MAGNETIC RESONANCE IMAGING

Abstract

The present invention relates generally to medical imaging and, more particularly, relates to systems and methods for obtaining magnetic resonance (MR) images of tissues and organs (particularly of the heart) or parts thereof.

Claims

1-20. (canceled)

21. A computer-implemented method for obtaining an indication of the differences in the performance of all or part a subject's tissue or organ under different conditions, the method comprising the steps of: (A) obtaining a first heart-rate-compensated magnetic resonance (MR) image of all or part of a tissue or organ of a subject, by a method comprising the steps: (a) acquiring, with an MR system, an MR data set from all or part of the tissue or organ of the subject using a pulse sequence, wherein the pulse sequence comprises at least two interleaved components: (i) a first component, wherein the first component consists of a T2- or T2*-weighted readout, and (ii) a second component, wherein the second component is a low flip angle readout without additional magnetisation preparation; (b) generating at least two image datasets from the MR dataset: a first image dataset derived from the signals obtained from the first component of the pulse sequence, and a second image dataset derived from the signals obtained from the second component of the pulse sequence; (c) normalising the first image dataset using the second image dataset as a reference dataset to produce a heart-rate-compensated MR image of all or part of the tissue or organ; wherein this first heart-rate-compensated MR image is obtained whilst subjecting the subject or all or part of the subject's tissue or organ to a first set of conditions; (B) obtaining a second heart-rate-compensated magnetic resonance (MR) image of all or part of the tissue or organ of the subject by the method as defined in Step (A), (a)-(c), wherein this second heart-rate-compensated MR image is obtained whilst subjecting the subject or all or part of the subject's tissue or organ to a second set of conditions, wherein the first set of conditions are different from the second set of conditions; and (C) comparing the first and second heart-rate-compensated MR images to obtain an indication of the differences in the performance of all or part of the subject's tissue or organ under the first and second conditions.

22. The method as claimed in claim 21, wherein the organ is a visceral organ; or a heart, liver, spleen, kidney, prostate, lung or pancreas.

23. The method as claimed in claim 21, wherein the tissue or organ is impaired or diseased.

24. The method as claimed in claim 21, wherein: (A) the first component of the pulse sequence provides strong T2-weighted or T2*-weighted MR data; or (B) the first component of the pulse sequence comprises a T2-preparation module or T2* preparation module.

25. The method as claimed in claim 21, wherein the first component of the pulse sequence comprises or consists of a gradient echo readout; an inherently T2-weighted readout; or an inherently T2*-weighted readout.

26. The method as claimed in claim 25, wherein the first component of the pulse sequence comprises or consists of a RF-spoiled gradient echo (FLASH), steady state free precession (SSFP) or balanced SSFP (bSSFP); a single shot fast spin echo or spin echo EPI; or a long echo time GRE/FLASH, GRE-EPI or FLASH.

27. The method as claimed in claim 21, wherein the first component of the pulse sequence comprises or consists of a T2 prepared bSSFP or FLASH, or a T2-prepared segmented bSSFP sequence.

28. The method as claimed in claim 21, wherein the second component of the pulse sequence comprises or consists of a low flip-angle GRE, SPGR, FLASH or GRE-EPI.

29. The method as claimed in claim 28, wherein the second component of the pulse sequence comprises or consists of a low flip angle FLASH readout, or a FLASH readout wherein the flip-angle is 1 to 10°, or 3 to 5°.

30. The method as claimed in claim 21, wherein the first component of the pulse sequence consists of a segmented T2-prepared bSSFP sequence, optionally with a T2-preparation module or T2* preparation module; and the second component of the pulse sequence consists of a segmented 5° FLASH sequence.

31. The method as claimed in claim 21, wherein the pulse sequence is synchronized with the subject's ECG signal to acquire MR data during a rest phase of the subject's heart cycle.

32. The method as claimed in claim 21, wherein the pulse sequence comprises a plurality of first and second components, wherein one second component of the pulse sequence is interleaved between adjacent pairs of first components of the pulse sequence.

33. The method as claimed in claim 32, wherein: (A) the second components of the pulse sequence are interleaved equidistantly between adjacent pairs of first components of the pulse sequence; or (B) the second components of the pulse sequence are interleaved non-equidistantly between adjacent pairs of first components of the pulse sequence.

