Fat suppression for time-of-flight magnetic resonance angiography with phase-sensitive water-fat separation

Abstract

Phase-sensitive water-fat separation magnetic resonance imaging (MRI) includes acquiring k-space data with an MRI system. The k-space data are acquired at echo times (TEs) at which water spins and fat spins are at least partially out-of-phase with each other. One or more TE images are reconstructed from the k-space data. A background phase is estimated from the TE image(s). One or more phase-corrected images are generated by performing phase correction on the TE image(s) using the background phase. Separated water images and fat images are generated from the phase-corrected image(s). At least one set of the water images or the fat images are then output.

Description

FAT SUPPRESSION FOR TIME-OF-FLIGHT MAGNETIC RESONANCE ANGIOGRAPHY WITH PHASE-SENSITIVE WATER-FAT SEPARATIONCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Patent Application Serial No. 63 / 636,061, filed on April 18, 2024, and entitled ‘TAT SUPPRESSION FOR TIME-OF- FLIGHT MAGNETIC RESONANCE ANGIOGRAPHY WITH PHASE-SENSITIVE WATER-FAT SEPARATION,” and of U.S. Provisional Patent Application Serial No. 63 / 643,253, filed on May 6, 2024, and entitled “FAT SUPPRESSION FOR TIME-OF- FLIGHT MAGNETIC RESONANCE ANGIOGRAPHY WITH PHASE-SENSITIVE WATER-FAT SEPARATION,” both of which are herein incorporated by reference in their entirety.BACKGROUND

[0002] Angiography techniques, such as digital subtraction angiography (DSA), computed tomography angiography (CTA), and magnetic resonance angiography (MRA), play a crucial role in the diagnosis of vascular diseases. A significant advantage of MRA compared to DSA and CTA is the lack of radiation and lack of the need for injection of iodine contrast. MRA can be classified into contrast-enhanced (CE) MRA or non-CE MRA depending on whether gadolinium-based contrast is used. Although gadolinium-based contrast is often considered safer than iodine contrast, there have also been increasing concerns over the safety of gadolinium-based contrast in high-risk patient groups and general accumulation of gadolinium in patients who receive this agent repeatedly. As the most common non-CE MRA imaging method, time-of-flight (TOF) MRA has been routinely used in intracranial imaging. Depending on the clinical application, a 2D, 3D or hybrid 2D / 3D gradient echo (GRE) sequence can be used for TOF.

[0003] Due to the slightly different resonant frequencies between water and fat signals, a phase difference is accumulated between the two signals. Data at an echo time (TE) close to water-fat out-of-phase (OP) are often collected in conventional TOF MRA, in which bright fat signal can still interfere with the visualization of vessels. Fat suppression techniques such as selective excitation, selective saturation, and short T1 inversion recovery (STIR) methods, either result in insufficient fat suppression and artifacts due to sensitivity to magnetic field inhomogeneity or are signal-to-noise ratio (SNR) inefficient because of the prolonged scantime for preparation. Water-fat separation methods such as Dixon imaging may be incorporated with TOF. The resulting water-only and fat-only images provide extra information in addition to fat-suppression. However, longer scan time is often associated with Dixon imaging because images of at least two TEs are commonly acquired.SUMMARY OF THE DISCLOSURE

[0004] According to an aspect of the present disclosure, a method for phase-sensitive water-fat separation magnetic resonance imaging (MRI) is provided. The method includes acquiring k-space data with an MRI system, wherein the k-space data are acquired at echo times (TEs) at which water spins and fat spins are at least partially out-of-phase with each other. The method further includes reconstructing TE images from the k-space data using a computer system, wherein the TE images depict magnetic resonance signals from water spins and from fat spins, wherein the magnetic resonance signals from the fat spins are at least partially out-of-phase with the magnetic resonance signals from the water spins. The method also includes estimating a background phase from the TE images using the computer system, generating phase-corrected images with the computer system by performing phase correction on the TE images using the background phase, generating separated water images and fat images from the phase-corrected images using the computer system, and outputting at least one set of the water images or the fat images with the computer system.BRIEF DESCRIPTION OF THE DRAWINGS

[0005] FIG. 1 is a flowchart of an example method for phase-sensitive water-fat separation using k-space data acquired with a time-of-flight (TOF) acquisition.

[0006] FIG. 2 illustrates an example of a criterion used to separate water and fat in the case when data are acquired at the OP TE such that water and fat are completely out-of-phase. If the phase of the signal in a voxel is in a range centered at 180 degrees, the voxel is identified as fat dominant voxel. If the phase is in another range centered at 0 degree, the voxel is identified as water dominant voxel. There can also be transition ranges between the two ranges, in which the signal from a voxel is assigned as partially water and partially fat. In each voxel, the percentages of water component and fat component add up to 100%.

[0007] FIG. 3 is a flowchart of an example reconstruction using sliding-slice LQ (ssLQ)TOF. After the general TOF reconstruction (left column), the proposed approach of phase-sensitive water-fat separation (right column) was applied to the out-of-phase (OP) complex images to obtain the water / fat images.

[0008] FIG. 4 shows a demonstration of phase-sensitive water-fat separation in a healthy volunteer at 3T with TE=3.45 ms. The phase of the coil-combined OP images was estimated and corrected. After the phase correction, the phase of water and fat is close to 0° and 180° respectively, which can be used to obtain the water and fat images. Note that the color of the left two images represents phase.

[0009] FIG. 5 shows maximum intensity projections (MIPs) of the OP images and water images at 3T before and after the phase-sensitive water-fat separation. MIP: maximum intensity projection. The proposed method reveals small vessels (arrows) that are otherwise not visible in OP MIPs.

[0010] FIG. 6 shows results of images of volunteer data at 1.5 T with TE=2.4 ms.

[0011] FIG. 7 shows MIPs of the OP images and water images at 1.5 T before and after the phase-sensitive water-fat separation.

