SPIRiT: Iterative self-consistent parallel imaging reconstruction from arbitrary k-space

Highly accelerated aortic 4D flow MR imaging with variable-density random undersampling

PURPOSE

To investigate an effective time-resolved variable-density random undersampling scheme combined with an efficient parallel image reconstruction method for highly accelerated aortic 4D flow MR imaging with high reconstruction accuracy.

MATERIALS AND METHODS

Variable-density Poisson-disk sampling (vPDS) was applied in both the phase-slice encoding plane and the temporal domain to accelerate the time-resolved 3D Cartesian acquisition of flow imaging. In order to generate an improved initial solution for the iterative self-consistent parallel imaging method (SPIRiT), a sample-selective view sharing reconstruction for time-resolved random undersampling (STIRRUP) was introduced. The performance of different undersampling and image reconstruction schemes were evaluated by retrospectively applying those to fully sampled data sets obtained from three healthy subjects and a flow phantom.

RESULTS

Undersampling pattern based on the combination of time-resolved vPDS, the temporal sharing scheme STIRRUP, and parallel imaging SPIRiT, were able to achieve 6-fold accelerated 4D flow MRI with high accuracy using a small number of coils (N=5). The normalized root mean square error between aorta flow waveforms obtained with the acceleration method and the fully sampled data in three healthy subjects was 0.04±0.02, and the difference in peak-systolic mean velocity was -0.29±2.56cm/s.

CONCLUSION

Qualitative and quantitative evaluation of our preliminary results demonstrate that time-resolved variable-density random sampling is efficient for highly accelerating 4D flow imaging while maintaining image reconstruction accuracy.

In vivo diffusion spectrum imaging of non-human primate brain: initial experience in transcallosal fiber examination

In comparison with conventional diffusion tensor imaging (DTI) technique, diffusion spectrum imaging (DSI) allows for delineating crossing and touching fibers in the brain and has been explored in clinical and preclinical studies. Non-human primates (NHPs) resemble most aspects of human and are widely employed in various neuroscience researches and pharmaceutical development. In the present study, a parallel imaging-based DSI protocol was implemented for in vivo fiber tracking of macaque monkey brains on a 3.0 T clinical scanner. Transcallosal fiber tracts of adult macaque brains were examined with DSI and compared with those from a a conventional DTI protocol. The results demonstrate that DSI can reveal the transcallosal fiber bundles much more extensively than the conventional DTI. The preliminary results suggest that DSI may provide a feasible and robust approach for characterizing the fiber pathways in various disease models of NHPs.

Fast, free-breathing, in vivo fetal imaging using time-resolved 3D MRI technique: preliminary results

Fetal MR imaging is very challenging due to the movement of fetus and the breathing motion of the mother. Current clinical protocols involve quick 2D scouting scans to determine scan plane and often several attempts to reorient the scan plane when the fetus moves. This makes acquisition of fetal MR images clinically challenging and results in long scan times in order to obtain images that are of diagnostic quality. Compared to 2D imaging, 3D imaging of the fetus has many advantages such as higher SNR and ability to reformat images in multiple planes. However, it is more sensitive to motion and challenging for fetal imaging due to irregular fetal motion in addition to maternal breathing and cardiac motion. This aim of this study is to develop a fast 3D fetal imaging technique to resolve the challenge of imaging the moving fetus. This 3D imaging sequence has multi-echo radial sampling in-plane and conventional Cartesian encoding through plane, which provides motion robustness and high data acquisition efficiency. The utilization of a golden-ratio based projection profile allows flexible time-resolved image reconstruction with arbitrary temporal resolution at arbitrary time points as well as high signal-to-noise and contrast-to-noise ratio. The nice features of the developed image technique allow the 3D visualization of the movements occurring throughout the scan. In this study, we applied this technique to three human subjects for fetal MRI and achieved promising preliminary results of fetal brain, heart and lung imaging.