34. The method as claimed in claim 33, wherein the first and second components are each temporally regularly spaced, one second component is interleaved between adjacent pairs of first components, and the time interval between the second component and the subsequent first component is less than the time interval between the first component and the subsequent second component.

35. The method as claimed in claim 34, wherein the time interval between the first component and the subsequent second component is 50-60%, 60-70%, 70-80%, 80-90% or 90-99.9% of the total time interval between consecutive first components, or 80-85%, 85%-90%, 90-95% or 95-99.9% of the total time interval between consecutive first components.

36. The method as claimed in claim 21, wherein a heart-rate compensated MR image of all or part of the subject's tissue or organ is displayed from the third image data set in colour wherein different signal intensity values or ranges are represented by different colours.

37. The method as claimed in claim 21, wherein: (A) (i) the first set of conditions are wherein the subject is under a stress; and  (ii) the second set of conditions are wherein the subject is at rest; or (B) (i) the first set of conditions are wherein the subject is at rest but has been exercising for a prescribed period beforehand; and  (ii) the second set of conditions are wherein the subject is at rest and has been at rest for a prescribed period beforehand.

38. The method as claimed in claim 21, wherein: (A) (i) the first set of conditions are wherein a vasoactive agent has been administered to the subject; and  (ii) the second set of conditions are control conditions wherein a vasoactive agent has not been administered to the subject.

39. A system or apparatus comprising at least one processing means arranged to carry out the steps of the method as claimed in claim 21.

40. A carrier bearing software comprising instructions for configuring a processor to carry out the steps of the method as claimed in claim 21.

Description

BRIEF DESCRIPTION OF THE FIGURES

[0101] Many aspects of the disclosure can be better understood with reference to the following Figures. The components in the Figures are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. Moreover, in the figures, like reference numerals designate corresponding parts throughout the several views.

[0102] FIG. 1. Comparison of mean signal intensity change of mBOLD and nBOLD showing the wide variance of mBOLD.

[0103] FIG. 2. Comparison of mean difference in segmental BOLD SIΔ for mBOLD (blue) and nBOLD (green) confirms a greater variance in segmental measurement of mBOLD. (Error Bars represent standard deviation).

[0104] FIG. 3. Two cases illustrating the difference in grey scale and colour for healthy subjects during rest (left) and stress (right).

[0105] FIG. 4. A case showing the easy identification of off-resonance artefacts (red arrow), which is more apparent on the colour map (B) as compared to the grey scale (A).

[0106] FIG. 5 is a schematic block diagram of an apparatus in which embodiments of the method of the invention may be implemented.

[0107] FIG. 6 shows a flowchart depicting some example methods of obtaining MRI images according to the invention.

[0108] FIG. 7 shows a Bloch simulation of the longitudinal magnetisation during an experiment and includes the timings of the T2-prepared SSFP (first readout) and FLASH (second readout), with the SSFP and FLASH equidistant between the two.

[0109] FIG. 8 shows the same as FIG. 7, but in the case that the second, FLASH readout is delayed to run immediately prior to the T2-prepared SSFP (first) readout.

EXAMPLES

[0110] The present invention is further illustrated by the following Examples, in which parts and percentages are by weight and degrees are Celsius, unless otherwise stated. It should be understood that these Examples, while indicating preferred embodiments of the invention, are given by way of illustration only. From the above discussion and these Examples, one skilled in the art can ascertain the essential characteristics of this invention, and without departing from the spirit and scope thereof, can make various changes and modifications of the invention to adapt it to various usages and conditions.

[0111] Thus, various modifications of the invention in addition to those shown and described herein will be apparent to those skilled in the art from the foregoing description. Such modifications are also intended to fall within the scope of the appended claims.

Example 1: Methods

[0112] Bloch Simulations Bloch simulations were carried out in order to modify the heart rate correction previously reported (12) to account for the additional FLASH readout and heartbeat between SSFP readouts. The T2 prep module was modelled as a multiplication in longitudinal magnetization, M.sub.z, during a time TE.sub.prep, by a factor exp(-T2/TE.sub.prep), where TE.sub.prep is the T2 prep echo time of 40 ms. The SSFP and FLASH readouts were implemented with identical timing to the imaging sequence, with TR/TE=2.86 ms/1.43 ms, 72 readout lines per heartbeat, flip angles of 44° (SSFP) and 5° (FLASH), with 10 linear ramp up pulses for SSFP. The two images were acquired in an interleaved fashion over three heartbeats each (six in total) with dummy SSFP and FLASH acquisitions beforehand (eight heartbeats total). In order to represent RF spoiling in the FLASH readout, M.sub.xy was reset to zero at the end of each short TR period. The mean M.sub.z just prior to each T2 prep was averaged to determine the steady state longitudinal magnetization. Myocardial T1 was set at 1471 ms and T2 at 44 ms to represent normal values at 3T (14). The sequence was simulated at RR intervals from 400 ms to 1500 ms in 50 ms increments.

