ESTIMATION OF B0 INHOMOGENEITIES FOR IMPROVED ACQUISITION AND/OR RECONSTRUCTION OF MAGNETIC RESONANCE IMAGES

Abstract

Disclosed herein is a medical system (100, 300, 500) comprising a memory (110) storing machine executable instructions (120) and a B.sub.0 field estimation module (126); and a computational system (106). Execution of the machine executable instructions causes the computational system to receive (200) an initial magnetic resonance image (122) that comprises a magnitude component and is descriptive of a first region (326) of interest of a subject (118). Execution of the machine executable instructions further causes the computational system to perform at least one iteration of the following: receive (202) subsequent k-space data (124) descriptive of subsequent region of interest (328) of the subject; calculate (204) an estimated B.sub.0 field mapping (128) for the subsequent region of interest from the initial magnetic resonance image by inputting the initial magnetic resonance image into the B.sub.0 field estimation module; and reconstruct (206) a corrected magnetic resonance image (130) from the subsequent k-space data and the estimated B.sub.0 field mapping.

Claims

1. A medical system comprising: a memory configured to store machine executable instructions and a B.sub.0 field estimation module configured to output an estimated B.sub.0 field mapping in response to receiving at least a magnitude component of an initial magnetic resonance image as input; and a computational system configured to control the medical system, wherein execution of the machine executable instructions causes the computational system to receive the initial magnetic resonance image, wherein the initial magnetic resonance image is descriptive of a first region of interest of a subject; wherein execution of the machine executable instructions further causes the computational system to: receive subsequent k-space data descriptive of a subsequent different region of interest of the subject, wherein the subsequent region of interest at least partially overlaps with the first region of interest and wherein the subsequent k-space date includes motion parameters descriptive of motion of the subject during or after the acquisition of the subsequent k-space data; calculate the estimated B.sub.0 field mapping for the subsequent region of interest from the initial magnetic resonance image by inputting the initial magnetic resonance image into the B.sub.0 field estimation module; and reconstruct a corrected magnetic resonance image from the subsequent k-space data and the estimated B.sub.0 field mapping.

2. The medical system of claim 1, wherein the medical system further comprises a magnetic resonance imaging system configured to acquire k-space data from an imaging zone, wherein the memory further contains first pulse sequence commands configured to acquire initial k-space data from the first region of interest, wherein the memory further contains a set of second pulse sequence commands each configured to acquire the subsequent k-space data from the subsequent region of interest, wherein execution of the machine executable instructions further causes the computational system to: control the magnetic resonance imaging system with the first pulse sequence commands to acquire the initial k-space data; and reconstruct the initial magnetic resonance image from the initial k-space data; and control the magnetic resonance imaging system with one of the set of second pulse sequence commands to acquire the subsequent k-space data for each iteration in that each iteration includes reconstructing a corrected magnetic resonance image from the subsequent k-space data and the estimated B0 field mapping.

3. The medical system of claim 2, wherein the magnetic resonance imaging system comprises a main magnet configured to generate a B.sub.0 magnetic field in the imaging zone, wherein the magnetic resonance imaging system further comprises an adjustable B.sub.0 magnetic field shim configured to shim the B.sub.0 magnetic field in the imaging zone, wherein execution of the machine executable instructions further causes the computational system to perform the following before each acquisition of the subsequent k-space data: calculate updated B.sub.0 shim settings configured to reduce B.sub.0 inhomogeneity using the estimated B.sub.0 field mapping; and shim the B.sub.0 magnetic field by controlling the adjustable B.sub.0 magnetic field shim with the updated B.sub.0 shim settings.

4. The medical system of claim 3, wherein the estimated B.sub.0 field mapping is calculated at least partially using the updated B.sub.0 shim settings.

5. The medical system of claim 3, wherein execution of the machine executable instructions further causes the computational system to perform the following for the acquisition of the subsequent k-space data: receive motion parameters descriptive of motion of the subject during or after acquisition of subsequent k-space data; wherein the estimated B.sub.0 field is calculated at least partially using the motion parameters.

6. The medical system of claim 5, wherein the medical system further comprises a motion sensor system configured for at least partially measuring the motion parameters, wherein execution of the machine executable instructions further causes the computational system to control the motion sensor system to measure the motion parameters.