Parallel imaging and compressed sensing combined framework for accelerating high-resolution diffusion tensor imaging using inter-image correlation

PURPOSE

Increasing acquisition efficiency is always a challenge in high-resolution diffusion tensor imaging (DTI), which has low signal-to-noise ratio and is sensitive to reconstruction artifacts. In this study, a parallel imaging (PI) and compressed sensing (CS) combined framework is proposed, which features motion error correction, PI calibration, and sparsity model using inter-image correlation tailored for high-resolution DTI.

THEORY AND METHODS

The proposed method, named anisotropic sparsity SPIRiT, consists of three steps: (i) motion-induced phase error estimation, (ii) initial CS reconstruction and PI kernel calibration, and (iii) final reconstruction combining PI and CS. Inter-image correlation of diffusion-weighted images are used through anisotropic signals for improved sparsity. A specific implementation based on multishot variable density spiral DTI is used to demonstrate the method.

RESULTS

The proposed reconstruction method was compared with CG-SENSE, CS-based joint reconstruction, and PI and CS combined methods with L1 and joint sparsity regularization, in brain DTI experiments at acceleration factors of 3 to 5. Both qualitative and quantitative results demonstrated that the proposed method resulted in better preserved image quality and more accurate DTI parameters than other methods.

CONCLUSION

The proposed method can accelerate high-resolution DTI acquisition effectively by using the sharable information among different diffusion encoding directions.

Free-breathing 3D late gadolinium enhancement imaging of the left ventricle using a stack of spirals at 3T

Purpose

To develop navigator-gated free-breathing 3D spiral late gadolinium enhancement (LGE) imaging of the left ventricle at 3T and compare it with conventional breath-hold 2D Cartesian imaging.

Materials and Methods

Equivalent slices from 3D spiral and multislice 2D Cartesian acquisitions were compared in 15 subjects in terms of image quality (1, nondiagnostic to 5, excellent), sharpness (1–3), and presence of artifacts (0–2). Blood signal-to-noise ratio (SNR), blood/myocardium contrast-to-noise ratio (CNR), and quantitative sharpness were also compared.

Results

All 3D spiral scans were completed faster than an equivalent 2D Cartesian short-axis stack (85 vs. 230 sec, P < 0.001). Image quality was significantly higher for 2D Cartesian images than 3D spiral images (3.7 ± 0.87 vs. 3.4 ± 1.05, P = 0.03) but not for mid or apical slices specifically. There were no significant differences in qualitative and quantitative sharpness (95% confidence interval [CI]: 1.91 ± 0.67 vs. 1.93 ± 0.69, P = 0.83 and 95% CI: 0.41 ± 0.07 vs. 0.40 ± 0.09, P = 0.25, respectively), artifact scores (95% CI: 0.16 ± 0.37 vs. 0.40 ± 0.58, P = 0.16), SNR (95% CI: 121.5 ± 55.3 vs. 136.4 ± 77.9, P = 0.13), and CNR (95% CI: 101.6 ± 48.4 vs. 102.7 ± 61.8, P = 0.98). Similar enhancement ratios (0.65 vs. 0.62) and volumes (13.8 vs. 14.1cm³) were measured from scar regions of three patients.

Conclusio

Navigator-gated 3D spiral LGE imaging can be performed in significantly and substantially shorter acquisition durations, although with some reduced image quality, than multiple breath-hold 2D Cartesian imaging while providing higher resolution and contiguous coverage..

Incorporating reference in parallel imaging and compressed sensing

PURPOSE

To develop a new compressed sensing parallel imaging technique called READ-PICS that can effectively incorporate prior information from a reference scan for MR image reconstruction from highly undersampled multichannel measurements.

METHODS

READ-PICS incorporates information from a high-spatial-resolution reference prior using the generalized series model, to achieve increased image sparsity and mitigated noise amplification simultaneously. To further improve the ill-conditioning of the parallel imaging system, an annular area in the central residual k-space is used for calibration. Additionally, the mixed L1-L2 norm of the coefficients from the prior component and residual component is used to enforce joint sparsity.