[0113] An exponential of the form

M.sub.z=1−βe.sup.−RR/T.sup.1,mod

was fitted to the resulting steady-state M.sub.z to produce an expression for heart rate correction in the same form as used in previous work (6, 12).

[0114] Population

[0115] CMR data from twenty healthy subjects was retrospectively analysed to address the aims of this study. Subjects had previously been scanned in a study was approved by the institutional ethics committee (reference12/LO/1979) and were selected as the first subjects in the study with SSFP BOLD imaging free of susceptibility artefacts. All subjects were regarded as healthy with no previous medical history, cardiac disease or risk factors for cardiac disease.

[0116] CMR Protocol

[0117] All 20 participants underwent cardiac magnetic resonance (CMR) at 3 Tesla (3T), Trio MR scanner (Siemens, Erlangen, Germany) for cine, adenosine stress BOLD and perfusion imaging. Participants were instructed to refrain from caffeine-containing drinks and food for at least 24 hours preceding the study. Cine CMR was planned and acquired from standard pilot images. Short-axis cine images covering the entire left ventricle were acquired using a retrospectively ECG-gated SSFP sequence (echo time, 1.5 ms; repetition time, 3 ms; flip angle, 50°). For BOLD-CMR, a single basal slice was acquired at systole using an ECG-gated T2-prepared segmented SSFP sequence with interleaved low flip angle FLASH reference images (13) (Siemens WIP 567, VB17). The sequence parameters matched those used for the Bloch simulations. This sequence outputs two images, the SSFP image alone, labelled “mag”, and the SSFP divided by the interleaved FLASH image, labelled “norm”. We use “magnitude” and “normalized” herein to refer to these images and signal intensities derived from them. Shimming and centre frequency adjustments were performed before BOLD imaging to generate images free from off-resonance artefacts. Adenosine was then infused at a dose of 140 mcg/kg/min and at peak vasodilator stress (at least 3-4 minutes) a slice-matched stress BOLD image was acquired. Blood pressure was recorded by a vital signs monitor machine at baseline and at 1-minute intervals during stress. Following the acquisition of stress BOLD images, first pass perfusion imaging was undertaken using a T1-weighted gradient echo sequence with saturation recovery magnetization preparation. A dose of 0.03 mmol/kg of Gadoterate Meglumine was injected at 6 ml/sec during stress followed by a saline flush 12 ml at 6 ml/sec and the same dose for rest acquisition (15).

[0118] CMR Image Analysis

[0119] Commercially available software (Circle Cardiovascular Imaging Inc., Calgary, Canada) was used to analyse left ventricular (LV) volumes, mass, ejection fraction (16, 17), myocardial perfusion reserve index (MPRI) and BOLD SI. Quantitative analysis of rest and stress BOLD images without (magnitude image; mBOLD SI) and with FLASH normalisation (normalised image; nBOLD SI) were undertaken by two observers (MH and KC). The signal intensity in the magnitude images was HR corrected based on the Bloch simulations described above. BOLD ΔSI was estimated as the relative increase in signal intensity between rest and stress BOLD images as previously described (12). For perfusion analysis, signal intensity curves were generated to measure MPRI as previously described (18).

[0120] To assess intra-observer variability, measurements were repeated on both magnitude and normalized imaged for the same subjects by one of the observers (KC) after two weeks.

[0121] Commercially available software (Circle Cardiovascular Imaging Inc., Calgary, Canada) T2 mapping module was also used to develop a colour map to visually represent SI variations in the myocardium based on the signal intensity ranges in the normalized images. Bright green was used to represent pixels with SI two standard deviations (2 SD) lower than the mean rest BOLD SI (˜200 AU) and orange for SI 2 SD above the mean rest SI˜238 AU. Coincidentally, this SI was also 2 SD below the mean segmental stress SI. Finally, red was used for the highest signal intensity ˜280 AU (2SD above the mean stress SI). For SI below the physiological range (˜175 AU), we used blue.

