58% reticular nucleus, ventral anterior thalamic nucleus, ventrolateral thalamic nucleus, ventromedial thalamic nucleus, ventral posteriolateral thalamic n, ventral posteriolmedial thalamic n, zona incerta 33 globus pallidus, lateral geniculate, 9.52% 1.85% 9.28% lemniscal nucleus, medial geniculate 34 ventral lateral striatum 4.66% 0.85% 3.97% 35 White Matter 5.30% 0.75% 4.65% 36 subiculum dorsal, ventral subiculum 8.12% 1.05% 7.67% 37 entorhinal ctx 10.85% 1.69% 10.95% 38 external plexiform layer 12.33% 1.68% 12.23% 39 gigantocellular reticular nucleus 5.65% 1.37% 4.83% 40 glomerular layer 16.91% 5.43% 15.00% 41 granular cell layer 11.05% 1.90% 10.78% 42 inferior colliculus 12.15% 1.53% 11.53% 43 insular ctx 4.42% 0.78% 3.38% 44 PCRt 6.81% 1.39% 6.21% 45 retrosplenial rostral ctx 11.97% 1.58% 11.33% 46 primary somatosensory ctx barrel f 6.01% 0.85% 4.32% 47 primary somatosensory ctx forelimb, 4.56% 0.75% 3.59% primary somatosensory ctx hindlimb, primary somatosensory ctx shoulder, primary somatosensory ctx trunk 48 primary somatosensory ctx jaw 4.35% 0.79% 3.07% 49 primary somatosensory ctx upper li 5.82% 0.92% 4.66% 50 secondary somaotsensory ctx 6.34% 0.99% 5.10% 51 simple lobule cerebellum 3.18% 1.37% 2.35% 52 pineal gland, superior colliculus 6.79% 1.07% 6.68% 53 tenia tecta ctx, olfactory tubercles 3.57% 1.06% 2.57% 54 visual 1 ctx 9.12% 1.22% 8.39% 55 visual 2 ctx 9.99% 0.85% 9.05% 56 locus ceruleus, parabrachial nucleus, solitary tract nucleus 7.12% 1.31% 6.23% 57 ectorhinal ctx, perirhinal ctx temporal ctx 10.77% 1.82% 10.10% 58 flocculus cerebellum, paramedian lobule 6.28% 8.18% 4.93% 59 Ventricle 4.42% 1.48% 3.63% Whole Brain 5.51% 0.76% 4.76% Region Mean CO2-Challenge Anesthetized Number std Anesthetized std N Baseline Baseline 1 6.88% 2.76% 6.85% 12 0.08% 0.31% 2 1.01% 3.80% 1.12% 12 0.40% 0.04% 3 0.79% 5.98% 0.94% 12 0.08% 0.04% 4 1.17% 5.72% 0.94% 12 0.95% 0.22% 5 0.97% 6.41% 1.02% 12 0.97% 0.20% 6 0.77% 2.85% 0.58% 12 0.62% 0.01% 7 0.98% 7.02% 0.88% 12 0.64% 0.23 8 0.80% 6.04% 0.58% 12 0.93% 0.38% 9 8.57% 10.53% 3.16% 12 4.20% 3.96% 10 0.94% 5.88% 0.72% 12 0.34% 0.23% 11 0.79% 3.60% 0.68% 12 0.79% 0.07% 12 0.71% 3.57% 0.68% 12 0.63% 0.08% 13 1.11% 7.68% 1.55% 12 0.27% 0.07 14 0.85% 3.66% 0.66% 12 0.68% 0.00% 15 7.13% 2.67% 6.89% 12 0.14% 0.57% 16 0.79% 2.60% 0.80% 12 0.96% 0.15% 17 0.93% 4.06% 0.67% 12 0.64% 0.29% 18 1.16% 5.70% 0.95% 12 0.36% 0.02% 19 1.04% 6.64% 0.98% 12 0.51% 0.44% 20 0.83% 2.41% 0.65% 12 0.95% 0.20% 21 0.92% 1.59% 0.74% 12 0.44% 0.27% 22 0.84% 1.04% 0.54% 12 1.10% 0.16% 23 1.10% 7.53% 0.92% 12 0.51% 0.21% 24 1.45% 9.07% 1.87% 12 0.14% 0.36% 25 0.78% 4.18% 0.97% 12 0.40% 0.43% 26 1.77% 5.61% 1.15% 12 0.92% 0.59% 27 1.24% 6.41% 1.45% 12 0.79% 0.12% 28 0.75% 1.48% 0.62% 12 0.81% 0.28% 29 0.84% 6.69% 1.38% 12 0.60% 1.22% 30 0.87% 6.15% 0.85% 12 0.55% 0.05% 31 0.79% 2.08% 0.41% 12 1.03% 0.11% 32 