7. The medical system of claim 5, wherein execution of the machine executable instructions further causes the computational system to: reconstruct an intermediate image from the subsequent k-space data; calculate a registration between the intermediate image and the initial magnetic resonance image; and calculate the motion parameters from the registration.

8. The medical system of claim 5, wherein the estimated B.sub.0 field mapping is calculated at least partially by at least one of the following: using an analytical model to calculate a spatial transformation of the estimated B.sub.0 field mapping using the motion parameters; or inputting the initial magnetic resonance image and the motion parameters into a trained neural network.

9. The medical system of claim 2, wherein the memory further contains a system model configured to output time dependent data descriptive of electromagnetic properties of the magnetic resonance imaging system in response to inputting the one of the set of second pulse sequence commands, wherein execution of the machine executable instructions further causes the computational system to calculate the time dependent data by inputting the subsequent pulse sequence commands into the system model, wherein the corrected magnetic resonance image is reconstructed from the subsequent k-space data, the estimated B.sub.0 field mapping, and the time dependent data.

10. The medical system of claim 9, wherein the time dependent data is descriptive of at least one of the following: B.sub.0 inhomogeneities caused by eddy currents, temperature dependent gradient magnetic field nonlinearities, time dependent gradient magnetic field nonlinearities, changes in magnetic resonance coil sensitivities, motion dependent B.sub.1 inhomogeneities, static magnetic gradient field nonlinearities, or concomitant magnetic field corrections.

11. The medical system of claim 2, wherein the one of the second pulse sequence commands is configured to acquire the subsequent k-spaced data according to at least one of the following: according to an Echo Planar Imaging magnetic resonance imaging protocol; according to a Multiband magnetic resonance imaging protocol; with a spiral k-space sampling pattern; or with a non-Cartesian sampling pattern.

12. The medical system of claim 1, wherein the B.sub.0 field estimation module is implemented by at least one of the following: a B.sub.0 modeling machine learning system; a B.sub.0 modeling neural network; a B.sub.0 modeling random forest regression system; a B.sub.0 support vector machine learning system; or a template based B.sub.0 magnetic field predictor system.

13. The medical system of claim 1, wherein the subsequent region of interest is within the first region of interest, wherein the subsequent region of interest has a volume less than or equal to the first region of interest; or the subsequent region of interest has a volume greater than the first region of interest.

14. A computer program comprising machine executable instructions stored in non-transitory computer readable medium for execution by a computational system controlling a medical system, wherein execution of the machine executable instructions causes the computational system to: receive an initial magnetic resonance image descriptive of a first region of interest of a subject, wherein the initial magnetic resonance image comprises a magnitude component; wherein execution of the machine executable instructions further causes the computational system to: receive subsequent k-space data descriptive of a subsequent different region of interest of the subject, wherein the subsequent region of interest at least partially overlaps the first region of interest and wherein the subsequent k-space data includes motion parameters descriptive of motion of the subject during or after the acquisition of the subsequent k-space data; calculate an estimated B.sub.0 field mapping for the subsequent region of interest from the initial magnetic resonance image by inputting the initial magnetic resonance image into the B.sub.0 field estimation module, wherein the B.sub.0 field estimation module is configured to output the estimated B.sub.0 field mapping in response to receiving at least the magnitude component of the initial magnetic resonance image; and reconstruct a corrected magnetic resonance image from the subsequent k-space data and the estimated B.sub.0 field mapping.

15. A method of medical imaging, wherein the method comprises receiving an initial magnetic resonance image descriptive of a first region of interest of a subject, wherein the initial magnetic resonance image comprises a magnitude component; wherein the method comprises: receiving subsequent k-space data descriptive of a subsequent different region of interest of the subject, wherein the subsequent region at least partially overlaps with the first region of interest and wherein the subsequent k-space data includes motion parameters descriptive of motion of the subject during or after the acquisition of the subsequent k-space data; calculating an estimated B.sub.0 field mapping for the subsequent region of interest from the initial magnetic resonance image by inputting the initial magnetic resonance image into a B.sub.0 field estimation module, wherein the B.sub.0 field estimation module configured for outputting the estimated B.sub.0 field mapping in response to receiving at least the magnitude component of the initial magnetic resonance image as input; and reconstructing a corrected magnetic resonance image from the subsequent k-space data and the estimated B.sub.0 field mapping.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In the following preferred embodiments of the invention will be described, by way of example only, and with reference to the drawings in which:

[0063] FIG. 1 illustrates an example of a medical system;

[0064] FIG. 2 shows a flow chart which illustrates a method of operating the medical system of FIG. 1;

[0065] FIG. 3 illustrates a further example of a medical system;

[0066] FIG. 4 shows a flow chart which illustrates a method of operating the medical system of FIG. 3;

[0067] FIG. 5 illustrates a further example of a medical system;

[0068] FIG. 6 shows a flow chart which illustrates a method of operating the medical system of FIG. 5;

[0069] FIG. 7 illustrates a method of medical imaging;

[0070] FIG. 8 illustrates an example of an image reconstruction module;

[0071] FIG. 9 illustrates a method of training a template-based AI system to calculate B.sub.0 inhomogeneity maps; and

[0072] FIG. 10 illustrates the use of the AI system of FIG. 9 to produce a B.sub.0 inhomogeneity map.

DETAILED DESCRIPTION OF THE EMBODIMENTS

[0073] 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.

[0074] FIG. 1 illustrates an example of a medical system 100. The medical system 100 in FIG. 1 is depicted as comprising a computer 102. The computer has an optional hardware interface 104 that may for example enable the computer 102 to communicate or control other components of the medical system 100. The computer 102 is shown as comprising a computational system 106. The computational system 106 may be implemented as a processor or multiple processors and may also be distributed in multiple locations. The computational system 106 may also be a field programmable gate array or other system capable of performing computations. The computer 102 is further shown as comprising an optional user interface 108. The user interface 108 may enable an operator to control the operation and function of the computer 102 and the medical system 100. The computer 102 is further shown as comprising a memory 110. The memory 110 is intended to represent different types of memory 110 that may be accessible to the computational system 106. The computational system 106 is shown as being in communication with the hardware interface 104, the user interface 108, and the memory 110.

[0075] The medical system 100 may take different forms in different examples. In one example the medical system 100 may be a remote server or a cloud computing component. In other examples the medical system 100 may be a workstation computer used by a physician or other medical professional. In other examples the medical system 100 may be integrated into the control system of a medical system 100 that controls a magnetic resonance imaging system.

[0076] The memory 110 is shown as containing machine-executable instructions 120. The machine-executable instructions 120 enable the computational system 106 to provide and perform various computational tasks. For example, this may include basic data processing, image processing, and medical image reconstruction tasks. The memory 110 is shown as containing an initial magnetic resonance image 122 that was received. It could have for example been received by a data carrier or it may have been received via a network or internet connection. The memory 110 is further shown as containing subsequent k-space data 124. The initial magnetic resonance image is acquired for a first region of interest of the subject. The subsequent k-space data is descriptive of a subsequent region of interest of the subject. In this particular example, the subsequent region of interest is within the first region of interest and has a volume that is less than or equal to the first region of interest. In other examples, the subsequent region of interest and the first region of interest may only be at least partially overlapping.

[0077] The memory 110 is further shown as containing a B.sub.0 field estimation module 126 that is configured for taking at least magnitude component of a magnetic resonance image as input. In response it outputs an estimated B.sub.0 field mapping 128 for the subsequent k-space data 124. The memory 110 is further shown as a corrected magnetic resonance image 130 that was reconstructed from the subsequent k-space data 124 using the estimated B.sub.0 field mapping 128. The estimated B.sub.0 field mapping 128 could for example be used for correcting for B.sub.0 inhomogeneities when the subsequent k-space data 124 was acquired.

[0078] FIG. 2 shows a flowchart which illustrates a method of operating the medical system 100 of FIG. 1. First, in step 200, the initial magnetic resonance image 122 is received. The initial magnetic resonance image 122 is descriptive of a first region of interest of a subject. The initial magnetic resonance image is a magnitude image. Next, the method proceeds to step 202. In step 202 the subsequent k-space data 124 is received. Next, in step 204, an estimated B.sub.0 field mapping 128 is calculated by inputting the initial magnetic resonance image 122 into the B.sub.0 field estimation module 126. Then, in step 206, the corrected magnetic resonance image 130 is calculated using the subsequent k-space data 124 and the estimated B.sub.0 field mapping 128.

[0079] FIG. 3 illustrates a further example of the medical system 300. The medical system 300 is similar to the medical system 100 in FIG. 1 except it additionally comprises a magnetic resonance imaging system 302.