RESULTS

The evaluations on parametric imaging and multiscan experiment demonstrate superior performance of READ-PICS in terms of detail preservation and noise suppression compared to state-of-the-art technique, L1-Iterative self-consistent parallel imaging reconstruction, and prescan required method, correlation imaging.

CONCLUSIONS

The proposed method can significantly increase signal sparsity and improve the ill-conditioning of the parallel imaging system using reference adaptive regularization. This technique can be easily adapted to other imaging applications where multiple images need to be acquired sequentially and a reference prior is also available.

Noncontrast peripheral MRA with spiral echo train imaging

PURPOSE

To develop a spin echo train sequence with spiral readout gradients with improved artery-vein contrast for noncontrast angiography.

THEORY

Venous T2 becomes shorter as the echo spacing is increased in echo train sequences, improving contrast. Spiral acquisitions, due to their data collection efficiency, facilitate long echo spacings without increasing scan times.

METHODS

Bloch equation simulations were performed to determine optimal sequence parameters, and the sequence was applied in five volunteers. In two volunteers, the sequence was performed with a range of echo times and echo spacings to compare with the theoretical contrast behavior. A Cartesian version of the sequence was used to compare contrast appearance with the spiral sequence. Additionally, spiral parallel imaging was optionally used to improve image resolution.

RESULTS

In vivo, artery-vein contrast properties followed the general shape predicted by simulations, and good results were obtained in all stations. Compared with a Cartesian implementation, the spiral sequence had superior artery-vein contrast, better spatial resolution (1.2 mm(2) versus 1.5 mm(2) ), and was acquired in less time (1.4 min versus 7.5 min).

CONCLUSION

The spiral spin echo train sequence can be used for flow-independent angiography to generate three-dimensional angiograms of the periphery quickly and without the use of contrast agents.

Distributed MRI reconstruction using Gadgetron-based cloud computing

PURPOSE

To expand the open source Gadgetron reconstruction framework to support distributed computing and to demonstrate that a multinode version of the Gadgetron can be used to provide nonlinear reconstruction with clinically acceptable latency.

METHODS

The Gadgetron framework was extended with new software components that enable an arbitrary number of Gadgetron instances to collaborate on a reconstruction task. This cloud-enabled version of the Gadgetron was deployed on three different distributed computing platforms ranging from a heterogeneous collection of commodity computers to the commercial Amazon Elastic Compute Cloud. The Gadgetron cloud was used to provide nonlinear, compressed sensing reconstruction on a clinical scanner with low reconstruction latency (eg, cardiac and neuroimaging applications).

RESULTS

The proposed setup was able to handle acquisition and 11 -SPIRiT reconstruction of nine high temporal resolution real-time, cardiac short axis cine acquisitions, covering the ventricles for functional evaluation, in under 1 min. A three-dimensional high-resolution brain acquisition with 1 mm(3) isotropic pixel size was acquired and reconstructed with nonlinear reconstruction in less than 5 min.

CONCLUSION

A distributed computing enabled Gadgetron provides a scalable way to improve reconstruction performance using commodity cluster computing. Nonlinear, compressed sensing reconstruction can be deployed clinically with low image reconstruction latency.

Accelerated MRI with CIRcular Cartesian UnderSampling (CIRCUS): a variable density Cartesian sampling strategy for compressed sensing and parallel imaging

PURPOSE

This study proposes and evaluates a novel method for generating efficient undersampling patterns for 3D Cartesian acquisition with compressed sensing (CS) and parallel imaging (PI).

METHODS

Image quality achieved with schemes that accelerate data acquisition, including CS and PI, are sensitive to the design of the specific undersampling scheme used. Ideally random sampling is required to recover MR images from undersampled data with CS. In practice, pseudo-random sampling schemes are usually applied. Radial or spiral sampling either for Cartesian or non-Cartesian acquisitions has been using because of its favorable features such as interleaving flexibility. In this study, we propose to undersample data on the ky-kz plane of the 3D Cartesian acquisition by circularly selecting sampling points in a way that maintains the features of both random and radial or spiral sampling.