1.04% 6.44% 0.92% 12 0.63% 0.13% 33 1.70% 8.63% 1.23% 12 0.24% 0.65% 34 0.69% 3.95% 0.78% 12 0.69% 0.02% 35 0.79% 4.62% 0.61% 12 0.65% 0.03% 36 0.82% 7.19% 0.99% 12 0.45% 0.48% 37 2.37% 10.73% 1.54% 12 0.10% 0.23% 38 1.27% 11.46% 1.69% 12 0.11% 0.76% 39 1.25% 5.32% 1.33% 12 0.82% 0.49% 40 3.34% 11.85% 7.84% 12 1.91% 3.16% 41 1.60% 9.53% 1.56% 12 0.27% 1.24% 42 1.75% 11.88% 1.86% 12 0.62% 0.35% 43 0.71% 3.42% 0.61% 12 1.04% 0.05% 44 1.35% 6.29% 1.44% 12 0.60% 0.08% 45 1.96% 10.25% 1.58% 12 0.64% 1.07% 46 0.82% 4.63% 0.67% 12 1.19% 0.19% 47 0.74% 3.35% 0.60% 12 0.97% 0.24% 48 0.84% 3.14% 0.68% 12 1.28% 0.07% 49 0.70% 4.39% 0.78% 12 1.15% 0.27% 50 0.88% 4.85% 0.74% 12 1.24% 0.25% 51 1.29% 2.49% 0.91% 12 0.83% 0.14% 52 1.27% 6.86% 1.23% 12 0.11% 0.18% 53 1.07% 3.18% 1.12% 12 1.00% 0.61% 54 1.06% 7.98% 0.94% 12 0.72% 0.41% 55 0.61% 7.83% 0.77% 12 0.94% 1.23 56 1.04% 6.35% 1.39% 12 0.89% 0.13% 57 1.96% 9.81% 1.57% 12 0.68% 0.28% 58 7.60% 5.17% 7.53% 12 1.35 0.24% 59 2.79% 3.79% 1.53% 12 0.79% 0.16% 0.75% 4.69% 0.64% 12 0.75% 0.07%
REFERENCES
[0182] [1] M. Rudin and R. Weissleder, Molecular imaging in drug discovery and development, Nat Rev Drug Discov, vol. 2, no. 2, pp. 123-131, 2003. [0183] [2] W. A. Weber, J. Czernin, M. E. Phelps, and H. R. Herschman, Technology Insight: novel imaging of molecular targets is an emerging area crucial to the development of targeted drugs, Nat Clin Pr. Oncol, vol. 5, no. 1, pp. 44-54, 2008. [0184] [3] J. K. Willmann, N. van Bruggen, L. M. Dinkelborg, and S. S. Gambhir, Molecular imaging in drug development, Nat Rev Drug Discov, vol. 7, no. 7, pp. 591-607, 2008. [0185] [4] M. E. MacDonald and R. Frayne, Cerebrovascular MRI: a review of state-of-the-art approaches, methods and techniques, NMR Biomed., vol. 28, no. 7, pp. 767-791, 2015. [0186] [5] T. Grobner and T. Nephrol Dial, Gadoliniuma specific trigger for the development of nephrogenic fibrosing dermopathy and nephrogenic systemic fibrosis?, Nephrol. Dial. Transplant., vol. 21, no. 4, pp. 1104-1108. [0187] [6] P. Marckmann, K. Skov L Fau-Rossen, A. Rossen K Fau-Dupont, M. B. Dupont A Fau-Damholt, J. G. Damholt Mb Fau-Heaf, H. S. Heaf Jg Fau-Thomsen, H. S. [0188] Thomsen, and J. A. S. Nephrol, Nephrogenic systemic fibrosis: suspected causative role of gadodiamide used for contrast-enhanced magnetic resonance imaging, Jouranl Am. Soc. Nephrol. JASN, vol. 17, no. 9, pp. 2359-2362. [0189] [7] J. Bremerich, D. Bilecen, and P. Reimer, MR angiography with blood pool contrast agents, European Radiology, vol. 17, no. 12. pp. 3017-3024, 2007. [0190] [8] E. a Neuwelt, B. E. Hamilton, C. G. Varallyay, W. R. Rooney, R. D. Edelman, P. M. Jacobs, and S. G. Watnick, Ultrasmall superparamagnetic iron oxides (USPIOs): a future alternative magnetic resonance (MR) contrast agent for patients at risk for nephrogenic systemic fibrosis (NSF)?, Kidney Int, vol. 75, no. 