[0080] The magnetic resonance imaging system 302 comprises a magnet 304. The magnet 304 may also be referred to as the main magnet. 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.

[0081] 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 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. Within the bore 306 of the magnet 304 is also visible a B.sub.0 magnetic field shim coil 322 that is connected to a B.sub.0 magnetic field shim power supply 324. The hardware interface 104 may be used to control and dynamically change the shimming of the main magnetic field of the magnet 304.

[0082] Within the imaging zone 308 is visible a first region of interest 326. It can be seen as encompassing almost the entire head region of the subject 318. Because it encompasses and images the entire head region it could provide a very good estimate of the B.sub.0 magnetic field inhomogeneities caused by placing the subject 318 within the imaging zone 308. Also, within the imaging zone 308 is visible a subsequent region of interest 328. This region is seen as being very closely around just a portion of the subject's 318 head. This may be used for providing clinical or more detailed magnetic resonance images. However, because only a small region of the head is imaged, it would not accurately produce information about the B.sub.0 inhomogeneities. The regions of the subject 318 outside of the subsequent region of interest 328 would also have the effect of distorting the B.sub.0 or main magnetic field.

[0083] 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 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.

[0084] 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. 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 receivers. 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.

[0085] The transceiver 316, the magnetic field gradient coil power supply 312, and the gradient controller 312 are shown as being connected to the hardware interface 106 of a computer system 102.

[0086] The memory 110 is further shown as containing first pulse sequence commands 330. The memory 110 is further shown as containing initial k-space data 332 that was acquired from the first region of interest 326 by controlling the magnetic resonance imaging system 302 with the first pulse sequence commands 330. The memory 110 is further shown as containing a set of second pulse sequence commands 334. These represent a variety of different magnetic resonance imaging protocols to which k-space data can be acquired. The memory 110 is further shown as containing one or a selection of the second pulse sequence commands 336. This is just one of the set 334. The memory 110 is further shown as containing subsequent k-space data 338 that was acquired for the subsequent region of interest 328 by controlling the magnetic resonance imaging system 302 with the one 336 of the second pulse sequence commands. The process may be repeated for different selections of the set of second pulse sequence commands 334. This may also involve that for each acquisition there is a different subsequent region of interest 328. This for example may enable a very flexible system of estimating the B.sub.0 field mapping 128 for a variety of different acquisitions.

[0087] FIG. 4 shows a flowchart which illustrates a method of operating the medical system 300 of FIG. 3. First, in step 400, the magnetic resonance imaging system 302 is controlled with the first pulse sequence commands 330 to acquire the initial k-space data 332. Next, in step 402, the initial magnetic resonance image 122 is reconstructed from the initial k-space data 332. The method then proceeds to step 200 of FIG. 2. After step 200, step 404 is performed. In step 404 the magnetic resonance imaging system 302 is controlled with the one 336 of the second pulse sequence commands to acquire the subsequent k-space data 338. The method then proceeds through steps 202, 204, and 206 of FIG. 2. Step 406 is a decision box and the question is are there more images to acquire. If the answer is yes then the method proceeds back to step 404. If the answer is no the method proceeds to step 408 where the method ends.

[0088] FIG. 5 illustrates a further example of a medical system 500. The medical system 500 illustrated in FIG. 5 is similar to the medical system 300 in FIG. 3 except that it additionally comprises a camera system 502. The camera system 502 may for example be used for directly measuring motion parameters descriptive of motion of the subject 318 during acquisition of the subsequent k-space data 338. The memory 110 is further shown as containing updated shim settings 504. These updated B.sub.0 shim settings may be configured to reduce the B.sub.0 inhomogeneity by controlling the B.sub.0 magnetic field shim power supply 324. The camera system 502 is a motion sensor system. The memory 110 is further shown as containing an intermediate image 508 that has been reconstructed from the subsequent k-space data 338 possibly without the B.sub.0 corrections and also without motion corrections. The memory 110 is further shown as containing the image registration 510 between the intermediate image 508 and the initial magnetic resonance image 112. This may be used to determine motion parameters 506 which are also shown as being stored in the memory 110. The camera system 502 can also measure the motion parameters 506. In some cases, both the camera 502 and the image registration 510 may both be used for deriving or calculating the motion parameters 506.

