WO2015111048A1 - Procédé et système d'accélération d'imagerie à résonance magnétique répétée - Google Patents

Procédé et système d'accélération d'imagerie à résonance magnétique répétée Download PDF

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WO2015111048A1
WO2015111048A1 PCT/IL2015/050071 IL2015050071W WO2015111048A1 WO 2015111048 A1 WO2015111048 A1 WO 2015111048A1 IL 2015050071 W IL2015050071 W IL 2015050071W WO 2015111048 A1 WO2015111048 A1 WO 2015111048A1
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space
partial
image
magnetic resonance
data
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Leo Joskowicz
Lior Weizman
Dafna BEN BASHAT
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Yissum Research Development Co of Hebrew University of Jerusalem
Medical Research Infrastructure and Health Services Fund of the Tel Aviv Medical Center
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Yissum Research Development Co of Hebrew University of Jerusalem
Medical Research Infrastructure and Health Services Fund of the Tel Aviv Medical Center
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R33/00—Arrangements or instruments for measuring magnetic variables
    • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
    • G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
    • G01R33/48—NMR imaging systems
    • G01R33/54—Signal processing systems, e.g. using pulse sequences ; Generation or control of pulse sequences; Operator console
    • G01R33/56—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution
    • G01R33/561—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution by reduction of the scanning time, i.e. fast acquiring systems, e.g. using echo-planar pulse sequences
    • G01R33/5619—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution by reduction of the scanning time, i.e. fast acquiring systems, e.g. using echo-planar pulse sequences by temporal sharing of data, e.g. keyhole, block regional interpolation scheme for k-Space [BRISK]

Definitions

  • the present disclosure relates generally to a method of and a system for Magnetic Resonance (MR) imaging. More particularly, the present disclosure relates to a method of and a system for accelerating MR imaging using a preliminarily acquired MR baseline image.
  • MR Magnetic Resonance
  • PICCS Principal image constrained compressed sensing
  • the present invention relates generally to medical imaging, and more particularly to acquisition and reconstruction of MR images.
  • Magnetic Resonance Imaging has become a well-established medical diagnostic tool for imaging structures within the body of a patient.
  • Image quality may be characterized by a host of parameters, including resolution, signal to noise, field of view, contrast, edge definition, and artifacts (for example, ghosts and streaks).
  • image quality improves with increasing data acquisition time. If the data acquisition time is increased, however, the patient is subjected to a longer scan time, which increases patient discomfort. In some instances, long scan times may actually degrade image quality because of movement of the region of interest during the scan. Short scan times are also necessary for near-real-time measurements, such as used in functional MRI. There is, thus, a fundamental trade-off between image quality and scan time.
  • Images are displayed on physical media; for example, emulsions on film or pixels on a monitor.
  • the normal physical world may be referred to as real space.
  • MR signals are captured in k-space.
  • k-space is also referred to as spatial-frequency domain.
  • data values in real space are then generated by taking the inverse Fourier transform of data values in k-space.
  • MR signals are not measured as a continuous function of position in k-space. They are sampled at discrete k-values. Subject to specific constraints and boundary conditions, image quality generally improves as the density and range of discrete k-space sampling points are increased. Recording a large number of samples, however, has disadvantages. One is the extended scan time discussed above. The other is low temporal resolution.
  • the acquisition of a magnetic resonance imaging of an object may involve existing data of the same object acquired at an earlier time point.
  • k-space or k-space data refer to data collected from the magnetic resonance signals acquired by a MRI system.
  • spatial encoding may be performed by a phase encoding and frequency encoding which enable to identify a site of origin of an MRI signal collected.
  • the k-space may have two axes with the horizontal axis (K x ) representing the frequency information and the vertical axis (K y ) representing the phase information and as such, the k-space maps the collected signal.
  • the k-space may be represented as a graphic matrix of digitized MR data which represents the MR image before Fourier transformation is performed.