[0012] FIG. 8 shows results of water-fat separation in a healthy volunteer for neck TOF at 3T.

[0013] FIG. 9 shows MIPs of the OP images and water images at 3T before and after the phase-sensitive water-fat separation for neck TOF.

[0014] FIG. 10 illustrates a demonstration of phase corrections of one slice of Cartesian multiple overlapping thin slab acquisition (MOTS A) TOF at 1.5T. Water spins and fat spins are partially out-of-phase at TE = 3.3 ms. The water phase is 0 degree and the fat phase, 7 / . is 116 degree according to the multi -peak fat model. An overall background phase is achieved after off-resonance and background phase corrections. Note that the color of the images represents phase.

[0015] FIG. 11 illustrates combining water and fat images computed with single-point Dixon and the phase-sensitive approach at TE = 3.3 ms at 1.5T. The phase difference of water and fat is about 116 degrees in this case.

[0016] FIG. 12 shows example MIPs of the Cartesian MOTS A images at TE = 3.3 ms and water images before and after the proposed water-fat separation.

[0017] FIG. 13 shows an example comparison of the methods described in the present disclosure and a two-point Dixon water-fat separation. Some flow signals were classified as fat as indicated in the circles due to flow accumulated phase.

[0018] FIG. 14 shows an example comparison of MIPs. The first column are MIPs from OP images and rest are the MIPs from water only images. MIPs of Cartesian water only images slightly improve the visualization of small vessels (indicated by arrows) compared to MIPs of OP images at TE = 6.9 ms. The improvement is more substantial for the spiral TOF (indicated by the arrows) because fat is brighter at TE = 2.3 ms.

[0019] FIG. 15 is a block diagram of an example magnetic resonance imaging (MRI) system that can implement the methods described in the present disclosure.

[0020] FIG. 16 is a block diagram of an example system for generating phase-sensitive water-fat separated images.

[0021] FIG. 17 is a block diagram of example components that can implement the system of FIG. 16DETAILED DESCRIPTION

[0022] Described here are systems and methods for efficient fat suppression to improve flow visualization in time-of-flight (TOF) magnetic resonance angiography (MRA). The phase difference of water and fat may be used for efficient fat suppression for TOF MRA. Advantageously, the disclosed methods can be used with existing pulse sequences and have a negligible computational cost. For example, the disclosed phase-sensitive water-fat separation techniques may be implemented with spin-echo (SE) pulse sequences as well as gradient echo (GRE) pulses sequences in various applications, and can be combined with different acquisition methods including spiral, radial, and Cartesian trajectories.

[0023] TOF MRA does not require the application of intravenous contrast agents or exposure to radiation. It has been routinely used in intracranial imaging and validated for imaging coronary, thoracic, renal and peripheral vasculatures. Depending on the clinical application, a 2D, 3D or hybrid 2D / 3D gradient echo (GRE) sequence is used for TOF. Since conventional TOF MRA is relatively slow compared to contrast-enhanced (CE) MRA, advanced k-space undersampling and reconstruction methods, such as sensitivity encoding (SENSE) and compressed sensing (CS), and / or efficient k-space sampling such as spiral readouts, can be used to achieve a high spatial resolution and a large coverage simultaneously.

[0024] The inherent T1 -weighting in TOF MRA makes fat as well as blood bright. Water and fat resonate at slightly different frequencies. Conventionally, data are often collected at an echo point (TE) that water and fat signals are close to out-of-phase for clear boundaries between water and fat. However, fat signal can still interfere with the visualization of vesselsaround fatty tissues in the raw images as well as in the maximum intensity projection (MIP) images.

[0025] The phase difference between water and fat at a single TE point may be used for water-fat separation. In this approach, a pixel can be identified as water or fat depending on its relative phase. After a general TOF reconstruction, background phase can be iteratively estimated and removed. Water and fat voxels can then be identified according to their phase. Background suppression can be substantially improved with the disclosed methods, resulting in enhanced visualization of small vessels. The proposed phase-sensitive approach requires no changes of pulse sequence and negligible computational cost. In principle, the proposed phasesensitive water-fat separation may be implemented with spin-echo (SE) sequences as well as gradient echo (GRE) sequences in various applications, and be combined with different acquisition methods including spiral, radial and Cartesian trajectories. As one non-limiting example, fat suppression can be provided for TOF MRA without any changes to the data acquisition of out-of-phase, or partially out-of-phase, TOF.

[0026] Referring now to FIG. 1, a flowchart is illustrated as setting forth the steps of an example method for generating separated water and fat images using MRI based on a phasesensitive water-fat separation technique.

[0027] The method includes accessing k-space data with a computer system, as indicated at step 102. Accessing the k-space data may include retrieving such data from a memory or other suitable data storage device or medium. Additionally or alternatively, accessing the k-space data may include acquiring such data from a subject with an MRI system and transferring or otherwise communicating the data to the computer sy stem, which may be a part of the MRI system.

[0028] In general, the k-space data are acquired using a time-of-flight (TOF) data acquisition. The k-space data include magnetic resonance signals from both water and fat spins. The k-space data are also acquired at one or more echo times (TEs) such that the water and fat signals are either completely or partially out-of-phase with each other. In general, images reconstructed from the k-space data may be referred to as TE images. In addition, images reconstructed from the k-space data in which water and fat signals are completely out-of-phase with each other may be referred to as out-of-phase (OP) images.