RESULTS

The proposed sampling scheme is shown to outperform conventional random and radial or spiral samplings for 3D Cartesian acquisition and is found to be comparable to advanced variable-density Poisson-Disk sampling (vPDS) while retaining interleaving flexibility for dynamic imaging, based on the results with retrospective undersampling. Our preliminary results with the prospective implementation of the proposed undersampling strategy demonstrated its favorable features.

CONCLUSIONS

The proposed undersampling patterns for 3D Cartesian acquisition possess the desirable properties of randomization and radial or spiral trajectories. It provides easy implementation, flexible sampling, and high accuracy of image reconstruction with CS and PI.

Fast reconstruction for multichannel compressed sensing using a hierarchically semiseparable solver

PURPOSE

The adoption of multichannel compressed sensing (CS) for clinical magnetic resonance imaging (MRI) hinges on the ability to accurately reconstruct images from an undersampled dataset in a reasonable time frame. When CS is combined with SENSE parallel imaging, reconstruction can be computationally intensive. As an alternative to iterative methods that repetitively evaluate a forward CS+SENSE model, we introduce a technique for the fast computation of a compact inverse model solution.

METHODS

A recently proposed hierarchically semiseparable (HSS) solver is used to compactly represent the inverse of the CS+SENSE encoding matrix to a high level of accuracy. To investigate the computational efficiency of the proposed HSS-Inverse method, we compare reconstruction time with the current state-of-the-art. In vivo 3T brain data at multiple image contrasts, resolutions, acceleration factors, and number of receive channels were used for this comparison.

RESULTS

The HSS-Inverse method allows for >6× speedup when compared to current state-of-the-art reconstruction methods with the same accuracy. Efficient computational scaling is demonstrated for CS+SENSE with respect to image size. The HSS-Inverse method is also shown to have minimal dependency on the number of parallel imaging channels/acceleration factor.

CONCLUSIONS

The proposed HSS-Inverse method is highly efficient and should enable real-time CS reconstruction on standard MRI vendors' computational hardware.

Combined dynamic contrast-enhanced liver MRI and MRA using interleaved variable density sampling

PURPOSE

To develop and evaluate a method for volumetric contrast-enhanced MRI of the liver, with high spatial and temporal resolutions, for combined dynamic imaging and MR angiography (MRA) using a single injection of contrast agent.

METHODS

An interleaved variable density (IVD) undersampling pattern was implemented in combination with a real-time-triggered, time-resolved, dual-echo 3D spoiled gradient echo sequence. Parallel imaging autocalibration lines were acquired only once during the first time frame. Imaging was performed in 10 subjects with focal nodular hyperplasia (FNH) and compared with their clinical MRI. The angiographic phase of the proposed method was compared with a dedicated MR angiogram acquired during a second injection of contrast.

RESULTS

A total of 21 FNH, three cavernous hemangiomas, and 109 arterial segments were visualized in 10 subjects. The temporally resolved images depicted the characteristic arterial enhancement pattern of the lesions with a 4-s update rate. Images were graded as having significantly higher quality compared with the clinical MRI. Angiograms produced from the IVD method provided noninferior diagnostic assessment compared with the dedicated MR angiogram.

CONCLUSION

Using an undersampled IVD imaging method, we have demonstrated the feasibility of obtaining high spatial and temporal resolution dynamic contrast-enhanced imaging and simultaneous MRA of the liver.