  • An MR image may be created from the k- space data by application of a two-dimensional Fourier transformation.
  • Figs. 7A-7B provide an illustration of a MR image of an orange slice and corresponding k-space data collected. It is noted that 3D MRI as obtained through parallel imaging techniques may also be encompassed by the present disclosure.
  • Collecting the MR signal may comprise scanning the object while acquiring local values of the MR.
  • the collecting of the MR signal may therefore correspond to a sampling of the k-space according to a sampling pattern.
  • the sampling pattern may be defined by a trajectory type and a trajectory density. It is noted that a sampling point density defining how the sampling points are acquired along the sampling trajectories for each trajectory may be constant for a given MRI scan.
  • the trajectory type may define a shape of the sampling trajectories, for example, the shape of the trajectories may be selected from any of: a row, a column, a spiral, a radial line, blades, etc.
  • the trajectory density may define how the sampling trajectories are distributed over the object.
  • the sampling trajectories may be lines or curves along which the scanning is performed. .
  • sampling of the k-space may be performed with different methods.
  • sampling of the k-space may be performed with a linear trajectory type i.e. by scanning along linear trajectories (scanning lines).
  • a trajectory density of the scanning lines may vary within the scanned field of view.
  • a density of the scanning lines may vary from one sampling to the other.
  • the scan illustrated on Fig. 6A may have a lower density of scanning trajectories than the scans illustrated on Figs. 6B-6C.
  • the k-space may also be scanned (sampled) along spiral trajectories or along radial trajectories as illustrated on Figs. 6E-6F.
  • a prior MR baseline image (also referred to as existing data acquired at an earlier time point) may result from a k-space sampling including a higher amount of sampling points than further partial k-space data.
  • the prior baseline k-space and the later partial k-space may result from sampling patterns having an identical or different trajectory type and/or different trajectory density and/or sampling point density.
  • the prior baseline k-space sampling pattern and the later partial k-space sampling pattern may be of different trajectory types.
  • the prior baseline k-space sampling pattern and the later partial k-space sampling pattern may be independent.
  • the prior MR baseline image may result from a full sampling of the k-space also referred to as "full k-space".
  • the term "full k-space" or full sampling of the k-space may refer to a sampling which satisfies a Nyquist criterion.
  • a partial k-space sampling may refer to a sampling with less sampling points than a full k-space sampling.
  • the partial k-space may be a partial scanning of the object for example obtained by incompletely scanning the sampling trajectories of the baseline sampling pattern. For example, if the baseline k-space is scanned along rows, a partial sampling may constitute selecting certain rows of the entire k-space.
  • the prior baseline sampling pattern and the later partial sampling pattern may have different trajectory types.
  • the present disclosure provides a method of magnetic resonance imaging based on a prior magnetic resonance baseline image of an object.
  • the method comprises: partially sampling a magnetic resonance signal originating from said object so as to collect partial k-space data; consolidating the partial k-space data with the k-space data of the baseline image to obtain consolidated k-space data; and creating an approximate image of the object using the consolidated k-space data.
  • partially sampling the magnetic resonance signal comprises acquiring an initial partial k-space and automatically refining the initial partial k-space by adding additional sampling points so as to obtain a refined partial k- space.
  • the method further comprises automatically refining the refined partial k-space by adding additional sampling points to the refined partial k- space.
  • automatically refining the initial partial k-space or the refined partial k-space is performed based on detecting changes of the object.
  • detecting changes of the object comprises detecting k- space discrepancies between the k-space data of the baseline image and the initial partial k-space or the refined partial k-space.
  • adding sampling points to the initial partial k-space or to the refined partial k-space comprises adding sampling points in the vicinity of the detected k-space discrepancies.
  • detecting changes of the object comprises detecting image discrepancies between an intermediate image derived from the initial partial k- space or from to the refined partial k-space and the baseline image.