[0029] As anon-limiting example, the TOF data acquisition may implement a localized quadratic (LQ) encoding scheme. LQ encoding is a hybrid 2D / 3D acquisition scheme, in which a chirp radiofrequency (RF) pulse is used with a linearly swept frequency to excite a slab. Asa result, the position along the slice direction is encoded by a quadratic phase. For example, the following quadratic phase may be encoded using an LQ encoding:1

[0030] where T - - is the RF pulse duration with y being the gyromagnetic ratio, yGSG being the slice selection gradient, and S being the target slice thickness;is a constant magnitude with respect to t ; the parameter M is the time-bandwidth product of the RF pulse, which also gives the ratio of the excited slab width to the final reconstructed slice resolution; During the acquisition, a series of slabs are overlapped with the center-to-center spacing between the adjacent slabs being 6. Images with slice resolution d can then be reconstructed by removing the predetermined quadratic phase in the Fourier domain of the slice dimension, as described below.

[0031] As one non-limiting example, the LQ encoding may be a sliding-slice LQ (ssLQ) acquisition, which may use a Cartesian sampling of k-space, a spiral sampling of k- space, a radial sampling of k-space, or some other suitable non-Cartesian sampling of k-space. In spiral ssLQ TOF, through-plane LQ encoding is combined with sliding-slice in-plane spiral readout for a fast, flexible and SNR efficient acquisition. When the sliding-slice spiral acquisition is applied to LQ encoding, the slab location is advanced by a step of 3 / b as the index of the spiral interleaf increases, where b is the number of spiral interleaves in one slab. A simple linear phase is applied in the Fourier domain of the slice dimension to reconstruct the images.

[0032] One or more TE images are then reconstructed from the k-space data, as indicated at step 104. As described above, the TE images are images in which water spins and fat spins are either completely or partially out-of-phase with each other. The TE image(s) can be reconstructed using any suitable reconstruction technique. In general, the TE image(s) may be complex-valued images having a real component (i.e., a magnitude component representing the magnitude of the detected magnetic resonance signals) and an imaginary component (i.e., a phase component representing the phase of the detected magnetic resonance signals).

[0033] As a non-limiting example, when the k-space data were acquired using a spiral ssLQ TOF acquisition, a general reconstruction for spiral ssLQ can be used. This reconstruction may include performing a sliding-slice phase correction and a localized quadratic (LQ)encoding phase correction. For instance, the k-space data can be Fourier transformed to create intermediate data. The intermediate data can be processed to perform the sliding-slice and / or LQ encoding phase corrections. A linear phase can be applied to the intermediate data (i.e., the k-space data transformed into the Fourier domain of the slice dimension) to correct for the sliding-slice phase and the quadratic phase can be estimated and removed from the intermediate data as described above. The resulting phase corrected data can then be transformed back into k-space using an inverse Fourier transform. A gridding and coil combination-based reconstruction can then be performed on the processed k-space data to reconstruct the TE image(s). In some instances, a deblurring at off-resonance frequency for water can also be performed as part of the reconstruction process.

[0034] An initial phase estimation is performed to estimate background phase from the TE image(s), as indicated at step 106. For example, the background phase (e.g., the slowly varying global phase) can be estimated from the TE image(s) based on filtering the TE image(s). As a non-limiting example, the background phase can be estimated by low-pass filtering the TE image(s). The TE image(s) may be filtered using a two-dimensional (2D) low- pass filter, a three-dimensional (3D) low pass filter, or other suitable filter.

[0035] The estimated background phase is used to generate one or more phase- corrected images, as indicated at step 108. As a non-limiting example, the phase-corrected image(s) may be generated by removing the estimated background phase from the TE image(s). The estimated background phase can be removed, for example, by subtracting the background phase from the TE image(s).

[0036] A phase-sensitive water-fat separation is then performed using the phase- corrected image(s) to generate separated water images and fat images, as indicated at step 110. For instance, the water and fat components can be assigned to the respective images according to the phase of each voxel in the phase-corrected image. As a non-limiting example, if the k- space data are collected at exact OP TE points, such as TE = 3.45 ms at 3T, water spins and fat spins are completely out-of-phase. In this case, the phase of water and fat is close to 0° and 180°, respectively. Therefore, the phase information in the phase-corrected image can be used to assign magnitude and phase information from each voxel to either a water images or a fat images. Thus, voxels having phase values in a range around 180 degrees can be assigned to the fat images, and voxels having phase values in a range around 0 degrees can be assigned to the water images. The range may be one the order a few degrees, tens of degrees, or the like. For example, voxels with phase values in a range of -90 degrees to +90 degrees (0+90 degrees) canbe assigned to the water images. In some other examples, phase values in a range of 0±60 degrees, 0±45 degrees, 0±30 degrees, 0±l 5 degrees, and so on. Similar ranges of phase values (e.g., ±15, ±30, ±45, ±60, ±90 degrees) from 180 degrees can be used for assigning voxels to the fat images. In this way, separate water and fat images can be generated from the phase- corrected images. There may also be transition ranges between the range of water and the range of fat, in which a voxel is assigned as partially water and partially fat. An example of possible criterion to separate water and fat is illustrated in FIG. 2. As another non-limiting example, if the k-space data are collected at other TEs, for instance, TE = 3.3 ms at 1.5 T, water spins and fat spins are partially out-of-phase. The phase of water and fat is close to 0° and 116°, respectively. Therefore, voxels having phase values in a range around 116 degrees (e.g., 116±46 degrees) can be assigned to the fat images, and voxels having phase values in a range around 0 degree (e.g., 0±70 degrees) can be assigned to the water images.

[0037] A determination is then made at decision block 112 whether the phase estimation should be updated. If so, then an updated background phase estimate is generated from the water and fat images, as indicated a step 114, which is then used to generate one or more updated phase-corrected images at step 108. The slowly varying background phase can be re-estimated using water-fat in-phase images obtained by complex subtraction of the estimated water and the fat images.

[0038] The updated phase-corrected image(s) may then be used in the phase-sensitive water-fat separation to generate updated water and fat images at step 110. When a stopping criterion is satisfied, the iterative phase estimation update loop is concluded at decision block 112.

[0039] The water and / or fat images can then be displayed to a user, stored for later use or further processing, or both, as indicated at step 116. For example, the water and / or fat images can be displayed to a user (e.g., a radiologist, another clinician).