[0089] The memory 110 is further shown as optionally containing a system model 512. The system model 512 contains a model of the magnetic resonance imaging system 302 in terms of its electromagnetic properties as a function of time in response to the pulse sequence commands. This can be used for such things as modeling the B.sub.0 inhomogeneities caused by eddy currents, the temperature dependent gradient magnetic field nonlinearities, other time dependent gradient magnetic field nonlinearities, changes in magnetic resonance coil sensitivities, motion dependent B.sub.1 inhomogeneities, static magnetic gradient field nonlinearities and concomitant magnetic field corrections. The memory 110 is further shown as containing time dependent data 514 output by the system model 512 in response to inputting the one 336 of the second pulse sequence commands. This time dependent data 514 may be used in improving the reconstruction of the corrected magnetic resonance image 130.

[0090] FIG. 6 shows a flowchart which illustrates a method of operating the medical system 500 of FIG. 5. Steps 400, 402, and 200, as is illustrated in FIG. 4, are performed first. Next, step 600 is optionally performed. In step 600 an initial B.sub.0 field mapping is estimated from the initial image. For example, if it is desired to shim the B.sub.0 magnetic field the initial B.sub.0 field mapping can be used for the first pass of the algorithm. Next, the method proceeds to step 602. Step 602 is also optional. In step 602 the updated B.sub.0 shim settings are calculated and configured to reduce B.sub.0 inhomogeneity using either the estimated B.sub.0 field mapping or the initial B.sub.0 field mapping.

[0091] Next, optional step 604 is performed. In step 604 the B.sub.0 magnetic field is shimmed by controlling the adjustable B.sub.0 magnetic field shim with the updated B.sub.0 shim settings. After step 606 the method proceeds to step 404 of FIG. 4. Then step 202, as was illustrated in FIG. 4, is also performed. The method then proceeds to step 608. Step 608 is also optional. In step 608 motion parameters 506 are received. The motion parameters are descriptive of motion of the subject 318 during or after acquisition of the subsequent k-space data 338. After step 608, step 204 as was previously described in FIG. 4, is performed. After step 204, optional step 610 is performed. In step 610 the time dependent data 514 is calculated by inputting the one 336 of the second pulse sequence commands into the system model 512. After step 610, step 206 as was described in FIG. 4, is performed. During the reconstruction of the corrected magnetic resonance image 130 the time dependent data 514 may be used if it is present.

[0092] FIG. 7 illustrates one method of medical imaging. In this example a so-called smart survey which corresponds to the initial magnetic resonance image 122 is acquired. This incorporates using a survey scan to calculate the B.sub.0 magnetic field. An artificial intelligence module which corresponds to the B.sub.0 field estimation module 126 is used before each acquisition of the subsequent k-space data 124 to correct and set hardware values such as the shimming of the B.sub.0 field. Images from earlier scans 124 may be used to estimate motion and/or hardware settings. At the end the data from the various acquisitions is used for a reconstruction of the corrected magnetic resonance image 130.

[0093] FIG. 8 illustrates an example of an image reconstruction module 800. The raw k-space data or subsequent k-space data 124 is input into it. First there is a reconstruction that reconstructs an intermediate image 508. This intermediate image 508 is reconstructed without motion correction and also possibly without corrections for the B.sub.0 inhomogeneities. The next step in the module estimates the motion 608. The motion parameters are received, this may be from an external camera system 502 or it may also be received from an image registration 510. This is then input into the next portion of the module which predicts 204 a new B.sub.0 for the new position of the subject. There may also be optionally a system which models the electromagnetic properties of the magnetic resonance imaging system and models hardware imperfections 610. The modeled electromagnetic properties of the magnetic resonance imaging system is the time dependent data 514, which may be calculated by inputting the pulse sequence commands into the system model 512. The module then performs a high-fidelity reconstruction 206 that uses the subsequent k-space data 124, the motion estimation from 608, the new B.sub.0 field from 204 and the hardware imperfections 610.

[0094] FIG. 9 illustrates the training of the artificial intelligence block. In this case the artificial intelligence block uses a machine learning algorithm that is trained by using measured images along with measured B.sub.0 field inhomogeneities 902 to co-register to a template 904. Parameters involving the transformation of the image 900 are trained as well as transformations for the measured B.sub.0 field inhomogeneities 902. The combination of these translations of the image and of the B.sub.0 field inhomogeneities 902 can be used to train a machine language regression algorithm with both these parameters. The training for the measured B.sub.0 field inhomogeneities 902 is done using co-registration parameters and spherical harmonic coefficients.