  • the method further comprises registering the intermediate image and the baseline image.
  • adding additional sampling points to the partial k-space or to the refined partial k-space comprises adding additional sampling points so as to image a vicinity of said detected image discrepancies.
  • insignificant k-space discrepancies or insignificant image discrepancies are discarded.
  • automatically refining the refined partial k-space is performed iteratively as long as additional sampling points cause changes of the object to be detected.
  • automatically refining the refined partial k-space is iterated until the refined partial k-space reaches a predetermined amount of sampling points.
  • automatically improving the initial partial k-space or the refined partial k-space comprises updating k-space sampling trajectories.
  • consolidating the partial k-space data with the k-space data of the baseline image comprises fully completing the partial k-space data with the k-space data of the baseline image.
  • the k-space data of the baseline image is a full k-space sampled to satisfy the Nyquist criterion.
  • creating the approximate image is based on compressed sensing.
  • the compressed sensing reconstruction is based on sparsity in the difference between the partial k-space data and the baseline k-space data.
  • the present disclosure further provides a method of resonance magnetic scanning of a three dimensional object based on a prior magnetic resonance baseline scan comprising a set of baseline slice images preliminarily acquired at different depths of the three dimensional object, the method comprising: defining a subset of depths belonging to a volume of lesser interest of the three dimensional object; creating approximate slice images for said subset of depths according to the method of magnetic resonance imaging as previously described.
  • the present disclosure further provides an image reconstruction module for a magnetic resonance imaging system, the image reconstruction module being configured for cooperating with the magnetic resonance system so as to perform the method according to any of the preceding claims.
  • the present disclosure further provides a magnetic resonance imaging system adapted to perform the method according to any of the preceding claims.
  • the present disclosure further provides an image reconstruction module for a magnetic resonance imaging system, the image reconstruction module being configured for cooperating with a magnetic resonance imaging system and comprising: a data acquisition server configured to obtain a prior magnetic resonance baseline image of an object and partial k-space data from partially sampling a magnetic resonance signal originating from said object; and a data processing server configured to consolidate the partial k-space data with the k-space data of the baseline image to obtain consolidated k-space data; and creating an approximate image of the object using the consolidated k- space data.
  • a data acquisition server configured to obtain a prior magnetic resonance baseline image of an object and partial k-space data from partially sampling a magnetic resonance signal originating from said object
  • a data processing server configured to consolidate the partial k-space data with the k-space data of the baseline image to obtain consolidated k-space data; and creating an approximate image of the object using the consolidated k- space data.
  • partially sampling the magnetic resonance signal comprises acquiring an initial partial k-space and the data acquisition server is further configured to enable the magnetic resonance system to perform automatically refining the initial partial k-space by adding additional sampling points so as to obtain a refined partial k-space.
  • the data processing server is configured to detect changes of the object and the data acquisition server is configured to automatically refine the initial partial k-space based on the detected changes.
  • the present disclosure further provides a magnetic resonance imaging system comprising the image reconstruction module as previously described.
  • the present disclosure further provides a computer program product implemented on a non-transitory computer usable medium having computer readable program code embodied therein to cause the computer to perform a method of magnetic resonance imaging based on a prior magnetic resonance baseline image of an object, the method comprising: obtaining partial k-space data from partially sampling a magnetic resonance signal originating from said object; consolidating the partial k-space data with the k-space data of the baseline image to obtain consolidated k-space data; and creating an approximate image of the object using the consolidated k-space data.
  • the method may comprise acquiring magnetic resonance datasets in the k-space, where the data sampling in the k-space domain is controlled during acquisition, based on comparison with the existing MRI of the object, in real- time mode.
  • a method for generating a reconstructed image of an object from image data obtained with an MRI system comprising a processor receiving the image data obtained with the medical imaging system by k-space sampling in trajectories computed in real-time, the processor compares the partially acquired image to an earlier time -point image of the same object and decides upon the optimal sampling of the k-space.
