ELECTICAL PROPERTIES TOMOGRAPHY MAPPING OF CONDUCTIVITY CHANGES

Abstract

The invention provides for a medical imaging system (100, 300) comprising: a memory (110) for storing machine executable instructions (120); and a processor (104) for controlling the medical imaging system. Execution of the machine executable instructions causes the processor to: receive (200) a resting group of B1 phase maps (122) of a region (309) of interest of a subject (318); receive (202) an active group of B1 phase maps (124) of the region of interest of the subject, calculate (204) a resting group of conductivity maps (126) for the region of interest using the resting group of B1 phase maps according to an electrical properties tomography algorithm; calculate (206) an active group of conductivity maps (128) for the region of interest using the active group of B1 phase maps according to the electrical properties tomography algorithm, and calculate (208) a conductivity change mapping (130) for the region of interest using the resting group of conductivity maps and the active group of conductivity maps.

Claims

1. A medical imaging system comprising: a memory for storing machine executable instructions; and a processor for controlling the medical imaging system, wherein execution of the machine executable instructions causes the processor to: receive a resting group of B1 phase maps of a region of interest of a subject; receive an active group of B1 phase maps of the region of interest of the subject; calculate a resting group of conductivity maps for the region of interest using the resting group of B1 phase maps according to an electrical properties tomography algorithm; calculate an active group of conductivity maps for the region of interest using the active group of B1 phase maps according to the electrical properties tomography algorithm; and calculate a conductivity change mapping for the region of interest using the resting group of conductivity maps and the active group of conductivity maps.

2. The medical imaging system of claim 1, wherein the resting group of B1 phase maps image a brain, wherein the active group of B1 phase maps image the brain, and wherein the conductivity change mapping is descriptive of difference in brain activity.

3. The medical imaging system of claim 2, wherein the electrical properties tomography algorithm of the calculation of the active group of conductivity maps and the resting group of conductivity maps is at least partially implemented as a machine learning algorithm.

4. The medical imaging system of claim 2, wherein the electrical properties tomography algorithm of the calculation of the active group of conductivity maps and the resting group of conductivity maps is at least partially implemented as a forward differential equation solver.

5. The medical imaging system of claim 3, wherein the forward differential equation solver is configured to calculate the conductivity of each voxel using the Laplacian of the phase map for a kernel of voxels surrounding each voxel.

6. The medical imaging system of claim 5, wherein execution of the machine executable instructions further causes the processor to: receive a tissue segmentation assigning each voxel in the region of interest a tissue type; adjust the kernel of voxels surrounding each voxel using the tissue segmentation before calculating the Laplacian, wherein the kernel of voxels surrounding each voxel is adjusted such that all voxels within the kernel have the same tissue type.

7. The medical imaging system of claim 1, wherein execution of the machine executable instructions further causes the processor to: receive a magnitude image of the region of interest; and render the magnitude image and the conductivity change mapping on a display, and wherein any one of the following: the conductivity change mapping is superimposed on the magnitude image and the conductivity change mapping and the magnitude image are displayed in adjacent regions with an identical scale.

8. The medical imaging system of claim 1, wherein execution of the machine executable instructions further causes the processor to: receive magnetic resonance imaging data acquired according to a B1 phase mapping magnetic resonance imaging protocol descriptive of the region of interest of the subject; receive metadata that assigns portions of the magnetic resonance imaging data to a resting state of the subject or an active state of the subject; reconstruct multiple B1 magnetic resonance phase maps of the region of interest of the subject from the portions of the magnetic resonance imaging data; construct the active group of B1 phase maps and the resting group of B1 phase maps by assigning each of the multiple B1 magnetic resonance phase maps using the metadata.