[0040] An example implementation with spiral sliding-slice localized quadratic (ssLQ) TOF is described below. An example workflow implementing the method shown in FIG. 1 for this example study is shown in FIG. 3.

[0041] A spiral ssLQ OP TOF acquisition was used to collect k-space data from the intracranial vasculature using a 3T MRI scanner and a 1.5T MRI scanner with 15-channel head coils around the circle of Willis. Spiral ssLQ OP neck TOF data around carotid bifurcations were collected with a 17-channel head and neck coil at 3T. Scan parameters for intracranial TOF included: field of view (FOV) = 200x200x91 mm3, resolution = 0.63x0.63x1.4 mm3(3T)and 0.73x0.73x1.4 mm3(1.5 T), TR=23ms, TE= 3.45 ms (second OP at 3T) and 2.4 ms (close to first OP at 1.5 T), flip angle = 21 degrees, slab-to-slice ratio M = 16, readout length = 6.0 ms (3T) and 9.7 ms (1.5T). Scan parameters for neck TOF included: field of view (FOV) = 200x200x161 mm3, resolution = 0.9x0.9x2 0 mm3, TR=20ms, TE= 1.14 ms (first OP at 3T). A venous saturation pulse was applied in each acquisition. Flow compensation gradients were applied in the slice direction. Cartesian pre-scans were also performed to obtain low-resolution field maps of off-resonance (magnetic inhomogeneity) for deblurrmg.

[0042] The reconstruction steps are illustrated in FIG. 3. After the general reconstruction for spiral ssLQ, including phase corrections for LQ and sliding slice, gridding and deblurring at off-resonance frequency for water, an initial slowly varying global phase was estimated from the complex OP images by 2D or 3D low-pass filtering. This background phase was then removed from the OP images at the phase correction stage. Subsequently, water and fat components were assigned according to the phase of each voxel as shown in FIGS. 2 and 3, exploiting the fact that the phase of water and fat is close to 0° and 180°, respectively. The slowly varying background phase was then re-estimated using the water-fat in-phase images obtained by complex subtraction of water and fat. This procedure was iterated before output of the final water and fat images.

[0043] The results of intracranial TOF at 3T are demonstrated in FIGS. 4-5. An overall flat background phase close to 0° can be achieved after several iterations, as shown in FIG. 4. The proposed method demonstrated better background suppression in images of maximum intensity projection (MIP), enabling increased visualization of small vessels (FIG. 5), although the difference is subtle. At 1.5T, the fat signal is relatively brighter since the spiral ssLQ sequence can acquire data at the first water / fat OP echo point rather than the second one. Therefore, the overall improvement of background suppression and visualization of vessels is much more substantial (FIGS. 6-7). Sufficient fat suppression can also be seen for neck TOF are shown in FIG. 8-9.

[0044] The proposed phase sensitive approach does not require modification of the sequences. The extra computational cost in addition to the existing TOF post-processing is negligible. Although the approach has been demonstrated with spiral ssLQ TOF, the method is not limited by specific acquisition methods. It can be applied to either 2D or 3D, GRE, spinecho and bSSFP sequences, with various readouts such as Cartesian, spiral and radial. A field map of magnetic inhomogeneity was used for spiral deblurring in the presented example. It also facilitated accurate removal of the background phase that was caused by the magneticinhomogeneity. Therefore, it may be used as optional pre-scan in general, at a cost of about 10 sec extra scan time. The preferred TE is close to OP point. However, the phase difference of water and fat signals does not have to be exactly 180 degrees. Depending on the SNR, the proposed approach can be easily extended for different phase differences.

[0045] In another example study, Cartesian and spiral multiple overlapping thin slab acquisition (MOTS A) TOF around the circle of Willis were collected for three volunteers on a 1.5T scanner with a 15-channel head coil. Imaging parameters included: field-of-view (FOV) = 200 x 200 x 91 mm3, flip angle = 20° and TR = 23 ms. Cartesian scans were collected with resolution = 0.6 x 0.9 x 1.4 mm3at TEs of 6.9 ms (OP) and 3.3 ms (shortest) respectively. The scan time was 3 min 58 sec with partial echo of 62.5% and SENSE factor of two. Two-point Dixon data were also collected with scan time of 7 min 54 sec. Spiral acquisitions had a resolution of 0.73 x 0.73 x 1.4 mm3. Data were collected at TEs of 2.3 ms (OP) and 1.6 ms (shortest) with scan time 2 min 23 sec. Spiral MOTSA with two TEs were also acquired for comparison with a spiral two-point Dixon approach. A 7-sec Cartesian pre-scan was also performed to obtain a low-resolution off-resonance field map for off-resonance phase correction.

[0046] After the complex raw images were exported from the scanner, the accumulated phase from off-resonance was removed according to the field map. Subsequently, the slowly varying global background phase was estimated by low-pass filtering and removed from the TE images. Water and fat components were assigned according to the phase of each voxel, with water phase close to 0° and fat phase, 0f, computed according to a multi-peak fat model. The background phase was then re-estimated using the water-fat in-phase images. This procedure was iterated before the final water-fat separation using both phase-sensitive and single-point Dixon approaches. Let (J ^F and (^2J'2) denote water and fat calculated from the phase-sensitive approach and single-point Dixon, respectively.

[0047] where STEdenotes the phase-corrected TE image and imagdenotes the imaginary part of the phase-corrected TE image. The SNR of the single-point Dixon decreasesas the angle 0f deviates from 90 degrees and approaches 0 when 0f is close to 180 degrees.The water and fat imagesF2) can be combined as:

[0048] As anon-limiting example, a = cos20.- and a2= sin20^ can be used.