  • an MR image of the object is obtained with sub-sampled k-space data (thus reducing the time required for acquisition), based on the previous time -point MR image of the object.
  • the datasets acquired in full k-space (baseline k-spaces) and the data acquired with any under-sampling technique (partial k-spaces), may not represent the same physical contrast of the object (i.e. may relate to different MR sequences for example the Tl weighted sequence or the T2 weighted sequence).
  • the combination may be provided with a different image contrast
  • Embodiments of the present disclosure have the advantage that the quality of MR data is improved allowing for higher spatial resolution while keeping the data acquisition time and data processing time low. This permits an improved diagnostic quality of, for example, tumor detection.
  • the present disclosure can for example be used to facilitate accelerated and high quality brain cancer diagnosis and grading.
  • the datasets are acquired in k-space, where an undersampling technique is designed in real time based on the comparison between the image being acquired and the early time-point image. This permits reduced scanning time, but still guarantees high image quality.
  • an object's regions of interest are detected in real-time scanning and only these regions are scanned, where regions outside the ROIs are taken from the early time -point existing scan of the object.
  • sub-sampled data is reconstructed with compressed sensing, under the assumption of sparsity in the image domain, in the difference between the current object being scanned and the same object scanned at an earlier time point (temporal sparsity).
  • additional sparsifying transforms such as Wavelet or Discrete Cosine Transform (DCT) are used in conjunction with the temporal sparsifying transform during the reconstruction.
  • DCT Discrete Cosine Transform
  • the present disclosure relates to a computer program product comprising computer executable instructions to perform any of the method steps described above.
  • the present disclosure relates to a magnetic resonance imaging apparatus for performing fast magnetic resonance imaging of an object, where an additional scan of the object from an earlier time point serves as an input.
  • the apparatus comprises a magnetic resonance imaging scanner for acquiring magnetic resonance image data, a controller adapted for controlling a scanner operation of acquiring magnetic resonance datasets in the k-space domain, wherein the controller is further adapted to perform the dataset acquisition employing undersampling, a control system adapted for applying trajectory changes in the k-space domain in real-time and a data reconstruction system which is further adapted to support the integration of existing prior information about the object being scanned from its previous scan.
  • Fig. 1 is a flowchart illustrating schematically steps of a method of magnetic resonance imaging according to embodiments of the present disclosure.
  • Fig. 2 is a flow chart illustrating schematically steps of automatically improving the partial sampling according to embodiments of the present disclosure.
  • Fig. 3 is a flowchart illustrating steps of an exemplary image reconstruction method utilizing an existing prior scan of the object according to embodiments of the present disclosure.
  • Figs. 4A-4B are pictorial representations of an exemplary MRI of subject, acquired respectively at an early time point and after two months.
  • Fig. 5 is a block diagram of an exemplary magnetic resonance imaging ("MRI") system that employs some embodiments of the present invention.
  • MRI magnetic resonance imaging
  • Figs. 6A-6F already described, illustrate different k-space sampling patterns according to embodiments of the present disclosure.
  • Figs. 7A-7B illustrate respectively a resonance magnetic image of an object and corresponding collected k-space data.
  • Figs. 8A-8C represent respectively a partial k-space, an intermediate image derived from said partial k-space and an differential image representing image discrepancies between the intermediate image and a baseline image in an example of the present disclosure.
  • Described herein are some examples of systems and methods useful for accelerating resonance magnetic imaging of an object, wherein a prior resonance magnetic baseline image of the object is available.
  • the term "prior RM baseline image” may refer to an RM image of the same object acquired at an earlier time point.
  • the object may be a human or animal body portion.
  • the earlier baseline image and later image of the object may be such that significant medical changes may have occurred in the object i.e. a time difference between the acquisition of the baseline image and the further image may be in the scale of one or more days.