9. The medical imaging system of claim 8, wherein the medical imaging system further comprises a magnetic resonance imaging system configured for acquiring the magnetic resonance imaging data from the subject from an imaging zone, wherein the memory further comprises pulse sequence commands, wherein the pulse sequence commands are configured to control the magnetic resonance imaging system to acquire the magnetic resonance imaging data from the region of interest according to the B1 phase mapping magnetic resonance imaging protocol, wherein the region of interest is within the imaging zone, wherein execution of the machine executable instructions further cause the processor to control the magnetic resonance imaging system to acquire the magnetic resonance imaging data using the pulse sequence commands.

10. The medical imaging system of claim 9, wherein the B1 phase mapping magnetic resonance imaging protocol is any one of the following: a balanced steady-state free precession magnetic resonance imaging protocol, a multi-echo-gradient echo magnetic resonance imaging protocol, and a spin echo based magnetic resonance imaging protocol.

11. The medical imaging system of claim 9, wherein the magnetic resonance imaging system further comprises a subject indicator configured for indicating the resting state and the active state to the subject, wherein execution of the machine executable instructions further causes the processor to: control the magnetic resonance imaging system to repeatedly acquire the magnetic resonance imaging data while the subject indicator alternates between the resting state and the active state; and generate the metadata for the magnetic resonance imaging data to match the subject indicator during the acquisition of the magnetic resonance imaging data.

12. The medical imaging system of claim 1, wherein the resting group of B1 phase maps and the active group of B1 phase maps each contain any one of the following: at least 5 B1 phase maps each, at least 10 B1 phase maps each, at least 20 B1 phase maps each, at least 40 B1 phase maps each, at least 60 B1 phase maps each, and at least 80 B1 phase maps each.

13. A method of operating a medical imaging system, wherein the method comprises: receiving a resting group of B1 phase maps of a region of interest of a subject; receiving an active group of B1 phase maps of the region of interest of the subject; calculating a resting group of conductivity maps for the region of interest using the resting group of B1 phase maps according to an electrical properties tomography algorithm; calculating an active group of conductivity maps for the region of interest using the active group of B1 phase maps according to the electrical properties tomography algorithm; and calculating a conductivity change mapping for the region of interest using the resting group of conductivity maps and the active group of conductivity maps.

14. A computer program product comprising machine executable instructions stored on a non-transitory computer readable medium for execution by a processor to control a medical imaging system, wherein execution of the machine executable instructions causes the processor to: receive a resting group of B1 phase maps of a region of interest of a subject; receive an active group of B1 phase maps of the region of interest of the subject; calculate a resting group of conductivity maps for the region of interest using the resting group of B1 phase maps according to an electrical properties tomography algorithm; calculate an active group of conductivity maps for the region of interest using the active group of B1 phase maps according to the electrical properties tomography algorithm; and calculate a conductivity change mapping for the region of interest using the resting group of conductivity maps and the active group of conductivity maps.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In the following preferred embodiments of the invention will be described, by way of example only, and with reference to the drawings in which:

[0048] FIG. 1 illustrates an example of a medical imaging system;

[0049] FIG. 2 shows a flow chart which illustrates a method of operating the medical imaging system of FIG. 1;

[0050] FIG. 3 illustrates a further example of a medical imaging system;

[0051] FIG. 4 shows a flow chart which illustrates a method of operating the medical imaging system of FIG. 3;

[0052] FIG. 5 illustrates how the multiple B1 magnetic resonance phase maps can be sorted; and

[0053] FIG. 6 illustrates examples of conductivity change mappings constructed according to some examples herein.

DETAILED DESCRIPTION OF THE EMBODIMENTS

[0054] Like numbered elements in these figures are either equivalent elements or perform the same function. Elements which have been discussed previously will not necessarily be discussed in later figures if the function is equivalent.