[0095] FIG. 10 illustrates the calculation of the B.sub.0 field mapping 128 using the system illustrated in FIG. 9. The initial magnetic resonance image 122 is co-registered to a template 904. This results in a transformed initial magnetic resonance image 122. Inputting these transformations into the artificial intelligence block of FIG. 9 enables prediction of the spherical harmonic coefficients and results in the estimated B.sub.0 field mapping 128. The rotations, translations and stretching skew, which were calculated for the initial magnetic resonance image, may be updated with the motion parameters 506 are then used to calculate the correct estimated B.sub.0 field mapping.

[0096] As was mentioned previously magnetic resonance imaging (MRI) scanners use B.sub.0 preparatory scans to improve SNR and also image quality during reconstruction (e.g. reduce image distortion in EPI, MB-SENSE, spirals). However, the preparatory scans can add substantial time to the overall scan duration (up to 10%). Moreover, despite using the B.sub.0 prescan, MRI images sometimes suffer from artifacts, ranging anywhere from being subtle to gross image degradation. Typically, motion and respiration combine with scanner hardware limitations and give rise to the artifacts. Occurrence of artifacts necessitates repeat scans, resulting in reduced scanner throughput and reduced patient comfort. Moreover, it also affects measurement confidence through reduced image quality. This is a widespread problem that affects nearly all MRI systems, and across all application areas.

[0097] An example image acquisition/reconstruction framework, such as depicted in FIG. 6 or 8, may solve this issue in a comprehensive manner by accounting for the fundamental physics behind the problem over a large class of fast MRI scans (EPI, MB-SENSE, spirals etc.). Examples may also lead to direct scan time reduction by eliminating the B.sub.0 preparatory scans needed for MRI exams. AI may be a key enabler in both acquisition and reconstruction stages. Examples may provide the combined benefits of improved image quality and reduced scan time.

[0098] Even in a well calibrated MRI scanner, artifacts affect image quality to varying extents, depending on the scan type and anatomy of interest. The root cause of the artifacts can be subdivided into two major types listed below with their causes: [0099] 1. System imperfections (examples given below) [0100] a. Eddy currents (can be fully modeled both in space and time for all acquisition conditions) [0101] b. Gradient nonlinearities (completely modeled for all conditions) [0102] 2. Human physiology related (examples given below) [0103] a. Magnetic field inhomogeneity (dependent on region of interest) [0104] b. Motion, breathing etc. (not easily characterized, but can be measured using tools like imaging)

[0105] System imperfections can be fully quantified with calibration scans performed once during installation and their impact is typically mitigated either during acquisition and/or during image reconstruction. However, physiology-induced artifacts are hard to predict. Some sources like magnetic field inhomogeneity are mitigated with preparatory-scans (B.sub.0 prescans). But that leads to increased scan time and could lead to significant overheads. However, most attempts try to mitigate artifacts using image processing, or have no solutions at all, requiring a repeat scan. In particular, motion induced artifacts are hard to quantify and correct, especially with dynamic scans which acquire a series of image volumes over time.

[0106] One issue which is often neglected, but important to note here is, all the sources of artifacts interact with each other and cannot be corrected using post-processing. For example, in case of brain scans, head motion changes magnetic field inhomogeneity, and also results in varying effects of eddy-currents and gradient nonlinearity. One way to maintain image fidelity under such conditions is to incorporate all the sources of artifacts into a single image reconstruction framework. This has resulted in a plethora of post-processing options, which try to correct different artifacts in a piece-meal manner, with none being able to provide the same image fidelity as accounting for all the causes concurrently during reconstruction.

[0107] Examples may address two technical issues. A) Eliminate the need for acquiring B.sub.0 prescans using in some examples AI-based predictions. B) Provide true fidelity reconstructions even in case of motion by using the same AI-based prediction strategy to predict residual B.sub.0 inhomogeneity after shimming which would be used as input to the reconstruction algorithm.

[0108] The B.sub.0 prescans that are performed routinely on the scanners before many scans do not directly offer any diagnostic value. But they are currently essential to improve SNR (shimming). They are also used to provide inputs to reconstruction algorithms to improve image quality. However, that comes with an assumption that the patient does not move between the prescan and the main scan. This assumption is often violated in practice, leading to image quality degradation.