5, pp. 465-474, 2009. [0191] [9] B. Turkbey, H. K. Agarwal, J. Shih, M. Bernardo, Y. L. McKinney, D. Daar, G. L. Griffiths, S. Sankineni, L. Johnson, K. B. Grant, J. Weaver, S. Rais-Bahrami, M. Harisinghani, P. Jacobs, W. Dahut, M. J. Merino, P. A. Pinto, and P. L. Choyke, A Phase I Dosing Study of Ferumoxytol for MR Lymphography at 3 T in Patients With Prostate Cancer, AJR Am J Roentgenol, vol. 205, no. 1, pp. 64-69, 2015. [0192] [10] J. S. Weinstein, C. G. Varallyay, E. Dosa, S. Gahramanov, B. Hamilton, W. D. Rooney, L. L. Muldoon, and E. A. Neuwelt, Superparamagnetic iron oxide nanoparticles: diagnostic magnetic resonance imaging and potential therapeutic applications in neurooncology and central nervous system inflammatory pathologies, a review, J. Cereb. Blood Flow Metab., vol. 30, no. 1, pp. 15-35, 2010. [0193] [11] E. A. Neuwelt, P. Vrallyay, A. G. Bag, L. L. Muldoon, G. Nesbit, and R. Nixon, Imaging of iron oxide nanoparticles by MR and light microscopy in patients with malignant brain tumours, Neuropathol. Appl. Neurobiol., vol. 30, no. 5, pp. 456-471, 2004. [0194] [12] E. Dosa, D. J. Guillaume, M. Haluska, C. A. Lacy, B. E. Hamilton, J. M. Njus, W. D. Rooney, D. F. Kraemer, L. L. Muldoon, and E. A. Neuwelt, Magnetic resonance imaging of intracranial tumors: Intra-patient comparison of gadoteridol and ferumoxytol, Neuro. Oncol., vol. 13, no. 2, pp. 251-260, 2011. [0195] [13] B. E. Hamilton, G. M. Nesbit, E. Dosa, S. Gahramanov, B. Rooney, E. G. Nesbit, J. Raines, and E. A. Neuwelt, Comparative analysis of ferumoxytol and gadoteridol enhancement using T.sub.1- and T.sub.2-weighted MRI in neuroimaging, Am. J. Roentgenol., vol. 197, no. 4, pp. 981-988, 2011. [0196] [14] D. Sosnovik, M. Nahrendorf, and R. Weissleder, Magnetic nanoparticles for MR imaging: agents, techniques and cardiovascular applications, Basic Res. Cardiol., vol. 103, no. 2, pp. 122-130, 2008. [0197] [15] M. V Yigit, A. Moore, and Z. Medarova, Magnetic nanoparticles for cancer diagnosis and therapy, Pharm Res, vol. 29, no. 5, pp. 1180-1188, 2012. [0198] [16] J. E. Rosen, L. Chan, D.-B. Shieh, and F. X. Gu, Iron oxide nanoparticles for targeted cancer imaging and diagnostics, Nanomedicine Nanotechnology, Biol. Med., vol. 8, no. 3, pp. 275-290, 2012. [0199] [17] J. W. Bulte and D. L. Kraitchman, Iron oxide MR contrast agents for molecular and cellular imaging, NMR Biomed, vol. 17, no. 7, pp. 484-499, 2004. [0200] [18] L. Wang, C. A. Corum, D. Idiyatullin, M. Garwood, and Q. Zhao, T(1) estimation for aqueous iron oxide nanoparticle suspensions using a variable flip angle SWIFT sequence, Magn Reson Med, vol. 70, no. 2, pp. 341-347, 2013. [0201] [19] L. Wang, X. Zhong, W. Qian, J. Huang, Z. Cao, Q. Yu, M. Lipowska, R. Lin, A. Wang, L. Yang, and H. Mao, Ultrashort Echo Time (UTE) imaging of receptor targeted magnetic iron oxide nanoparticles in mouse tumor