[0055] FIG. 1 illustrates an example of a medical imaging system 100. The medical imaging system 100 is shown as comprising a computer 102 that comprises a processor 104. The processor is shown as being connected to an optional hardware interface 106, and an optional user interface 108. The user interface 108 may be or include a display for rendering images. The hardware interface 106 may for example be a network interface or it may also be used for exchanging data or commands with other components of the medical imaging system. The processor 104 is further shown as being connected to a memory 110. The memory 110 may be any combination of memory which is accessible to the processor 104. This may include such things as main memory, cached memory, and also non-volatile memory such as flash RAM, hard drives, or other storage devices. In some examples the memory 104 may be considered to be a non-transitory computer-readable medium.

[0056] The memory is shown as containing machine-executable instructions 120. The machine-executable instructions 120 enable the processor 104 to perform various data processing tasks and also in some examples to control other components of the medical imaging system 100.

[0057] The memory 110 is shown as containing a resting group of B1 phase maps 122. The resting group of B1 phase maps 122 is a group of B1 phase maps that is labeled the resting group of B1 phase maps. The memory 110 is further shown as containing an active group of B1 phase maps 124. Likewise, the active group of B1 phase maps 124 is a group of B1 phase maps that is labeled active group of B1 phase maps 124. The memory 110 is further shown as containing a resting group of conductivity maps 126 that was calculated using each of the resting group of B1 phase maps 122. The memory 110 is further shown as containing an active group of conductivity maps 128 that was calculated using each of the active group of B1 phase maps 124. The memory 110 is further shown as containing a conductivity change mapping 130. The conductivity change mapping 130 is a change in conductivity for a region of interest of a subject that was calculated using the resting group of conductivity maps 126 and the active group of conductivity maps 128.

[0058] FIG. 2 shows a flowchart which illustrates a method of operating the medical imaging system 100 of FIG. 1. First in step 200 the processor 104 receives the resting group of B1 phase maps 122 which are for a region of interest of a subject. Next in step 202 the processor 104 receives the active group of B1 phase maps 124 of the region of interest of the subject. The resting group of B1 phase maps 122 and the active group of B1 phase maps 124 could be received in different ways. In one case they may be received via a network or other data transfer method. In other examples the medical imaging system could be part of a magnetic resonance imaging system and the resting group of B1 phase maps 122 and the active group of B1 phase maps 124 could be received by reconstructing them from magnetic resonance imaging data.

[0059] Next in step 204 the resting group of conductivity maps 126 is calculated for the region of interest from the resting group of B1 phase maps. Next in step 206 the active group of conductivity maps 128 is calculated for the region of interest using the active group of B1 phase maps 124. The resting group of conductivity maps 126 and the active group of conductivity maps 128 are both calculated according to an electrical properties tomography algorithm. Finally in step 208 the conductivity change mapping 130 is calculated for the region of interest using the resting group of conductivity maps 126 and the active group of conductivity maps 128. Optional steps which are not displayed in FIG. 2 may include such things as rendering the conductivity change mapping which may also include rendering the conductivity change mapping on a display with a magnetic resonance image that illustrates the region of interest.

[0060] FIG. 3 illustrates a further example of a medical imaging system 300. The medical imaging system 300 in FIG. 3 is similar to the medical imaging system 100 of FIG. 1 with the exception that the medical imaging system 300 also comprises a magnetic resonance imaging system.

[0061] The magnetic resonance imaging system 302 comprises a magnet 304. The magnet 304 is a superconducting cylindrical type magnet with a bore 306 through it. The use of different types of magnets is also possible; for instance it is also possible to use both a split cylindrical magnet and a so called open magnet. A split cylindrical magnet is similar to a standard cylindrical magnet, except that the cryostat has been split into two sections to allow access to the iso-plane of the magnet, such magnets may for instance be used in conjunction with charged particle beam therapy. An open magnet has two magnet sections, one above the other with a space in-between that is large enough to receive a subject: the arrangement of the two sections area similar to that of a Helmholtz coil. Open magnets are popular, because the subject is less confined. Inside the cryostat of the cylindrical magnet there is a collection of superconducting coils. Within the bore 306 of the cylindrical magnet 304 there is an imaging zone 308 where the magnetic field is strong and uniform enough to perform magnetic resonance imaging. A region of interest 309 is shown within the imaging zone 308. The magnetic resonance data that is acquired typically acquired for the region of interest. A subject 318 is shown as being supported by a subject support 320 such that at least a portion of the subject 318 is within the imaging zone 308 and the region of interest 309.