[0109] Examples may eliminate the need for performing B.sub.0 prescans and thus lead to direct scan time reduction. This could save 10% or more in terms of overall scan time. Moreover, this may provide for a way to predict B.sub.0 inhomogeneity and thus directly improve image quality for a large class of scans, even in the presence of motion. Thus, examples may provide for a motion-robust, reduced scan time acquisition and reconstruction framework.

[0110] Recent studies have highlighted that the B.sub.0 inhomogeneity in humans is very similar across subjects, irrespective of the anatomy under consideration. The main variability in B.sub.0 inhomogeneity stems from different anatomical shapes and patient positioning inside the scanners. Both these will be captured by the survey scan done (initial magnetic resonance image 122) in every MR examination to aid planning.

[0111] Examples may benefit the very widely used fast imaging scans like echo-planar imaging (EPI) and its variants (e.g., MB-SENSE) and also scans with great clinical potential which have not been widely adopted so far because of their vulnerability to artifacts, for example, spiral imaging. Both types of scans typically capture image volumes within a few seconds.

[0112] Examples also provide for the implementation of a comprehensive image acquisition/reconstruction framework as is shown in FIGS. 6 and 8. This framework may be used to take into account time dependent data 514 such as eddy currents, gradient nonlinearities and magnetic field inhomogeneity. In examples, the reconstruction framework may include motion-related effects (direct and indirect), but also to reduce scan time by eliminating the need of the preparatory scan performed to measure magnetic field inhomogeneity (by determining the estimated B.sub.0 field mapping 128).

[0113] One of the hurdles in developing a comprehensive image reconstruction framework including motion is an assumption underlying current image reconstruction techniques. For example, current EPI and spiral image reconstructions perform a preparatory scan (prescan) to measure magnetic field inhomogeneity, but inherently assume that there is no motion between the preparatory scan and the main scan (EPI/spiral) acquisitions. However, this assumption can be violated, especially in a dynamic scan involving multiple volumes where subject motion typically leads to unexpected artifacts. In contrast to the disclosed examples, the currently used frameworks may typically require repeated preparatory scans to measure magnetic field inhomogeneity which is completely infeasible in dynamic scans, as motion occurs continuously (especially in the torso region).

[0114] Recent research has shown that in brain scans, one can predict the magnetic field inhomogeneity resulting from motion if the initial magnetic field inhomogeneity distribution is known. While this concept has been used to correct EPI images at the post-processing stage, examples may incorporate this concept directly into the reconstruction framework as disclosed herein. Examples may predict magnetic field inhomogeneity using survey scans using AI and thus eliminate the need of acquiring preparatory scans for measuring magnetic field inhomogeneity.

[0115] An example acquisition/reconstruction framework may involve one or more of the following steps: [0116] a) Perform survey scan for planning (e.g. EPI/spiral volume) (receive 200 initial magnetic resonance image 122) [0117] b) Predict magnetic field inhomogeneity from survey scans using AI (calculate an estimated B.sub.0 field mapping 204 or calculate an initial B.sub.0 field mapping 600) [0118] c) Improve the field homogeneity by setting appropriate shim currents (calculate 602 updated shim settings 504 and shim 604 the B.sub.0 magnetic field) and also predict the residual magnetic field inhomogeneity (adjust the estimated B.sub.0 field mapping using the change in the B.sub.0 shim settings 606). This can be done using simple calculations. [0119] d) Perform EPI/Spiral dynamic scan (multiple volumes over time, e.g. DTI, fMRI, bolus tracking etc.) (acquire 404 the subsequent k-space data 124) [0120] e) Reconstruct the first image volume in the series with high fidelity using residual magnetic field obtained from step c and with the knowledge of system imperfections. Note that there may be no time gap between prediction at step c and the acquisition of first volume in the time series. (reconstruct 206 a corrected magnetic resonance image 130) [0121] f) For each subsequent image volume [0122] 1. Perform low quality reconstruction (reconstruct the intermediate image 508) (with the existing framework and not the proposed one) and use image co-registration (registration 510) to the first volume (first region of interest 326) to estimate motion parameters (translations, rotations, skew and stretch). Alternatively, obtain reliable motion estimates from other sources, e.g. from a camera system 502. [0123] 2. Use the motion parameters to predict residual magnetic field inhomogeneity using AI (calculate 128 the estimated B.sub.0 field mapping 128 using the B.sub.0 field estimation module 126) [0124] 3. Use the motion parameters 506 and the predicted residual magnetic field inhomogeneity along with updated system imperfections (to account for motion) in the comprehensive reconstruction framework to obtain high fidelity reconstructions (the corrected magnetic resonance image 130).