  • object may refer to a sliced object or to a three-dimensional object. It is further noted that the present disclosure may be similarly applied to parallel acquisition (3D MRI) of a plurality of k- spaces corresponding to a plurality of slices of the object.
  • 3D MRI parallel acquisition
  • a system and method for fast MRI acquisition and reconstruction is provided.
  • raw k-space data may be partially acquired, thus reducing the time required for acquisition.
  • the remainder of missing k-space data may be filled in by utilizing existing MRI data of the same object from a previous scan, and the image may be reconstructed.
  • the acquisition process may be monitored and updated in real-time. The monitoring and updating may be so as to acquire only necessary data required for repeated image reconstruction when the prior image is available.
  • Fig. 1 illustrates generally a method of magnetic resonance imaging using a prior magnetic resonance baseline image of an object according to embodiments of the present disclosure.
  • sampling partially a MR signal originating from the object may be performed.
  • the object may be a sliced object or a given slice of a three dimensional object.
  • the partial sampling may be performed for example using a MRI system, for example as described in Fig. 5.
  • the implementation of the method may be controlled by an image reconstruction module implemented on a data acquisition server and a data processing server of the MRI system.
  • the partial sampling step may be commanded by the data acquisition server of the MRI system.
  • the baseline image may be associated with k-space data including a higher amount of sampling points than the partial k-space acquired in S100.
  • the baseline k-space data may be a full k- space satisfying a Nyquist criterion.
  • the baseline k-space data may be acquired according to a baseline sampling pattern.
  • the partial k-space data may be acquired with a similar sampling pattern including fewer sampling points.
  • the consolidating of the partial k-space may be performed for example using the data processing server.
  • the partial k-space data may be partially or fully completed using the baseline k-space data. In other words, missing sampling values of the acquired partial k-space may be filled by preliminarily acquired baseline k-space data.
  • an approximate image may be created using the consolidated k- space data.
  • the approximate image may be obtained by Fourier transformation of the consolidated k-space data.
  • the creation of the approximate image may be performed for example using the data processing server of the MRI system as illustrated on Fig. 5.
  • Fig. 2 illustrates optional additional steps of automatically improving the partial sampling by selectively adding sampling points to the partial sampling acquired in step S100.
  • These optional additional steps may be performed using the image reconstruction module implemented on the data processing server and the data acquisition server of the MRI system as illustrated on Fig. 5.
  • These optional steps may also be performed using a dedicated module implemented on a dedicated hardware.
  • the image reconstruction module may be configured to cooperate with the MRI system so as to be capable of commanding the data processing server and the data acquisition server of the MRI system to perform an initial partial sampling step and/or a further refining of the initial partial sampling.
  • steps S101 in which an initial partial sampling is acquired, in a step S110, changes of the object may be detected.
  • changes of the object may be detected in the k-space, for example by comparing the acquired values of the initial partial k-space with the baseline k-space to detected discrepancies between the collected partial k-space and the baseline k-space.
  • the detected k-space discrepancies may provide k-space positions for which the initial partial k-space differs from the baseline k-space.
  • changes may be detected in the image space.
  • change detection in the image space may comprise: creating an intermediate image from the initial partial k-space and detecting discrepancies between the intermediate image and the baseline image, for example by comparing pixel intensities of the intermediate image with corresponding pixel intensities in the baseline image.
  • the detected image discrepancies may provide pixel positions for which the intermediate image differs from the baseline image.
  • Fig. 8A illustrates a partial k-space acquired by a vertical scanning of central rows and
  • Fig. 8B illustrates an intermediate image obtained by a Fourier transformation of the partial k-space of Fig. 8A.
  • Fig. 8C illustrates detected image discrepancies between the intermediate image of Fig. 8B and a baseline image of the same object (not shown).
  • insignificant k-space discrepancies (in the first variant) or insignificant image discrepancies (in the second variant) may be discarded. In some embodiments, certain discrepancies may be discarded as related to a movement of the object.