[0049] FIG. 10 demonstrates the phase corrections at TE = 3.3ms. An overall flat background phase close to 0° can be achieved after phase corrections for off-resonance and the background, which enabled both single-point Dixon and phase-sensitive approaches for waterfat separation (FIG. 11). The results are combined according to equation (2) to ameliorate the trade-off between SNR and accuracy with flexible TEs. The maximum intensity projections (MIPs) of water images from the proposed method shows significantly improved background suppression (FIG. 12). Like two-point Dixon, the proposed method classifies some flow signal as fat due to the flow accumulated phase (FIG. 13), which may be partially corrected by setting the voxels with highest magnitude as water only voxels (not show n in the figure). The proposed method resulted in increased visualization of small vessels in w ater only MIPs compared to OP images (FIG. 14). Two-point Dixon methods provide slightly cleaner fat suppression and better transition at the water-fat boundaries at a cost of doubling the scan time. The weights used in equation (2) can be optimized for improved performance.

[0050] Referring particularly now to FIG. 15, an example of an MRI system 1500 that can implement the methods described here is illustrated. The MRI system 1500 includes an operator workstation 1502 that may include a display 1504, one or more input devices 1506 (e.g., a keyboard, a mouse), and a processor 1508. The processor 1508 may include a commercially available programmable machine running a commercially available operating system. The operator workstation 1502 provides an operator interface that facilitates entering scan parameters into the MRI system 1500. The operator workstation 1502 may be coupled to different servers, including, for example, a pulse sequence server 1510, a data acquisition server 1512, a data processing server 1514, and a data store server 1516. The operator workstation 1502 and the servers 1510, 1512, 1514, and 1516 may be connected via a communication system 1540, which may include wired or wireless network connections.

[0051] The pulse sequence server 1510 functions in response to instructions provided by the operator workstation 1502 to operate a gradient system 1518 and a radiofrequency (“RF”) system 1520. Gradient waveforms for performing a prescribed scan are produced andapplied to the gradient system 1518, which then excites gradient coils in an assembly 1522 to produce the magnetic field gradients Gx, G , and Gzthat are used for spatially encoding magnetic resonance signals. The gradient coil assembly 1522 forms part of a magnet assembly 1524 that includes a polarizing magnet 1526 and a whole-body RF coil 1528.

[0052] RF waveforms are applied by the RF system 1520 to the RF coil 1528, or a separate local coil to perform the prescribed magnetic resonance pulse sequence. Responsive magnetic resonance signals detected by the RF coil 1528, or a separate local coil, are received by the RF system 1520. The responsive magnetic resonance signals may be amplified, demodulated, filtered, and digitized under direction of commands produced by the pulse sequence server 1510. The RF system 1520 includes an RF transmitter for producing a wide variety of RF pulses used in MRI pulse sequences. The RF transmitter is responsive to the prescribed scan and direction from the pulse sequence server 1510 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 1528 or to one or more local coils or coil arrays.

[0053] The RF system 1520 also includes one or more RF receiver channels. An RF receiver channel includes an RF preamplifier that amplifies the magnetic resonance signal received by the coil 1528 to which it is connected, and a detector that detects and digitizes the I and Q quadrature components of the received magnetic resonance signal. The magnitude of the received magnetic resonance signal may, therefore, be determined at a sampled point by the square root of the sum of the squares of the I and Q components:

[0054] and the phase of the received magnetic resonance signal may also be determined according to the following relationship:

[0055] The pulse sequence server 1510 may receive patient data from a physiological acquisition controller 1530. By way of example, the physiological acquisition controller 1530 may receive signals from a number of different sensors connected to the patient, includingelectrocardiograph (ECG) signals from electrodes, or respiratory signals from a respiratory bellows or other respiratory monitoring devices. These signals may be used by the pulse sequence server 1510 to synchronize, or “gate,” the performance of the scan with the subject’s heart beat or respiration.

[0056] The pulse sequence server 1510 may also connect to a scan room interface circuit 1532 that receives signals from various sensors associated with the condition of the patient and the magnet system. Through the scan room interface circuit 1532, a patient positioning system 1534 can receive commands to move the patient to desired positions during the scan.

[0057] The digitized magnetic resonance signal samples produced by the RF system 1520 are received by the data acquisition server 1512. The data acquisition server 1512 operates in response to instructions downloaded from the operator workstation 1502 to receive the realtime magnetic resonance data and provide buffer storage, so that data is not lost by data overrun. In some scans, the data acquisition server 1512 passes the acquired magnetic resonance data to the data processor server 1514. In scans that require information derived from acquired magnetic resonance data to control the further performance of the scan, the data acquisition server 1512 may be programmed to produce such information and convey it to the pulse sequence server 1510. For example, during pre-scans, magnetic resonance data may be acquired and used to calibrate the pulse sequence performed by the pulse sequence server 1510. As another example, navigator signals may be acquired and used to adjust the operating parameters of the RF system 1520 or the gradient system 1518, or to control the view order in which k-space is sampled. In still another example, the data acquisition server 1512 may also process magnetic resonance signals used to detect the arrival of a contrast agent in a magnetic resonance angiography (MRA) scan. For example, the data acquisition server 1512 may acquire magnetic resonance data and processes it in real-time to produce information that is used to control the scan.

[0058] The data processing server 1514 receives magnetic resonance data from the data acquisition server 1512 and processes the magnetic resonance data in accordance with instructions provided by the operator workstation 1502. Such processing may include, for example, reconstructing two-dimensional or three-dimensional images by performing a Fourier transformation of raw k-space data, performing other image reconstruction algorithms (e.g., parallel imaging, iterative or backproj ection reconstruction algorithms), applying filters to rawk-space data or to reconstructed images, generating functional magnetic resonance images, or calculating motion or flow images.

[0059] Images reconstructed by the data processing server 1514 are conveyed back to the operator workstation 1502 for storage. Real-time images may be stored in a data base memory cache, from which they may be output to operator display 1502 or a display 1536. Batch mode images or selected real time images may be stored in a host database on disc storage 1538. When such images have been reconstructed and transferred to storage, the data processing server 1514 may notify the data store server 1516 on the operator workstation 1502. The operator workstation 1502 may be used by an operator to archive the images, produce films, or send the images via a network to other facilities.