models, J. Magn. Reson. Imaging, p. DOI: 10.1002/jmri.24453, 2013. [0202] [20] M. J. Lipinski, K. C. Briley-Saebo, V. Mani, and Z. A. Fayad, Positive Contrast IRON MRI: A Change for the Better?, J. Am. Coll. Cardiol., vol. 52, no. 6, pp. 492-494, August 2008. [0203] [21] C. H. Cunningham, T. Arai, P. C. Yang, M. V. McConnell, J. M. Pauly, and S. M. Conolly, Positive contrast magnetic resonance imaging of cells labeled with magnetic nanoparticles,Magn. Reson. Med., vol. 53, no. 5, pp. 999-1005, 2005. [0204] [22] M. Stuber, W. D. Gilson, M. Schr, D. A. Kedziorek, L. V. Hofmann, S. Shah, E. J. Vonken, J. W. M. Bulte, and D. L. Kraitchman, Positive contrast visualization of iron oxide-labeled stem cells using inversion-recovery with ON-resonant water suppression (IRON), Magn. Reson. Med., vol. 58, no. 5, pp. 1072-1077, 2007. [0205] [23] J. H. Seppenwoolde, M. A. Viergever, and C. J. G. Bakker, Passive tracking exploiting local signal conservation: The white marker phenomenon,Magn. Reson. Med., vol. 50, no. 4, pp. 784-790, 2003. [0206] [24] V. Mani, K. C. Briley-Saebo, V. V. Itskovich, D. D. Samber, and Z. A. Fayad, GRadient echo Acquisition for Superparamagnetic particles with Positive contrast (GRASP): Sequence characterization in membrane and glass superparamagnetic iron oxide phantoms at 1.5 T and 3 T,Magn. Reson. Med., vol. 55, no. 1, pp. 126-135, 2006. [0207] [25] J. E. Holmes and G. M. Bydder, MR imaging with ultrashort TE (UTE) pulse sequences: Basic principles, Radiography, vol. 11, no. 3, pp. 163-174, 2005. [0208] [26] Y. Zhang, E. M. Hetherington Hp Fau-Stokely, G. F. Stokely Em Fau-Mason, D. B. Mason Gf Fau-Twieg, D. B. Twieg, and M. Magn Reson, A novel k-space trajectory measurement technique, Magn. Reson. Med., vol. 39, no. 6, pp. 999-1004, 1998. [0209] [27] K. C. Barick, M. Aslam, Y.-P. Lin, D. Bahadur, P. V. Prasad, and V. P. Dravid, Novel and efficient MR active aqueous colloidal Fe3O4 nanoassemblies, J Mater. Chem., vol. 19, no. 38, p. 7023, 2009. [0210] [28] M. S. Judenhofer, H. F. Wehrl, D. F. Newport, C. Catana, S. B. Siegel, M. Becker, A. Thielscher, M. Kneilling, M. P. Lichy, M. Eichner, K. Klingel, G. Reischl, S. Widmaier, M. Rocken, R. E. Nutt, H. J. Machulla, K. Uludag, S. R. Cherry, C. D. Claussen, and B. J. Pichler, Simultaneous PET-MRI: a new approach for functional and morphological imaging, Nat Med, vol. 14, no. 4, pp. 459-465, 2008. [0211] [29] E. S. Amis Jr., P. F. Butler, K. E. Applegate, S. B. Birnbaum, L. F. Brateman, J. M. Hevezi, F. A. Mettler, R. L. Morin, M. J. Pentecost, G. G. Smith, K. J. Strauss, and R. K. Zeman, American College of Radiology white paper on radiation dose in medicine, J Am Coll Radiol, vol. 4, no. 5, pp. 272-284, 2007. [0212] [30] M. G. Olivier, R. Ludovic de, P.