Low-rank modeling of local k-space neighborhoods (LORAKS) for constrained MRI

Recent theoretical results on low-rank matrix reconstruction have inspired significant interest in low-rank modeling of MRI images. Existing approaches have focused on higher-dimensional scenarios with data available from multiple channels, timepoints, or image contrasts. The present work demonstrates that single-channel, single-contrast, single-timepoint k-space data can also be mapped to low-rank matrices when the image has limited spatial support or slowly varying phase. Based on this, we develop a novel and flexible framework for constrained image reconstruction that uses low-rank matrix modeling of local k-space neighborhoods (LORAKS). A new regularization penalty and corresponding algorithm for promoting low-rank are also introduced. The potential of LORAKS is demonstrated with simulated and experimental data for a range of denoising and sparse-sampling applications. LORAKS is also compared against state-of-the-art methods like homodyne reconstruction, l1-norm minimization, and total variation minimization, and is demonstrated to have distinct features and advantages. In addition, while calibration-based support and phase constraints are commonly used in existing methods, the LORAKS framework enables calibrationless use of these constraints.

Self-gated free-breathing 3D coronary CINE imaging with simultaneous water and fat visualization

The aim of this study was to develop a novel technique for acquiring 3-dimensional (3D) coronary CINE magnetic resonance images with both water and fat visualization during free breathing and without external respiratory or cardiac gating. The implemented multi-echo hybrid 3D radial balanced Steady-State Free Precession (SSFP) sequence has an efficient data acquisition and is robust against motion. The k-space center along the slice encoding direction was repeatedly acquired to derive both respiratory and cardiac self-gating signals without an increase in scan time, enabling a free-breathing acquisition. The multi-echo acquisition allowed image reconstruction with water-fat separation, providing improved visualization of the coronary artery lumen. Ten healthy subjects were imaged successfully at 1.5 T, achieving a spatial resolution of 1.0×1.0×3.0 mm³ and scan time of about 5 minutes. The proposed imaging technique provided coronary vessel depiction comparable to that obtained with conventional breath-hold imaging and navigator gated free-breathing imaging.

Robust 4D flow denoising using divergence-free wavelet transform

PURPOSE

To investigate four-dimensional flow denoising using the divergence-free wavelet (DFW) transform and compare its performance with existing techniques.

THEORY AND METHODS

DFW is a vector-wavelet that provides a sparse representation of flow in a generally divergence-free field and can be used to enforce "soft" divergence-free conditions when discretization and partial voluming result in numerical nondivergence-free components. Efficient denoising is achieved by appropriate shrinkage of divergence-free wavelet and nondivergence-free coefficients. SureShrink and cycle spinning are investigated to further improve denoising performance.

RESULTS

DFW denoising was compared with existing methods on simulated and phantom data and was shown to yield better noise reduction overall while being robust to segmentation errors. The processing was applied to in vivo data and was demonstrated to improve visualization while preserving quantifications of flow data.

CONCLUSION

DFW denoising of four-dimensional flow data was shown to reduce noise levels in flow data both quantitatively and visually.

Inlet and outlet valve flow and regurgitant volume may be directly and reliably quantified with accelerated, volumetric phase-contrast MRI

PURPOSE

To determine whether it is feasible to use solely an accelerated 4D phase-contrast magnetic resonance imaging (4D-PC MRI) acquisition to quantify net and regurgitant flow volume through each of the cardiac valves.

MATERIALS AND METHODS

Accelerated, 4D-PC MRI examinations performed between March 2010 through June 2011 as part of routine MRI examinations for congenital, structural heart disease were retrospectively reviewed and analyzed using valve-tracking visualization and quantification algorithms developed in Java and OpenGL. Excluding patients with transposition or single ventricle physiology, a total of 34 consecutive pediatric patients (19 male, 15 female; mean age 6.9 years; age range 10 months to 15 years) were identified. 4D-PC flow measurements were compared at each valve and against routine measurements from conventional cardiac MRI using Bland-Altman and Pearson correlation analysis.

RESULTS

Inlet and outlet valve net flow were highly correlated between all valves (P = 0.940-0.985). The sum of forward flow at the outlet valve and regurgitant flow at the inlet valve were consistent with volumetric displacements in each ventricle (P = 0.939-0.948). These were also highly consistent with conventional planar MRI measurements with net flow (P = 0.923-0.935) and regurgitant fractions (P = 0.917-0.972) at the outlet valve and ventricular volumes (P = 0.925-0.965).