[0122] Statistical Analysis

[0123] All statistical analyses were undertaken using IBM SPSS Statistics version 23.0 (IBM Corp., Armonk, N.Y., USA), except for the tests for normality and linear mixed modelling which were carried out in Matlab (Mathworks, Natick, Mass.). Analysis was carried out for slice-averaged data for the raw signal intensities in the normalized and HR-corrected magnitude images, and for both slice-averaged and segmental nBOLD and mBOLD signals. All variables were tested for normality with the Kolmogorov-Smirnov test with p>0.1 (for normality tests only) taken to indicate data consistent with a normal distribution.

[0124] Data (slice/segmentally averaged, signal intensities and BOLD ΔSI) were characterized by mean and standard deviation and the coefficient of variation calculated. A one-sided F-test was used to test whether the population variance was reduced in slice-averaged SI from normalized images relative to that from magnitude images.

[0125] Paired, two-sided t-tests were used to test whether nBOLD and mBOLD were statistically significantly different from each other, both for the whole slice and for each segment, and f-tests used to test whether both whole slice and segmental nBOLD variance was lower than mBOLD variance. Linear mixed models were used to assess the dependence of segmental mBOLD, nBOLD and BOLD difference (mBOLD-nBOLD) on the fixed effects segment, rest HR, stress HR, and segmental MPRI. Subject intercept was included as a random parameter and models were compared using likelihood ratio tests to determine whether the inclusion of the fixed effects one by one improved the model and should therefore be included. Visual inspection of residual plots did not reveal any obvious deviations from homoscedasticity or normality. Statistical significance was indicated by p<0.05.

[0126] Two-way random Intra Class Correlation (ICC) was used to assess the level of agreement between observers and two-way mixed ICC was used to level of agreement within the same observer at a per-segment level and per-subject level. Reproducibility was deemed to have improved statistically significantly if the confidence intervals did not overlap.

Example 2: Results

[0127] Bloch Simulations

[0128] The resulting equation for HR correction of magnitude images was

[00001]$\begin{array}{cc}S=\frac{S_0}{1-0.98e^{-\mathrm{RR}/980}}& \left(1\right)\end{array}$

[0129] where S.sub.0 is the measured signal intensity, S is the heart rate corrected signal intensity, and RR denotes the RR interval during the BOLD acquisition in ms.

[0130] Baseline Characteristics

[0131] Data from all 20 subjects and all 240 (rest and stress) segments were included for the analysis. Baseline characteristics are listed in Table 1. Mean age of all subjects was 47±15 years. Eleven (55%) out of 20 were male. Left ventricular indices and myocardial perfusion reserve indexes were within normal limits. All patients had a low (<10%) 10 year Framingham risk of coronary disease. Signal intensities and BOLD ΔSI, both whole slice and segmental, as well as heart rate changes, were all normally distributed.

TABLE-US-00001 TABLE 1 Baseline characteristics of healthy controls. CMR (n = 20) Age (years) 42 ± 12 Male, % (n) 55 (11) Body mass index (kg/m.sup.2) 25 ± 3  Rest heart rate (bpm) 62 ± 13 Stress heart rate (bpm) 93 ± 20 Absolute increase in heart rate 31 ± 11 Relative increase in heart rate   50 ± 16% CMR findings LVEF, % 63 ± 16 LVEDV (ml) 151 ± 31  LVESV (ml) 98 ± 10 Stroke volume (ml) 103 ± 21  LV Mass (g) 91 ± 15 LV Mass index (g/m.sup.2) 81 ± 28 MPRI 1.96 ± 0.38

[0132] Data are mean±standard deviation. LV, Left ventricular; EDV, end-diastolic volume; ESV, end-systolic volume; EF, ejection fraction; MPRI Myocardial perfusion reserve index, bpm beats per minute.

[0133] FLASH-“normalized” and HR-corrected “magnitude” image signal intensities

[0134] Slice Level Comparisons

[0135] In the mean heart rate (HR) corrected mBOLD SI mean and (HR uncorrected) nBOLD SI at rest and stress, an F-test showed that the variance in SI was statistically significantly reduced in the nBOLD images at both rest and stress (p<0.0001).