  • discrepancies may be discarded based on a threshold defining a minimal extension either in the k- space or in the image space.
  • additional sampling points may be added to the initial partial k-space so as to obtain a refined partial k-space.
  • the additional sampling points may be acquired according to the sampling pattern used for acquiring the initial partial k-space data in step S100.
  • the additional sampling points may be acquired according to additional trajectories determined based on the detected discrepancies. In other words, the additional sampling points may be acquired by sampling additional trajectories, wherein the additional trajectories follow the trajectory type of the partial sampling pattern.
  • the additional sampling trajectories may be selected so as to acquire specific sampling points based on the detected discrepancies.
  • the additional sampling points can be acquired in a spiral pattern whose origin is the center of the k-space, by alternating horizontal lines across the k-space, or on the bottom half of the k-space. It is noted that the additional trajectories are determined in real-time i.e. during the scanning (imaging) of the object.
  • the additional sampling points may be acquired in the vicinity of the k-space discrepancies.
  • the additional trajectories may be selected so as to acquire additional sampling points in the vicinity of the k-space discrepancies.
  • the additional sampling points may be acquired so as to image a vicinity of the detected image discrepancies.
  • the additional trajectories may be selected so as to acquire additional sampling points imaging the vicinity of the detected image discrepancies.
  • These additional sampling points may be determined by inverse Fourier transformation of the detected image discrepancies.
  • the vicinity of the k-space discrepancies may be defined as a predetermined radius in the k-space, for example said radius may be of about 5 to 10 per mm.
  • the vicinity of the image discrepancies may be defined as a predetermined radius in the image space, for example said radius may be of about 1 to 3 pixels.
  • the automatic improving (refining) of the partial sampling may be iterative. The automatic improving of the partial sampling may be performed for example until changes are not detected or until a certain amount of sampling points have been acquired.
  • FIG. 3 a flow diagram representing a technique for acquisition and reconstruction of an MR image according to an embodiment of the invention is shown.
  • the technique begins at block 100, where partial image data may be acquired. Since in modern MRI the image is sampled in the k-space domain, the partial data is actually undersampled k-space data. On account of the well-known characteristics of the k- space, a full scale image can be reconstructed from only a few samples of the k-space, as shown in block 102. This would probably be a low-resolution image due to the partial data in the k-space domain. Then, an image quality check may be performed in block 104.
  • the existing prior scan of the object is used to identify the location of the next set of the samples in the k-space, as follows.
  • the image produced in block 102 may be spatially registered with the existing prior image at block 106.
  • rigid registration techniques may be sufficient for this purpose, while in body MRI non-rigid registration techniques may be used.
  • the registered, low-quality current image may be compared to the prior image of the object at block 108. This comparison may result in a priority map of regions to be scanned, where regions with high differences in comparison with the prior image may be given high priority.
  • This priority map can be generated in the image domain, in the k-space domain, or in any other domain (e.g. wavelet domain).
  • the k-space sampling trajectories may be updated at block 110, such that the next set of k-space samples are acquired to reduce the difference in prioritized regions. This process may be repeated and additional k-space samples may be acquired until satisfactory results are obtained at block 104. Finally, the image may be stored in block 112.
  • the method of image acquisition and reconstruction in Fig. 3 may have many advantages over previous work in the field.
  • One major advantage may be the update of the k-space trajectories during real time acquisition.
  • k-space sampling trajectories may be a-priori defined, and the scanner may be somewhat "blind" to the object currently being scanned.
  • the k-space trajectories may be continuously updated during scanning, to acquire only necessary data for optimal results. Indeed, in order to follow up on tumor development, it is generally only necessary to image areas which show changes, while major portions of the body part imaged remain in fact unchanged.
  • the most important information is the difference between the repeated scan and the prior (baseline) scan, rather than the repeated scan as a standalone scan. This difference is what the acquisition and reconstruction techniques of the present disclosure cover.