-Q. Marie, D. Luc, and F. M. Robert, Quantification strategies for MRI, in Molecular Imaging Techniques: New Frontiers, Future Science Ltd, 2013, pp. 66-80. [0213] [31] M. A. Bernstein, J. Huston 3rd, and H. A. Ward, Imaging artifacts at 3.0 T, J Magn Reson Imaging, vol. 24, no. 4, pp. 735-746, 2006. [0214] [32] C. T. Farrar, G. Dai, M. Novikov, A. Rosenzweig, R. Weissleder, B. R. Rosen, and D. E. Sosnovik, Impact of field strength and iron oxide nanoparticle concentration on the linearity and diagnostic accuracy of off-resonance imaging, NMR Biomed, vol. 21, no. 5, pp. 453-463, 2008. [0215] [33] L. de Rochefort, T. Nguyen, R. Brown, P. Spincemaille, G. Choi, J. Weinsaft, M. R. Prince, and Y. Wang, In vivo quantification of contrast agent concentration using the induced magnetic field for time-resolved arterial input function measurement with MRI,Med Phys, vol. 35, no. 12, pp. 5328-5339, 2008. [0216] [34] S. Walker-Samuel, M. O. Leach, and D. J. Collins, Reference tissue quantification of DCE-MRI data without a contrast agent calibration, Phys Med Biol, vol. 52, no. 3, pp. 589-601, 2007. [0217] [35] S. Boutry, D. Forge, C. Burtea, I. Mahieu, O. Murariu, S. Laurent, L. Vander Elst, and R. N. Muller, How to quantify iron in an aqueous or biological matrix: a technical note, Contrast Media Mol Imaging, vol. 4, no. 6, pp. 299-304, 2009. [0218] [36] M. C. Schabel and D. L. Parker, Uncertainty and bias in contrast concentration measurements using spoiled gradient echo pulse sequences, Phys Med Biol, vol. 53, no. 9, pp. 2345-2373, 2008. [0219] [37] M. Srinivas, P. A. Morel, L. A. Ernst, D. H. Laidlaw, and E. T. Ahrens, Fluorine-19 MRI for visualization and quantification of cell migration in a diabetes model, Magn Reson Med, vol. 58, no. 4, pp. 725-734, 2007. [0220] [38] J. Langley, W. Liu, E. K. Jordan, J. A. Frank, and Q. Zhao, Quantification of SPIO nanoparticles in vivo using the finite perturber method, Magn Reson Med, vol. 65, no. 5, pp. 1461-1469, 2011. [0221] [39] Q. Zhao, J. Langley, S. Lee, and W. Liu, Positive contrast technique for the detection and quantification of superparamagnetic iron oxide nanoparticles in MRI, NMR Biomed, vol. 24, no. 5, pp. 464-472, 2011. [0222] [40] U. C. Hoelscher, S. Lother, F. Fidler, M. Blaimer, and P. Jakob, Quantification and localization of contrast agents using delta relaxation enhanced magnetic resonance at 1.5 T, MAGMA, vol. 25, no. 3, pp. 223-231, 2012. [0223] [41] C. A. Gharagouzloo, P. N. McMahon, and S. Sridhar, Quantitative contrast-enhanced MRI with superparamagnetic nanoparticles using ultrashort time-to-echo pulse sequences,Magn. Reson. Med., vol. 00, pp. 1-11, August 2014. [0224] [42] J. H. Duyn, J. A. Yang Y Fau-Frank, J. W. Frank Ja Fau-van der Veen, J. W. van der Veen, and J. M. Reson, Simple correction method for k-space trajectory deviations in MRI, J. Magn. Reson., vol. 132, no. 1, pp. 150-153. [0225] [43] P. Philippe, R. Caroline, R. Isabelle, G. Irene, D. Anne, C. Claire, I. Jean-Marc, R. Jean-Sebastien, P. Marc, and