[0136] mBOLD and nBOLD

[0137] Slice Comparisons

[0138] The relative increase in SI for mBOLD and nBOLD during stress were similar 17±10% and 18±3% respectively, with no statistically significant difference between the two (p=0.79) (FIG. 1), corresponding to coefficients of variance of 59% and 17%. mBOLD ΔSI has a significantly higher variance compared to nBOLD ΔSI on a per-slice/subject basis (p<0.0001).

[0139] Segmental Comparisons

[0140] Segmental mBOLD and nBOLD ΔSI are shown in Table 2, along with the results of the statistical comparisons of values and variances. There was no significant difference in BOLD values between mBOLD and nBOLD, and but the AS segment showed a statistically significant improvement in variance with nBOLD over mBOLD. The data are also presented in FIG. 2.

TABLE-US-00002 TABLE 2 Comparison of segmental ΔSI for mBOLD and nBOLD images. Comparison p-values t-test f-test mBOLD ΔSI nBOLD ΔSI (difference (difference Mean ± s.d. CoV Mean ± s.d. CoV in means) in variances) Slice 17.4% ± 9.8%  56% 18.1% ± 2.8% 15.5%.sup.  0.75 <0.001 average A 18.0% ± 17.7% 98% 21.8% ± 8.2% 38% 0.280 0.001 AS 17.2% ± 8.9%  52%  16.1% ± 6.05% 38% 0.567 0.09 IS 15.3% ± 13.0% 85% 15.3% ± 3.7% 24% 0.994 <0.001 I 20.5% ± 14.7% 70% 19.1% ± 5.7% 30% 0.691 <0.001 IL 22.7% ± 18.7% 82% 19.2% ± 9.3% 48% 0.346 0.003 AL 15.8% ± 16.0% 101%  18.5% ± 7.2% 39% 0.426 <0.001 A anterior, AS anteroseptal, IS Inferoseptal, I Inferior, IL Inferolateral, AL Anterolateral

[0141] Origins of Differences Between mBOLD and nBOLD

[0142] Building linear mixed models for segmental BOLD responses showed that mBOLD ΔSI only showed a statistically significant dependence on stress heart rate (0.23%/bpm, equivalent to 17% BOLD ΔSI for the range of stress heart rates in these normal volunteers, p=0.03). In contrast, nBOLD ΔSI had no dependence on heart rate, rest or stress, but did have some segmental dependence (anterior ΔSI was 6.5% higher than inferoseptal, p=0.003). Only the heart rate dependence of mBOLD was reflected in the mixed model of the BOLD difference (mBOLD-nBOLD), which had a similar dependence on stress HR (0.24%/bpm, p=0.04) but no segmental dependence.

[0143] Slice and Segmental Reproducibility

[0144] On a slice-level, inter- and intra-observer ICC for nBOLD were excellent at 0.88 (95% CI 0.71-0.95) and 0.90 (95% CI 0.74-0.96), p<0.001. Similarly, mBOLD had a high inter-observer ICC and intra-observer ICC at 0.84 (95% CI 0.59-0.93) and 0.92 (95% CI 0.79-0.97), p<0.001 respectively.

[0145] On a segmental level, nBOLD had a higher inter- and intra-observer ICC compared to mBOLD with very minimal overlap of confidence intervals (Table 3).

TABLE-US-00003 TABLE 3 Inter-observer and intra-observer intra-class correlation coefficient for segmental analysis for mBOLD and nBOLD ICC 95% CI p-value mBOLD Interobserver 0.77 0.67-0.84 <0.0001 Intraobserver 0.85 0.76-0.90 <0.0001 nBOLD Interobserver 0.89 0.84-0.92 <0.0001 Intraobserver 0.92 0.89-0.95 <0.0001 CI = confidence interval; ICC = intraclass correlation.

[0146] Colour Map

[0147] Two examples of applying the standardized colour map derived from the normal population limits in the normalized rest and stress signal intensities are shown in FIG. 3. When applied to the normalized images, the colour map enables a clear visualization of the difference between rest and stress perfusion in this group of normal volunteers, even before calculating the change in BOLD SI.

[0148] The application of the colour map to the normalised image without the need for additional HR correction also enabled the rapid identification of artefacts which are otherwise difficult to appreciate on the grey scale magnitude image. FIG. 4 demonstrates an example of susceptibility artifact near the heart lung interface which can be more clearly visualized using a colour map than in the grey scale image.

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