  • FIGs. 4A-4B two MR images of the same brain slice of a patient, acquired with a two month gap, are presented. These images illustrate the similarity between recurrent images of the same patient. It can be seen that despite very small differences between the later image (FIG 4B) and the earlier one (FIG 4B), the two images are very similar. This similarity allows the high quality reconstruction of the later image from partial acquisition of its k-space, and constitutes the basic premise of the technique presented in the present disclosure.
  • the method of image reconstruction illustrated in Figs. 1-3 can be implemented with an exemplary magnetic resonance imaging ("MRI") system configured to implement this image reconstruction method.
  • MRI magnetic resonance imaging
  • the MRI system 300 may include a Workstation 302 having a display 304 and a keyboard 306.
  • the Workstation 302 may include a processor 308, such as a commercially available programmable machine running a commercially available operating system.
  • the Workstation 302 may provide the operator interface that enables scan prescriptions to be entered into the MRI system 300.
  • the Workstation 302 may be coupled to four servers: a pulse sequence server 310; a data acquisition server 312; a data processing server 314, and a data store server 316.
  • the method of magnetic resonance imaging based on a prior magnetic resonance baseline image may be performed by an image reconstruction module 315 implemented on the data acquisition server 312 and on the data processing server 314 of the MRI system.
  • the image reconstruction module 315 may be configured to implement the method of magnetic resonance imaging based on a prior magnetic resonance baseline image as previously described.
  • the data processing server 314 may be configured to perform the consolidating step (b), the creating step (c) as well as the processing steps related to the refining of the initial partial sampling.
  • the data acquisition server 312 may be configured to command the MRI system to perform the steps related to the scanning of the object.
  • the workstation 302 and each server 310, 312, 314 and 316 may be connected to communicate with each other.
  • the pulse sequence server 310 may be configured to function in response to instructions downloaded from the workstation 302 to operate a gradient system 318 and a radiofrequency ("RF") system 320.
  • Gradient waveforms necessary to perform the prescribed scan may be produced and applied to the gradient system 318, which excites gradient coils in an assembly 322 to produce the magnetic field gradients G x , G y , and G z used for position encoding MR signals.
  • the gradient coil assembly 322 may form part of a magnet assembly 324 that includes a polarizing magnet 326 and a whole-body RF coil 328.
  • RF excitation waveforms may be applied to the RF coil 328, or a separate local coil (not shown in Fig. 5), by the RF system 320 to perform the prescribed magnetic resonance pulse sequence.
  • Responsive MR signals detected by the RF coil 328, or a separate local coil (not shown in Fig. 5) may be received by the RF system 320, amplified, demodulated, filtered, and digitized under the direction of commands produced by the pulse sequence server 310.
  • the RF system 320 may include an RF transmitter configured for producing a wide variety of RF pulses used in MR pulse sequences.
  • the RF transmitter may be configured for being responsive to the scan prescription and direction from the pulse sequence server 310 to produce RF pulses of the desired frequency, phase, and pulse amplitude waveform.
  • the generated RF pulses may be applied to the whole body RF coil 328 or to one or more local coils or coil arrays (not shown in FIG. 3).
  • the pulse sequence server 310 may also be optionally configured to receive patient data from a physiological acquisition controller 330.
  • the controller 330 may be configured to receive signals from a number of different sensors connected to the patient, such as electro cardiograph ("ECG") signals from electrodes, or respiratory signals from bellows or another respiratory monitoring device. Such signals may typically be used by the pulse sequence server 310 to synchronize, or "gate,” the performance of the scan with the subject's heart beat or respiration.
  • ECG electro cardiograph
  • the pulse sequence server 310 may also be configured to connect to a scan room interface circuit 332.
  • the scan room interface circuit 332 may be configured to receive signals from various sensors associated with the condition of the patient and the magnet system. It is also through the scan room interface circuit 332 that a patient positioning system 334 may be configured to receive commands to move the patient to desired positions during the scan.