CONCLUSION

It is possible to obtain consistent measurements of net and regurgitant blood flow across the inlet and outlet valves relying solely on accelerated 4D-PC. This may facilitate more efficient clinical quantification of valvular regurgitation.

Accelerating parameter mapping with a locally low rank constraint

PURPOSE

To accelerate MR parameter mapping using a locally low rank (LLR) constraint, and the combination of parallel imaging and the LLR constraint.

THEORY AND METHODS

An LLR method is developed for MR parameter mapping and compared with a globally low rank method in a multiecho spin-echo T2 mapping experiment. For acquisition with coil arrays, a combined LLR and parallel imaging method is proposed. The proposed method is evaluated in a variable flip angle T1 mapping experiment and compared with the LLR method and parallel imaging alone.

RESULTS

In the multiecho spin-echo T2 mapping experiment, the LLR method is more accurate than the globally low rank method for acceleration factors 2 and 3, especially for tissues with high T2 values. Variable flip angle T1 mapping is achieved by acquiring datasets with 10 flip angles, each dataset accelerated by a factor of 6, and reconstructed by the proposed method with a small normalized root mean square error of 0.025.

CONCLUSIONS

The LLR method is likely superior to the globally low rank method for MR parameter mapping. The proposed combined LLR and parallel imaging method has better performance than the two methods alone, especially with highly accelerated acquisition.

Augmented Lagrangian with variable splitting for faster non-Cartesian L1-SPIRiT MR image reconstruction

SPIRiT (iterative self-consistent parallel imaging reconstruction), and its sparsity-regularized variant L1-SPIRiT, are compatible with both Cartesian and non-Cartesian magnetic resonance imaging sampling trajectories. However, the non-Cartesian framework is more expensive computationally, involving a nonuniform Fourier transform with a nontrivial Gram matrix. We propose a novel implementation of the regularized reconstruction problem using variable splitting, alternating minimization of the augmented Lagrangian, and careful preconditioning. Our new method based on the alternating direction method of multipliers converges much faster than existing methods because of the preconditioners' heightened effectiveness. We demonstrate such rapid convergence substantially improves image quality for a fixed computation time. Our framework is a step forward towards rapid non-Cartesian L1-SPIRiT reconstructions.

Single echo MRI

Purpose

Previous nonlinear gradient research has focused on trajectories that reconstruct images with a minimum number of echoes. Here we describe sequences where the nonlinear gradients vary in time to acquire the image in a single readout. The readout is designed to be very smooth so that it can be compressed to minimal time without violating peripheral nerve stimulation limits, yielding an image from a single 4 ms echo.

Theory and Methods

This sequence was inspired by considering the code of each voxel, i.e. the phase accumulation that a voxel follows through the readout, an approach connected to traditional encoding theory. We present simulations for the initial sequence, a low slew rate analog, and higher resolution reconstructions.

Results

Extremely fast acquisitions are achievable, though as one would expect, SNR is reduced relative to the slower Cartesian sampling schemes because of the high gradient strengths.

Conclusions

The prospect that nonlinear gradients can acquire images in a single <10 ms echo makes this a novel and interesting approach to image encoding.

Accelerating sequences in the presence of metal by exploiting the spatial distribution of off-resonance

PURPOSE

To demonstrate feasibility of exploiting the spatial distribution of off-resonance surrounding metallic implants for accelerating multispectral imaging techniques.

THEORY AND METHODS

Multispectral imaging (MSI) techniques perform time-consuming independent three-dimensional acquisitions with varying radio frequency offsets to address the extreme off-resonance from metallic implants. Each off-resonance bin provides a unique spatial sensitivity that is analogous to the sensitivity of a receiver coil and, therefore, provides a unique opportunity for acceleration. Fully sampled MSI was performed to demonstrate retrospective acceleration. A uniform sampling pattern across off-resonance bins was compared with several adaptive sampling strategies using a total hip replacement phantom. Monte Carlo simulations were performed to compare noise propagation of two of these strategies. With a total knee replacement phantom, positive and negative off-resonance bins were strategically sampled with respect to the B0 field to minimize aliasing. Reconstructions were performed with a parallel imaging framework to demonstrate retrospective acceleration.