[0062] Within the bore 306 of the magnet there is also a set of magnetic field gradient coils 310 which is used for acquisition of preliminary magnetic resonance data to spatially encode magnetic spins within the imaging zone 308 of the magnet 304. The magnetic field gradient coils 310 are connected to a magnetic field gradient coil power supply 312. The magnetic field gradient coils 310 are intended to be representative. Typically magnetic field gradient coils 310 contain three separate sets of coils for spatially encoding in three orthogonal spatial directions. A magnetic field gradient power supply supplies current to the magnetic field gradient coils. The current supplied to the magnetic field gradient coils 310 is controlled as a function of time and may be ramped or pulsed.

[0063] Adjacent to the imaging zone 308 is a radio-frequency coil 314 for manipulating the orientations of magnetic spins within the imaging zone 308 and for receiving radio transmissions from spins also within the imaging zone 308. In this example the radio-frequency coil 314 is a head coil and the region of interest 309 images the brain of the subject 318.

[0064] The radio frequency antenna may contain multiple coil elements. The radio frequency antenna may also be referred to as a channel or antenna. The radio-frequency coil 314 is connected to a radio frequency transceiver 316. The radio-frequency coil 314 and radio frequency transceiver 316 may be replaced by separate transmit and receive coils and a separate transmitter and receiver. It is understood that the radio-frequency coil 314 and the radio frequency transceiver 316 are representative. The radio-frequency coil 314 is intended to also represent a dedicated transmit antenna and a dedicated receive antenna. Likewise the transceiver 316 may also represent a separate transmitter and receiver. The radio-frequency coil 314 may also have multiple receive/transmit elements and the radio frequency transceiver 316 may have multiple receive/transmit channels. For example if a parallel imaging technique such as SENSE is performed, the radio-frequency could 314 will have multiple coil elements.

[0065] Within the bore 306 of the magnet 304 there is a subject indicator 322. The subject indicator may for example provide an audio and/or visual stimulus to the subject 318. The subject indicator 322 is able to provide a stimulus in one of two different distinct states; an active state and a resting state. When the subject indicator 322 shows an active state the subject 318 either thinks particular thoughts or performs particular physical activity such as moving a limb or performing another action. The subject indicator 322 could for example have a light which is visible to the subject 318, be a display, or provide an audio signal. The transceiver 316, the gradient controller 312, and the subject indicator 322 are shown as being connected to the hardware interface 106 of the computer system 102.

[0066] The memory 110 is further shown as containing the pulse sequence commands 330. The pulse sequence commands are either commands or data which can be converted into such commands which enable the processor 104 to control the magnetic resonance imaging system 302. The memory 110 is further shown as containing magnetic resonance imaging data 332 that was acquired by controlling the magnetic resonance imaging system 302 with the pulse sequence commands 330. The pulse sequence commands 330 may also contain instructions which cause the subject indicator 322 to change between indicating the active and resting state during individual acquisitions of the magnetic resonance imaging data 332. Data which can be used to determine later which state the magnetic resonance imaging data 332 is in the meta data 334.

[0067] The meta data 334 is shown as being stored in the memory 110. The memory 110 shows multiple B1 magnetic resonance phase maps 336 that have been reconstructed from the magnetic resonance imaging data 332. The meta data 334 is a key which can be used to determine which of the multiple B1 magnetic resonance phase maps belong to the resting group of B1 phase maps 122 and the active group of B1 phase maps 124. Using the meta data 334 the processor 104 can sort the multiple B1 magnetic resonance phase maps 336 into the resting group of B1 phase maps 122 and the active group of B1 phase maps 124.