  • the digitized MR signal samples produced by the RF system 320 may be received by the data acquisition server 312.
  • the data acquisition server 312 may be configured to operate in response to instructions downloaded from the workstation 302 to receive the real-time MR data and provide buffer storage, such that no data is lost by data overrun. In some scans, the data acquisition server 312 may transfer the acquired MR data to the data processor server 314. In scans that require information derived from acquired MR data to control the further performance of the scan, the data acquisition server 312 may be programmed to produce such information and convey it to the pulse sequence server 310. For example, during pre-scans, MR data may be acquired and used to calibrate the pulse sequence performed by the pulse sequence server 310.
  • navigator signals may be acquired during a scan and used to adjust the operating parameters of the RF system 320 or the gradient system 318, or to control the view order in which k-space is sampled.
  • the data acquisition server 312 may also be employed to process MR signals used to detect the arrival of a contrast agent in a magnetic resonance angiography ("MRA") scan. In all these examples, the data acquisition server 312 acquires MR data and processes it in real time to produce information that is used to control the scan.
  • MRA magnetic resonance angiography
  • the data processing server 314 may be configured to receive MR data from the data acquisition server 312 and processes it in accordance with instructions downloaded from the workstation 302. Such processing may include, for example: Fourier transformation of raw k-space MR data to produce two or three-dimensional images; the application of filters to a reconstructed image; the performance of a back projection image reconstruction of acquired MR data; the generation of functional MR images; and the calculation of motion or flow images. Images reconstructed by the data processing server 314 may be conveyed back to the workstation 302 where they may be stored. Real-time images may be stored in a data base memory cache (not shown in Fig.
  • the Workstation 302 may be used by an operator to archive the images, produce films, or send the images via a network to other facilities.
  • the present invention encompasses parallel magnetic resonance imaging methods (3D MRI) enabling to acquire a plurality of k-spaces corresponding to a plurality of slices of the object.
  • 3D MRI parallel magnetic resonance imaging methods

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  • Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Radiology & Medical Imaging (AREA)
  • Engineering & Computer Science (AREA)
  • Signal Processing (AREA)
  • High Energy & Nuclear Physics (AREA)
  • Condensed Matter Physics & Semiconductors (AREA)
  • General Physics & Mathematics (AREA)
  • Magnetic Resonance Imaging Apparatus (AREA)

Abstract

La présente invention porte sur un procédé d'imagerie à résonance magnétique (RM) basée sur une image de ligne de base de résonance magnétique antérieure d'un objet, le procédé comprenant : échantillonner partiellement un signal de résonance magnétique provenant dudit objet de manière à collecter des données d'espace k partielles ; consolider les données d'espace k partielles à l'aide des données d'espace k de l'image de ligne de base pour obtenir des données d'espace k consolidées ; et créer une image approximative de l'objet à l'aide des données d'espace k consolidées. Le procédé comprend un matériel unique, un module de commande en temps réel dans le système RM.
PCT/IL2015/050071 2014-01-23 2015-01-21 Procédé et système d'accélération d'imagerie à résonance magnétique répétée Ceased WO2015111048A1 (fr)

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WO2019220213A1 (fr) * 2018-05-18 2019-11-21 Insightec, Ltd. Échantillonnage adaptatif de l'espace des k au cours d'une thérapie non invasive guidée par rm
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CN109738840B (zh) * 2018-12-29 2022-06-14 佛山瑞加图医疗科技有限公司 一种磁共振成像系统和方法
CN113256749A (zh) * 2021-04-20 2021-08-13 南昌大学 一种基于高维相关性先验信息的快速磁共振成像重建算法
CN113256749B (zh) * 2021-04-20 2022-12-06 南昌大学 一种基于高维相关性先验信息的快速磁共振成像重建算法

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