RESULTS

An adaptive sampling scheme dramatically improved reconstruction quality, which was supported by the noise propagation analysis. Independent acceleration of negative and positive off-resonance bins demonstrated reduced overlapping of aliased signal to improve the reconstruction.

CONCLUSION

This work presents the feasibility of acceleration in the presence of metal by exploiting the spatial sensitivities of off-resonance bins.

Non-Cartesian parallel imaging reconstruction

Non-Cartesian parallel imaging has played an important role in reducing data acquisition time in MRI. The use of non-Cartesian trajectories can enable more efficient coverage of k-space, which can be leveraged to reduce scan times. These trajectories can be undersampled to achieve even faster scan times, but the resulting images may contain aliasing artifacts. Just as Cartesian parallel imaging can be used to reconstruct images from undersampled Cartesian data, non-Cartesian parallel imaging methods can mitigate aliasing artifacts by using additional spatial encoding information in the form of the nonhomogeneous sensitivities of multi-coil phased arrays. This review will begin with an overview of non-Cartesian k-space trajectories and their sampling properties, followed by an in-depth discussion of several selected non-Cartesian parallel imaging algorithms. Three representative non-Cartesian parallel imaging methods will be described, including Conjugate Gradient SENSE (CG SENSE), non-Cartesian generalized autocalibrating partially parallel acquisition (GRAPPA), and Iterative Self-Consistent Parallel Imaging Reconstruction (SPIRiT). After a discussion of these three techniques, several potential promising clinical applications of non-Cartesian parallel imaging will be covered.

Investigating the quantitative fidelity of prospectively undersampled chemical shift imaging in muscular dystrophy with compressed sensing and parallel imaging reconstruction

PURPOSE

Fat fraction measurement in muscular dystrophy has an important role to play in future therapy trials. Undersampled data acquisition reconstructed by combined compressed sensing and parallel imaging (CS-PI) can potentially reduce trial cost and improve compliance. These benefits are only gained from prospectively undersampled acquisitions.

METHODS

Eight patients with Becker muscular dystrophy were recruited and prospectively undersampled data at ratios of 3.65×, 4.94×, and 6.42× were acquired in addition to fully sampled data: equivalent coherent undersamplings were acquired for reconstruction with parallel imaging alone (PI). Fat fraction maps and maps of total signal were created using a combined compressed sensing/parallel imaging (CS-PI) reconstruction.

RESULTS

The CS-PI reconstructions are of sufficient quality to allow muscle delineation at 3.65× and 4.94× undersampling but some muscles were obscured at 6.42×. When plotted against the fat fractions derived from fully sampled data, non-significant bias and 95% limits of agreement of 1.58%, 2.17% and 2.41% were found for the three CS-PI reconstructions, while a 3.36× PI reconstruction yields 2.78%, 1.8 times worse than the equivalent CS-PI reconstruction.

CONCLUSION

Prospective undersampling and CS-PI reconstruction of muscle fat fraction mapping can be used to accelerate muscle fat fraction measurement in muscular dystrophy.

PCLR: phase-constrained low-rank model for compressive diffusion-weighted MRI

PURPOSE

This work develops a compressive sensing approach for diffusion-weighted (DW) MRI.

THEORY AND METHODS

A phase-constrained low-rank (PCLR) approach was developed using the image coherence across the DW directions for efficient compressive DW MRI, while accounting for drastic phase changes across the DW directions, possibly as a result of eddy current, and rigid and nonrigid motions. In PCLR, a low-resolution phase estimation was used for removing phase inconsistency between DW directions. In our implementation, GRAPPA (generalized autocalibrating partial parallel acquisition) was incorporated for better phase estimation while allowing higher undersampling factor. An efficient and easy-to-implement image reconstruction algorithm, consisting mainly of partial Fourier update and singular value decomposition, was developed for solving PCLR.