[0125] Some examples may provide for a B.sub.0 prescan prediction (estimated B.sub.0 field mapping 128) from survey scan and there by its elimination from actual scanning can be used irrespective of the scanner (field strength and variant), scan type and potentially anatomy as well (although our initial tests have been restricted to brain scans). This directly leads to scan time reduction by pre-scan elimination.

[0126] Other examples may provide for incorporating the residual B.sub.0 in image reconstruction applies to all the fields strength and variants and a large class of MRI scans, both currently in the field and those being newly developed. e.g. EPI distortion correction, SENSE, MB-SENSE, CSENSE etc., which are all already in the field can benefit from the proposed comprehensive reconstruction framework, as long as the acquisition volume can be acquired in a few second (<10 s), which they typically do.

[0127] 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.

[0128] 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

[0129] 100 medical system [0130] 102 computer [0131] 104 hardware interface [0132] 106 computational system [0133] 108 user interface [0134] 110 memory [0135] 120 machine executable instructions [0136] 122 initial magnetic resonance image [0137] 124 subsequent k-space data [0138] 126 B.sub.0 field estimation module [0139] 128 estimated B.sub.0 field mapping [0140] 130 corrected magnetic resonance image [0141] 200 receive an initial magnetic resonance image descriptive of a first region of interest of a subject, wherein the initial magnetic resonance image is a magnitude image [0142] 202 receive subsequent k-space data descriptive of a subsequent region of interest of the subject, wherein the subsequent region of interest is within the first region of interest [0143] 204 calculate an estimated B.sub.0 field mapping for the subsequent region of interest from the initial magnetic resonance image by inputting the initial magnetic resonance image into the B.sub.0 field estimation module [0144] 206 reconstruct a corrected magnetic resonance image from the subsequent k-space data and the estimated B.sub.0 field mapping [0145] 300 medical system [0146] 302 magnetic resonance imaging system [0147] 304 main magnet [0148] 306 bore of magnet [0149] 308 imaging zone [0150] 310 magnetic field gradient coils [0151] 312 magnetic field gradient coil power supply [0152] 314 radio-frequency coil [0153] 316 transceiver [0154] 318 subject [0155] 320 subject support [0156] 322 B.sub.0 magnetic field shim coils [0157] 324 B.sub.0 magnetic field shim power supply [0158] 326 first region of interest [0159] 328 subsequent region of interest [0160] 330 first pulse sequence commands [0161] 332 initial k-space data [0162] 334 set of second pulse sequence commands [0163] 336 one of the second pulse sequence commands [0164] 338 subsequent k-space data [0165] 400 control the magnetic resonance imaging system with the first pulse sequence commands to acquire the initial k-space data [0166] 402 reconstruct the initial magnetic resonance image from the initial k-space data [0167] 404 control the magnetic resonance imaging system with one of the set of second pulse sequence commands to acquire the subsequent k-space data for each iteration [0168] 500 medical system [0169] 502 camera system [0170] 504 updated shim settings [0171] 506 motion parameters [0172] 508 intermediate image [0173] 510 registration [0174] 512 system model [0175] 514 time dependent data [0176] 600 calculate an initial B.sub.0 field mapping from the initial magnetic resonance image [0177] 602 calculate updated B.sub.0 shim settings configured to reduce B.sub.0 inhomogeneity using the estimated B.sub.0 field mapping [0178] 604 shim the B.sub.0 magnetic field by controlling the adjustable B.sub.0 magnetic field shim with the updated B.sub.0 shim settings [0179] 606 use the change in B.sub.0 shim settings to adjust the estimated B.sub.0 field mapping [0180] 608 receive motion parameters descriptive of motion of the subject during or after acquisition of subsequent k-space data [0181] 610 calculate time dependent data [0182] 800 reconstruction module [0183] 900 measured image [0184] 902 measured B.sub.0 field inhomogeneities [0185] 904 template