[0068] FIG. 4 shows a flowchart which illustrates a method of operating the medical imaging system 300 of FIG. 3. The method in FIG. 4 is similar to the method illustrated in FIG. 2 with the addition of a number of additional steps. The method of FIG. 4 starts with step 400. In step 400 the processor 104 controls the magnetic resonance imaging system 302 to repeatedly acquire the magnetic resonance imaging data 332. As step 400 is performed, step 402 is also performed. In step 402 the processor 104 generates the meta data 334 for the magnetic resonance imaging data 332. This is so that the meta data matches the subject indicator 322 during acquisition of the magnetic resonance imaging data.

[0069] Next in step 404 the processor receives the magnetic resonance imaging data 332. The magnetic resonance imaging data for this method has been acquired for the region of interest 309 of the subject's 318 brain region. Next in step 406 the processor 104 receives the meta data 334. Next in step 408 the processor reconstructs the multiple B1 magnetic resonance phase maps 336 from the portions of the magnetic resonance imaging data 332. Next in step 410 the processor 104 constructs or sorts the active group of B1 phase maps 124 and the resting group of phase maps 122 by assigning them each of the multiple B1 magnetic resonance phase maps using the meta data 334. After step 410 the method then proceeds to step 200 as is illustrated in FIG. 2.

[0070] Magnetic resonance imaging is a very valuable tool to visualize anatomy and morphology of different organs of the body. In addition to that, MRI can be used to measure brain activity by detecting changes associated with cerebral blood flow, also referred to as functional magnetic resonance imaging (fMRI). It relies on the fact that cerebral blood flow and neuronal activation are coupled. When a certain area of the brain is in use (e.g. parts of the motor cortex when a person is moving muscles willingly) the cerebral blood flow to that region as well as the oxygen consumption increases.

[0071] The hemodynamic response of brain activation causes magnetic and electric changes in the activated brain area. Up to now, it is only known how to image the magnetic changes of the hemodynamic response with MRI, e.g. based on cerebral blood flow (CBF) changes or magnetic properties of deoxygenated blood. It is not known how to image the electric changes of the hemodynamic response.

[0072] The primary form of fMRI is based on the blood-oxygen-level dependent (BOLD) effect. Hemoglobin presents different magnetic properties in its oxygenated and deoxygenated forms which lead to magnetic signal variation that can be detected using an MRI scanner, usually by a T2*-sensitive sequence. Given many repetitions of an action performed by a subject while scanning, statistical methods can be used to determine the areas of the brain which reliably have more of this difference as a result, and therefore which areas of the brain are most active during that action. The resulting brain activation can be color-coded and superimposed to previously acquired anatomical images, thus, visualizing active parts of the brain.

[0073] fMRI is clinically used to visualize brain activation with respect to a tumor in order to enable a surgeon to plan a surgery or make other therapy decisions. In neuro science, fMRI is a welcome tool to study complex processes in the brain related to behavior, action etc. which may lead to a better understanding of various diseases like depression, schizophrenia, autism, epilepsy etc.

[0074] Examples may provide for the use of Electric Properties Tomography (EPT) to measure electric conductivity and/or permittivity during brain activation. The measurements are performed similar to conventional fMRI experiments, but instead of the BOLD contrast EPT maps are generated for activation and resting periods. The data can be subsequently analysed by using statistical methods, but it is also possible to quantify electrical properties during activation and resting periods. BOLD measurements mainly rely on alterations due to deoxygenated blood increase, so that the signal may be biased towards the venous blood which would not be the case for the proposed approach.

[0075] The increase in cerebral blood flow increases the conductivity in the activated area (since blood conductivity˜1.25 S/m is higher than gray/white matter conductivity˜0.45 S/m) as well as the permittivity in the activated area (since (relative) blood permittivity˜70 is higher than (relative) gray/white matter permittivity˜60).