[0060] The MRI system 1500 may also include one or more networked workstations 1542. For example, a networked workstation 1542 may include a display 1544, one or more input devices 1546 (e.g., a keyboard, a mouse), and a processor 1548. The networked workstation 1542 may be located within the same facility as the operator workstation 1502, or in a different facility, such as a different healthcare institution or clinic.

[0061] The networked workstation 1542 may gain remote access to the data processing server 1514 or data store server 1516 via the communication system 1540. Accordingly, multiple networked workstations 1542 may have access to the data processing server 1514 and the data store server 1516. In this manner, magnetic resonance data, reconstructed images, or other data may be exchanged between the data processing server 1514 or the data store server 1516 and the networked workstations 1542, such that the data or images may be remotely processed by a networked workstation 1542.

[0062] FIG. 16 shows an example of a system 1600 for generating phase-sensitive water-fat separated images in accordance with some embodiments described in the present disclosure. As shown in FIG. 16, a computing device 1650 can receive one or more types of data (e.g., k-space data, out-of-phase image data) from data source 1602. In some embodiments, computing device 1650 can execute at least a portion of a phase-sensitive waterfat separation system 1604 to generate phase-sensitive water and fat images from data received from the data source 1602.

[0063] Additionally or alternatively, in some embodiments, the computing device 1650 can communicate information about data received from the data source 1602 to a server 1652 over a communication network 1654, which can execute at least a portion of the phase-sensitive water-fat separation system 1604. In such embodiments, the server 1652 can return informationto the computing device 1650 (and / or any other suitable computing device) indicative of an output of the phase-sensitive water-fat separation system 1604.

[0064] In some embodiments, computing device 1650 and / or server 1652 can be any suitable computing device or combination of devices, such as a desktop computer, a laptop computer, a smartphone, a tablet computer, a wearable computer, a server computer, a virtual machine being executed by a physical computing device, and so on. The computing device 1650 and / or server 1652 can also reconstruct images from the data.

[0065] In some embodiments, data source 1602 can be any suitable source of data (e.g., measurement data, images reconstructed from measurement data, processed image data), such as an MRI system, another computing device (e.g., a server storing measurement data, images reconstructed from measurement data, processed image data), and so on. In some embodiments, data source 1602 can be local to computing device 1650. For example, data source 1602 can be incorporated with computing device 1650 (e.g., computing device 1650 can be configured as part of a device for measuring, recording, estimating, acquiring, or otherwise collecting or storing data). As another example, data source 1602 can be connected to computing device 1650 by a cable, a direct wireless link, and so on. Additionally or alternatively, in some embodiments, data source 1602 can be located locally and / or remotely from computing device 1650, and can communicate data to computing device 1650 (and / or server 1652) via a communication network (e.g., communication network 1654).

[0066] In some embodiments, communication network 1654 can be any suitable communication network or combination of communication networks. For example, communication network 1654 can include a Wi-Fi network (which can include one or more wireless routers, one or more switches, etc.), a peer-to-peer network (e.g., a Bluetooth network), a cellular network (e.g., a 3G network, a 4G network, etc., complying with any suitable standard, such as CDMA, GSM, LTE, LTE Advanced, WiMAX, etc.), other types of wireless network, a wired network, and so on. In some embodiments, communication network 1654 can be a local area network, a wide area network, a public network (e.g., the Internet), a private or semi -private network (e.g., a corporate or university intranet), any other suitable type of network, or any suitable combination of networks. Communications links shown in FIG. 16 can each be any suitable communications link or combination of communications links, such as wired links, fiber optic links, Wi-Fi links, Bluetooth links, cellular links, and so on.

[0067] Referring now to FIG. 17, an example of hardware 1700 that can be used to implement data source 1602, computing device 1650, and server 1652 in accordance with some embodiments of the systems and methods described in the present disclosure is shown.

[0068] As shown in FIG. 17, in some embodiments, computing device 1650 can include a processor 1702, a display 1704, one or more inputs 1706, one or more communication systems 1708, and / or memory 1710. In some embodiments, processor 1702 can be any suitable hardware processor or combination of processors, such as a central processing unit (CPU), a graphics processing unit (GPU), and so on. In some embodiments, display 1704 can include any suitable display devices, such as a liquid crystal display (LCD) screen, a light-emitting diode (LED) display, an organic LED (OLED) display, an electrophoretic display (e.g., an “e- ink” display), a computer monitor, a touchscreen, a television, and so on. In some embodiments, inputs 1706 can include any suitable input devices and / or sensors that can be used to receive user input, such as a keyboard, a mouse, a touchscreen, a microphone, and so on.

[0069] In some embodiments, communications systems 1708 can include any suitable hardware, firmware, and / or software for communicating information over communication network 1654 and / or any other suitable communication networks. For example, communications systems 1708 can include one or more transceivers, one or more communication chips and / or chip sets, and so on. In a more particular example, communications systems 1708 can include hardware, firmware, and / or software that can be used to establish a Wi-Fi connection, a Bluetooth connection, a cellular connection, an Ethernet connection, and so on.

[0070] In some embodiments, memory 1710 can include any suitable storage device or devices that can be used to store instructions, values, data, or the like, that can be used, for example, by processor 1702 to present content using display 1704, to communicate with server 1652 via communications system(s) 1708, and so on. Memory 1710 can include any suitable volatile memory, non-volatile memory, storage, or any suitable combination thereof. For example, memory 1710 can include random-access memory (RAM), read-only memory (ROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM), other forms of volatile memory, other forms of non-volatile memory, one or more forms of semi -volatile memory, one or more flash drives, one or more hard disks, one or more solid state drives, one or more optical drives, and so on. In some embodiments, memory 1710 can have encoded thereon, or otherwise stored therein, a computer program for controlling operation ofcomputing device 1650. In such embodiments, processor 1702 can execute at least a portion of the computer program to present content (e.g., images, user interfaces, graphics, tables), receive content from server 1652, transmit information to server 1652, and so on. For example, the processor 1702 and the memory 1710 can be configured to perform the methods described herein (e.g., the method of FIG. 1).