R. Philippe, Superparamagnetic Contrast Agents, in Molecular and Cellular MR Imaging, CRC Press, 2007, pp. 59-83. [0226] [44] D. J. Tyler, M. D. Robson, R. M. Henkelman, I. R. Young, and G. M. Bydder, Magnetic resonance imaging with ultrashort TE (UTE) PULSE sequences: technical considerations, J Magn Reson Imaging, vol. 25, no. 2, pp. 279-289, 2007. [0227] [45] R. Fedorov A Fau-Beichel, J. Beichel R Fau-Kalpathy-Cramer, J. Kalpathy-Cramer J Fau-Finet, J.-C. Finet J Fau-Fillion-Robin, S. Fillion-Robin J Fau-Pujol, C. Pujol S Fau-Bauer, D. Bauer C Fau-Jennings, F. Jennings D Fau-Fennessy, M. Fennessy F Fau-Sonka, J. Sonka M Fau-Buatti, S. R. Buatti J Fau-Aylward, J. V Aylward S Fau-Miller, S. Miller J Fau-Pieper, R. Pieper S Fau-Kikinis, R. Kikinis, and Elsevier, 3D Slicer as an Image Computing Platform for the Quantitative Imaging Network, Magn Reson Imaging, vol. 30, no. 9, pp. 1323-1341. [0228] [46] S. Walker-Samuel, M. O. Leach, and D. J. Collins, Reference tissue quantification of DCE-MRI data without a contrast agent calibration, Phys. Med. Biol., vol. 52, no. 3, pp. 589-601, 2007. [0229] [47] M. C. Schabel and D. L. Parker, Uncertainty and bias in contrast concentration measurements using spoiled gradient echo pulse sequences, Phys. Med. Biol., vol. 53, no. 9, pp. 2345-2373, 2008. [0230] [48] E. Parzy, S. Miraux, F. Jean-Michel, and E. Thiaudire, In vivo quantification of blood velocity in mouse carotid and pulmonary arteries by ECG-triggered 3D time-resolved magnetic resonance angiography, NMR Biomed, vol. 22, no. 5, pp. 532-537, 2009. Committee for Medicinal Products for Human Use (CHMP), Ferumoxytol Assessment, 2012. [0231] [50] Y. Matsumura and H. Maeda, A new concept for macromolecular therapeutics in cancer chemotherapy: Mechanism of tumoritropic accumulation of proteins and the antitumor agent smancs, Cancer Res., vol. 46, no. 12 I, pp. 6387-6392, 1986. [0232] [51] A. E. Hansen, A. L. Petersen, J. R. Henriksen, B. Boerresen, P. Rasmussen, D. R. Elema, P. M. Rosenschoeld, A. T. Kristensen, A. Kjr, and T. L. Andresen, Positron Emission Tomography based Elucidation of the Enhanced Permeability and Retention Effect in Dogs with Cancer using Copper-64 Liposomes, ACS Nano, no. 7, pp. 6985-6995, 2015. [0233] [52] J. C. Sachdev, R. K. Ramanathan, N. Raghunand, J. Kim, S. G. Klinz, E. Bayever, J. B. Fitzgerald, and R. L. Korn, Abstract P5-01-06: Characterization of metastatic breast cancer lesions with ferumoxytol MRI and treatment response to MM-398, nanoliposomal irinotecan (nal-IRI), Cancer Res., vol. 75, no. 9 Supplement, pp. P5-01-06-P5-01-06, May 2015. [0234] [53] S. J. Shin, J. R. Beech, and K. A. Kelly, Targeted nanoparticles in imaging: paving the way for personalized medicine in the battle against cancer, Integr Biol, vol. 5, no. 1, pp. 29-42, 2013. [0235] [54] L. Zhang, X. Zhong, L. Wang, H. Chen, Y. A. Wang, J. Yeh, L. Yang, and