[0076] An EPT fMRI measurement can be performed using a balanced steady-state free precession (bSSFP) sequence (pulse sequence commands 330) for data acquisition. The image volume of the scan covers the whole brain or a single region where activity is expected (region of interest 309). The image volume is acquired several times, e.g. 60 dynamics are performed. During scanning, a volunteer or patient is being advised to perform a certain task alternating with resting periods, e.g. finger tapping, looking at pictures, producing words etc. (cf FIG. 5 below). In addition to the reconstructed magnitude images also the phase images of the scans are stored for subsequent reconstruction of electrical conductivity maps via

σ=Δφ/(2μω.sup.2),   (1)

where ω=Larmor frequency, Δ=Laplacian operator (second spatial derivative in 3D), φ=bSSFP phase map, and μ=magnetic permeability of the body. Conductivity is reconstructed for all 60 dynamics separately using Eq. (1), and results are investigated using the statistical methods usually applied for fMRI. The same procedure can be done reconstructing tissue permittivity, which is enabled if additionally the brain B1 map (i.e., the magnitude of the RF transmit field) is measured. Conductivity-based fMRI is more promising than permittivity-based fMRI, since (a) phase can be measured faster and more accurate than B1 magnitude, (b) differences between blood and brain are higher for conductivity than permittivity.

[0077] Comparing conductivity maps from the different dynamics, any errors from imperfect measurement or imperfect reconstruction cancel out, as long as these errors are (roughly) the same for all dynamics.

[0078] FIG. 5 shows a Fig. which illustrates how the multiple B1 magnetic resonance phase maps 336 can be sorted. The patient (subject 318) is being advised to perform a certain task (by the subject indicator 322) while the image volume is constantly being acquired. The top part of the plot shows a square wave waveform that indicates the signal 500 provided by the subject indicator 322. The two states have an activity state 504 and a resting state or inactivity 502. Below this is a series of images which represent the multiple B1 magnetic resonance phase maps 336. The data from the waveform 500 can be correlated to each of the multiple B1 magnetic resonance phase maps 336 in the form of meta data which are then used to sort the multiple B1 magnetic resonance phase maps 336 into the resting group of B1 phase maps 122 and the active group of B1 phase maps 124.

[0079] FIG. 6 illustrates examples of conductivity change mappings constructed according to some examples herein. There are nine images arranged in a matrix. In columns this corresponds to data that was acquired at identical times and for different motions of the subject. The images in column 600 correspond to left hand motion by the subject in the active state. Images in the middle column labeled 602 correspond to motion by the subject of the right hand during the active phase. The column to the far right labeled 604 corresponds to motion of the subject of both feet.

[0080] The images in the first row 606 show the results of calculating the t value between the conductivity maps with and without activation. The conductivity maps with activation correspond to the active group of conductivity maps 128. The conductivity maps without activation correspond to the resting group of conductivity maps 126. The Figs. in row 606 is one example of a conductivity change mapping 130 with and without activation. Row 608 illustrates results of a statistical t-test from a corresponding conventional fMRI study using the BOLD effect. The bottom row, row 610 shows the results of row 608 superimposed on a bSSFP magnitude image. In each column 600, 602, 604 there are regions of interest 612. Within each column the region of interest 612 is identical.

[0081] FIG. 6 above shows results for three different volunteer experiments using a 3T magnet, testing motion of the left hand, the right hand, and the feet. The area of brain activation is almost the same for fMRI using conventional EPI and using EPT as proposed. The difference in conductivity with/without activation is of the order of 0.1 S/m, corresponding to a blood volume change of 10%, which is in line with expectations. Using EPT, the activated region appears blurred towards the inner part of the brain, which can be explained by the following issue:

[0082] Solving the Laplacian of Eq. (1) numerically requires an ensemble of voxels (the so-called “kernel”) around the target voxel. This kernel has to be based on voxels with the same conductivity as the target voxel to avoid reconstruction errors. Usually, this is realized by taking tissue boundaries from the bSSFP magnitude image into account, to match local geometric shape of kernel and tissue. In the case of fMRI, no tissue boundary between activated/non-activated areas was available, which is the reason for the above-mentioned observation that the activated region appears blurred. The blurring appears only towards the inner part of the brain (since no clear boundary is given on this side of the activation area), but blurring does not appear towards the outer part of the brain (since a clear boundary is given on this side of the activation area).