[0071] In some embodiments, server 1652 can include a processor 1712, a display 1714, one or more inputs 1716, one or more communications systems 1718, and / or memory 1720. In some embodiments, processor 1712 can be any suitable hardware processor or combination of processors, such as a CPU, a GPU, and so on. In some embodiments, display 1714 can include any suitable display devices, such as an LCD screen, LED display, OLED display, electrophoretic display, a computer monitor, a touchscreen, a television, and so on. In some embodiments, inputs 1716 can include any suitable input devices and / or sensors that can be used to receive user input, such as a keyboard, a mouse, a touchscreen, a microphone, and so on.

[0072] In some embodiments, communications systems 1718 can include any suitable hardware, firmware, and / or software for communicating information over communication network 1654 and / or any other suitable communication networks. For example, communications systems 1718 can include one or more transceivers, one or more communication chips and / or chip sets, and so on. In a more particular example, communications systems 1718 can include hardware, firmware, and / or software that can be used to establish a Wi-Fi connection, a Bluetooth connection, a cellular connection, an Ethernet connection, and so on.

[0073] In some embodiments, memory 1720 can include any suitable storage device or devices that can be used to store instructions, values, data, or the like, that can be used, for example, by processor 1712 to present content using display 1714, to communicate with one or more computing devices 1650, and so on. Memory 1720 can include any suitable volatile memory, non-volatile memory, storage, or any suitable combination thereof. For example, memory 1720 can include RAM, ROM, EPROM, EEPROM, other types of volatile memory, other types of non-volatile memory, one or more types of semi-volatile memory, one or more flash drives, one or more hard disks, one or more solid state drives, one or more optical drives, and so on. In some embodiments, memory 1720 can have encoded thereon a server program for controlling operation of server 1652. In such embodiments, processor 1712 can execute at least a portion of the server program to transmit information and / or content (e.g., data, images,a user interface) to one or more computing devices 1650, receive information and / or content from one or more computing devices 1650, receive instructions from one or more devices (e.g., a personal computer, a laptop computer, a tablet computer, a smartphone), and so on.

[0074] In some embodiments, the server 1652 is configured to perform the methods described in the present disclosure. For example, the processor 1712 and memory 1720 can be configured to perform the methods described herein (e.g., the method of FIG. 1).

[0075] In some embodiments, data source 1602 can include a processor 1722, one or more data acquisition systems 1724, one or more communications systems 1726, and / or memory 1728. In some embodiments, processor 1722 can be any suitable hardware processor or combination of processors, such as a CPU, a GPU, and so on. In some embodiments, the one or more data acquisition systems 1724 are generally configured to acquire data, images, or both, and can include an MRI system. Additionally or alternatively, in some embodiments, the one or more data acquisition systems 1724 can include any suitable hardware, firmware, and / or software for coupling to and / or controlling operations of an MRI system. In some embodiments, one or more portions of the data acquisition system(s) 1724 can be removable and / or replaceable.

[0076] Note that, although not shown, data source 1602 can include any suitable inputs and / or outputs. For example, data source 1602 can include input devices and / or sensors that can be used to receive user input, such as a keyboard, a mouse, a touchscreen, a microphone, a trackpad, a trackball, and so on. As another example, data source 1602 can include any suitable display devices, such as an LCD screen, an LED display, an OLED display, an electrophoretic display, a computer monitor, a touchscreen, a television, etc., one or more speakers, and so on.

[0077] In some embodiments, communications systems 1726 can include any suitable hardware, firmware, and / or software for communicating information to computing device 1650 (and, in some embodiments, over communication network 1654 and / or any other suitable communication networks). For example, communications systems 1726 can include one or more transceivers, one or more communication chips and / or chip sets, and so on. In a more particular example, communications systems 1726 can include hardware, firmware, and / or software that can be used to establish a wired connection using any suitable port and / or communication standard (e.g., VGA, DVI video, USB, RS-232, etc ), Wi-Fi connection, a Bluetooth connection, a cellular connection, an Ethernet connection, and so on.

[0078] In some embodiments, memory 1728 can include any suitable storage device or devices that can be used to store instructions, values, data, or the like, that can be used, forexample, by processor 1722 to control the one or more data acquisition systems 1724, and / or receive data from the one or more data acquisition systems 1724; to generate images from data; present content (e.g., data, images, a user interface) using a display; communicate with one or more computing devices 1650; and so on. Memory 1728 can include any suitable volatile memory, non-volatile memory, storage, or any suitable combination thereof. For example, memory 1728 can include RAM, ROM, EPROM, EEPROM, other types of volatile memory, other types of non-volatile memory, one or more types of semi-volatile memory, one or more flash drives, one or more hard disks, one or more solid state drives, one or more optical drives, and so on. In some embodiments, memory 1728 can have encoded thereon, or otherwise stored therein, a program for controlling operation of data source 1602. In such embodiments, processor 1722 can execute at least a portion of the program to generate images, transmit information and / or content (e.g., data, images, a user interface) to one or more computing devices 1650, receive information and / or content from one or more computing devices 1650, receive instructions from one or more devices (e.g., a personal computer, a laptop computer, a tablet computer, a smartphone, etc.), and so on.