H. Mao, T(1)-weighted ultrashort echo time method for positive contrast imaging of magnetic nanoparticles and cancer cells bound with the targeted nanoparticles, J Magn Reson Imaging, vol. 33, no. 1, pp. 194-202, 2011. [0236] [55] O. M. Girard, R. Ramirez, S. McCarty, and R. F. Mattrey, Toward absolute quantification of iron oxide nanoparticles as well as cell internalized fraction using multiparametric MRI, Contrast Media Mol Imaging, vol. 7, no. 4, pp. 411-417, 2012. [0237] [56] O. M. Girard, J. Du, L. Agemy, K. N. Sugahara, V. R. Kotamraju, E. Ruoslahti, G. M. Bydder, and R. F. Mattrey, Optimization of iron oxide nanoparticle detection using ultrashort echo time pulse sequences: comparison of T1, T2*, and synergistic T1-T2* contrast mechanisms, Magn Reson Med, vol. 65, no. 6, pp. 1649-1660, 2011. [0238] [57] A. B. Nayak, A. Luhar, M. Hanudel, B. Gales, T. R. Hall, J. P. Finn, I. B. Salusky, and J. Zaritsky, High-resolution, whole-body vascular imaging with ferumoxytol as an alternative to gadolinium agents in a pediatric chronic kidney disease cohort, Pediatr. Nephrol., vol. 30, no. 3, pp. 515-21, 2015. [0239] [58] M. D. Hope, T. A. Hope, C. Zhu, F. Faraji, H. Haraldsson, K. G. Ordovas, and D. Saloner, Vascular imaging with ferumoxytol as a contrast agent, Am. J. Roentgenol., vol. 205, no. 3, pp. W366-W373, 2015. [0240] [59] E. L. Barbier, L. Lamalle, and M. Dcorps, Methodology of brain perfusion imaging, J. Magn. Reson. Imaging, vol. 13, no. 4, pp. 496-520, 2001. [0241] [60] K. A. Rempp, G. Brix, F. Wenz, C. R. Becker, F. Gckel, and W. J. Lorenz, Quantification of regional cerebral blood flow and volume with dynamic susceptibility contrast-enhanced MR imaging, Radiology, vol. 193, no. 3, pp. 637-641, 1994. [0242] [61] T. Yankeelov and J. Gore, Dynamic contrast enhanced magnetic resonance imaging in oncology: theory, data acquisition, analysis, and examples, Curr. Med. Imaging Rev., vol. 3, no. 2, pp. 91-107, 2009. [0243] [62] I. Troprs, S. Grimault, A. Vaeth, E. Grillon, C. Julien, J. F. Payen, L. Lamalle, and M. Dcorps, Vessel size imaging,Magn. Reson. Med., vol. 45, no. 3, pp. 397-408, 2001. [0244] [63] T. Christen, W. Ni, D. Qiu, H. Schmiedeskamp, R. Bammer, M. Moseley, and G. Zaharchuk, High-resolution cerebral blood volume imaging in humans using the blood pool contrast agent ferumoxytol,Magn. Reson. Med., vol. Im, no. September 2012, pp. 705-710, 2012. [0245] [64] J. B. Mandeville, IRON fMRI measurements of CBV and implications for BOLD signal, Neurolmage, vol. 62, no. 2. pp. 1000-1008, 2012. [0246] [65] R. M. Henkelman, Measurement of signal intensities in the presence of noise in MR images,Medical physics, vol. 12, no. 2. pp. 232-233, 1985. [0247] [66] H. Gudbjartsson and S. Patz, The Rician distribution of noisy MRI data,Magn. Reson. Med., vol. 34, no. 6, pp. 910-914, 1995.