[0083] While the invention has been illustrated and described in detail in the drawings and foregoing description, such illustration and description are to be considered illustrative or exemplary and not restrictive; the invention is not limited to the disclosed embodiments.

[0084] Other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from a study of the drawings, the disclosure, and the appended claims. In the claims, the word “comprising” does not exclude other elements or steps, and the indefinite article “a” or “an” does not exclude a plurality. A single processor or other unit may fulfill the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measured cannot be used to advantage. A computer program may be stored/distributed on a suitable medium, such as an optical storage medium or a solid-state medium supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the Internet or other wired or wireless telecommunication systems. Any reference signs in the claims should not be construed as limiting the scope.

LIST OF REFERENCE NUMERALS

[0085] 100 medical imaging system [0086] 102 computer [0087] 104 processor [0088] 106 hardware interface [0089] 108 user interface [0090] 110 computer memory [0091] 120 machine executable instructions [0092] 122 resting group of B1 phase maps [0093] 124 active group of B1 phase maps [0094] 126 resting group of conductivity maps [0095] 128 active group of conductivity maps [0096] 130 conductivity change mapping [0097] 200 receive a resting group of B1 phase maps of a region of interest of a subject [0098] 202 receive an active group of B1 phase maps of the region of interest of the subject [0099] 204 calculate a resting group of conductivity maps for the region of interest using the resting group of B1 phase maps according to an electrical properties tomography algorithm [0100] 206 calculate an active group of conductivity maps for the region of interest using the active group of B1 phase maps according to the electrical properties tomography algorithm [0101] 208 calculate a conductivity change mapping for the region of interest using the resting group of conductivity maps and the active group of conductivity maps [0102] 300 medical imaging system [0103] 302 magnetic resonance imaging system [0104] 304 magnet [0105] 306 bore of magnet [0106] 308 imaging zone [0107] 309 region of interest [0108] 310 magnetic field gradient coils [0109] 312 magnetic field gradient coil power supply [0110] 314 radio-frequency coil [0111] 316 transceiver [0112] 318 subject [0113] 320 subject support [0114] 322 subject indicator [0115] 330 pulse sequence commands [0116] 332 magnetic resonance imaging data [0117] 334 metadata [0118] 336 multiple B1 magnetic resonance phase maps [0119] 400 control the magnetic resonance imaging system to repeatedly acquire the magnetic resonance imaging data while the subject indicator alternates between the resting state and the active state [0120] 402 generate the metadata for the magnetic resonance imaging data to match the subject indicator during the acquisition of the magnetic resonance imaging data [0121] 404 receive magnetic resonance imaging data acquired according to a B1 phase mapping magnetic resonance imaging protocol descriptive of the region of interest of the subject [0122] 406 receive metadata that assigns portions of the magnetic resonance imaging data to a resting state of the subject or an active state of the subject [0123] 408 reconstruct multiple B1 magnetic resonance phase maps of the region of interest of the subject from the portions of the magnetic resonance imaging data [0124] 410 construct the active group of B1 phase maps and the resting group of B1 phase maps by assigning each of the multiple B1 magnetic resonance phase maps using the metadata [0125] 500 signal provided by subject indicator [0126] 502 resting state [0127] 504 active state [0128] 600 left hand motion during active period [0129] 602 right hand motion during active period [0130] 604 motion of both feet during active period [0131] 606 t-value between conductivity maps with/without activation [0132] 608 results of conventional fMRI using BOLD [0133] 610 figures from 608 overlaid on bSSFP magnitude maps [0134] 612 region of interest with mapping