[0079] In some embodiments, any suitable computer-readable media can be used for storing instructions for performing the functions and / or processes described herein. For example, in some embodiments, computer-readable media can be transitory or non-transitory. For example, non-transitory computer-readable media can include media such as magnetic media (e.g., hard disks, floppy disks), optical media (e.g., compact discs, digital video discs, Blu-ray discs), semiconductor media (e.g., RAM, flash memory, EPROM, EEPROM), any suitable media that is not fleeting or devoid of any semblance of permanence during transmission, and / or any suitable tangible media. As another example, transitory computer- readable media can include signals on networks, in wires, conductors, optical fibers, circuits, or any suitable media that is fleeting and devoid of any semblance of permanence during transmission, and / or any suitable intangible media.

[0080] As used herein in the context of computer implementation, unless otherwise specified or limited, the terms “component,” “system,” “module,” “framework,” and the like are intended to encompass part or all of computer-related systems that include hardware, software, a combination of hardware and software, or software in execution. For example, a component may be, but is not limited to being, a processor device, a process being executed (or executable) by a processor device, an object, an executable, a thread of execution, a computer program, or a computer. By way of illustration, both an application running on acomputer and the computer can be a component. One or more components (or system, module, and so on) may reside within a process or thread of execution, may be localized on one computer, may be distributed between two or more computers or other processor devices, or may be included within another component (or system, module, and so on).

[0081] In some implementations, devices or systems disclosed herein can be utilized or installed using methods embodying aspects of the disclosure. Correspondingly, description herein of particular features, capabilities, or intended purposes of a device or system is generally intended to inherently include disclosure of a method of using such features for the intended purposes, a method of implementing such capabilities, and a method of installing disclosed (or otherwise known) components to support these purposes or capabilities. Similarly, unless otherwise indicated or limited, discussion herein of any method of manufacturing or using a particular device or system, including installing the device or system, is intended to inherently include disclosure, as embodiments of the disclosure, of the utilized features and implemented capabilities of such device or system.

[0082] The present disclosure has described one or more preferred embodiments, and it should be appreciated that many equivalents, alternatives, variations, and modifications, aside from those expressly stated, are possible and within the scope of the invention.

Claims

CLAIMS1. A method for phase-sensitive water-fat separation magnetic resonance imaging (MRI), comprising: acquinng k-space data with an MRI system, wherein the k-space data are acquired at echo times (TEs) at which water spins and fat spins are at least partially out- of-phase with each other; reconstructing TE images from the k-space data using a computer system, wherein the TE images depict magnetic resonance signals from water spins and from fat spins, wherein the magnetic resonance signals from the fat spins are at least partially out-of-phase with the magnetic resonance signals from the water spins; estimating a background phase from the TE images using the computer system; generating phase-corrected images with the computer system by performing phase correction on the TE images using the background phase; generating separated water images and fat images from the phase-corrected images using the computer system; and outputting at least one set of the water images or the fat images with the computer system.

2. The method of claim 1, further comprising re-estimating the background phase from the water images and fat images, updating the phase-corrected images using the re- estimated background phase, and generating updated water images and updated fat images from the updated phase-corrected images.

3. The method of claim 2, wherein the background phase is re-estimated based on a complex subtraction of the water images and the fat images.

4. The method of claim 1, wherein generating separated water images and fat images from the phase-corrected images comprises assigning magnitude and phase information from each voxel in the phase-corrected images to either the water images or the fat images based on a phase value of the voxel in the phase-corrected image.

5. The method of claim 4, wherein voxels in the phase-corrected images having phase values within a first range are assigned to the water images and voxels in the phase- corrected images having phase values within a second range are assigned to the fat images.

6. The method of claim 5, wherein the first range is 0±90 degrees and the second range is 180±90 degrees.

7. The method of claim 5, wherein the first range is 0±70 degrees and the second range is 116±46 degrees.

8. The method of claim 1, wherein generating he phase-corrected image by performing the phase correction on the TE images comprises subtracting the background phase from the TE images.

9. The method of claim 1, wherein the k-space data are acquired using a time-of- flight (TOF) pulse sequence.

10. The method of claim 9, wherein the TOF pulse sequence is a TOF magnetic resonance angiography (MRA) pulse sequence.

11. The method of claim 9, wherein the TOF pulse sequence includes a localized quadratic (LQ) encoding scheme.

12. The method of claim 11, wherein reconstructing the TE images includes correcting for quadratic phase in the k-space data.

13. The method of claim 11, wherein the TOF pulse sequence further includes a sliding-slice acquisition.

14. The method of claim 13, wherein reconstructing the TE images includes correcting for a linear phase in the k-space data.

15. The method of claim 9, wherein the TOF pulse sequence is a slide-slice localized quadratic encoding (ssLQ) acquisition.

16. The method of claim 15, wherein the ssLQ acquisition is a spiral ssLQ acquisition that samples k-space using one or more spiral trajectories.

17. The method of claim 1, wherein reconstructing TE images includes deblurring for spiral trajectories using an off-resonance field map.

18. The method of claim 17, wherein the off-resonance field map is reconstructed from pre-scan k-space data acquired with a pre-scan pulse sequence.

19. The method of claim 9, wherein the TOF pulse sequence includes a multiple overlapping thin slab acquisition (MOTSA) acquisition.

20. The method of claim 19, wherein the MOTSA acquisition is a Cartesian MOTSA acquisition that samples k-space data using a Cartesian sampling trajectory.

21. The method of claim 19, wherein the MOTSA acquisition is a spiral MOTSA acquisition that samples k-space data using one or more spiral sampling trajectories.

22. The method of claim 1, wherein reconstructing the TE images includes correcting for off-resonance phase using an off-resonance field map.

23. The method of claim 1, wherein the background phase is estimated from the TE images using a low pass filtering of the TE images.

24. The method of claim 23, wherein the background phase is estimated by low pass filtering the TE images with a two-dimensional (2D) or three-dimensional (3D) low pass filter.

25. The method of claim 1, wherein the k-space data are acquired at TEs at which the water spins and the fat spin are completely out-of-phase with each other.

Images