RESULTS

The error measures based on diffusion-tensor-derived metrics and tractography indicated that PCLR, with its joint reconstruction of all DW images using the image coherence, outperformed the frame-independent reconstruction through zero-padding FFT. Furthermore, using GRAPPA for phase estimation, PCLR readily achieved a four-fold undersampling.

CONCLUSION

The PCLR is developed and demonstrated for compressive DW MRI. A four-fold reduction in k-space sampling could be readily achieved without substantial degradation of reconstructed images and diffusion tensor measures, making it possible to significantly reduce the data acquisition in DW MRI and/or improve spatial and angular resolutions.

Calibrationless parallel magnetic resonance imaging: a joint sparsity model

State-of-the-art parallel MRI techniques either explicitly or implicitly require certain parameters to be estimated, e.g., the sensitivity map for SENSE, SMASH and interpolation weights for GRAPPA, SPIRiT. Thus all these techniques are sensitive to the calibration (parameter estimation) stage. In this work, we have proposed a parallel MRI technique that does not require any calibration but yields reconstruction results that are at par with (or even better than) state-of-the-art methods in parallel MRI. Our proposed method required solving non-convex analysis and synthesis prior joint-sparsity problems. This work also derives the algorithms for solving them. Experimental validation was carried out on two datasets-eight channel brain and eight channel Shepp-Logan phantom. Two sampling methods were used-Variable Density Random sampling and non-Cartesian Radial sampling. For the brain data, acceleration factor of 4 was used and for the other an acceleration factor of 6 was used. The reconstruction results were quantitatively evaluated based on the Normalised Mean Squared Error between the reconstructed image and the originals. The qualitative evaluation was based on the actual reconstructed images. We compared our work with four state-of-the-art parallel imaging techniques; two calibrated methods-CS SENSE and l1SPIRiT and two calibration free techniques-Distributed CS and SAKE. Our method yields better reconstruction results than all of them.

Radial k-t SPIRiT: autocalibrated parallel imaging for generalized phase-contrast MRI

PURPOSE

To extend SPIRiT to additionally exploit temporal correlations for highly accelerated generalized phase-contrast MRI and to compare the performance of the proposed radial k-t SPIRiT method relative to frame-by-frame SPIRiT and radial k-t GRAPPA reconstruction for velocity and turbulence mapping in the aortic arch.

THEORY AND METHODS

Free-breathing navigator-gated two-dimensional radial cine imaging with three-directional multi-point velocity encoding was implemented and fully sampled data were obtained in the aortic arch of healthy volunteers. Velocities were encoded with three different first gradient moments per axis to permit quantification of mean velocity and turbulent kinetic energy. Velocity and turbulent kinetic energy maps from up to 14-fold undersampled data were compared for k-t SPIRiT, frame-by-frame SPIRiT, and k-t GRAPPA relative to the fully sampled reference.

RESULTS

Using k-t SPIRiT, improvements in magnitude and velocity reconstruction accuracy were found. Temporally resolved magnitude profiles revealed a reduction in spatial blurring with k-t SPIRiT compared with frame-by-frame SPIRiT and k-t GRAPPA for all velocity encodings, leading to improved estimates of turbulent kinetic energy.

CONCLUSION

k-t SPIRiT offers improved reconstruction accuracy at high radial undersampling factors and hence facilitates the use of generalized phase-contrast MRI for routine use.

Calibrationless parallel imaging reconstruction based on structured low-rank matrix completion

PURPOSE

A calibrationless parallel imaging reconstruction method, termed simultaneous autocalibrating and k-space estimation (SAKE), is presented. It is a data-driven, coil-by-coil reconstruction method that does not require a separate calibration step for estimating coil sensitivity information.

METHODS

In SAKE, an undersampled, multichannel dataset is structured into a single data matrix. The reconstruction is then formulated as a structured low-rank matrix completion problem. An iterative solution that implements a projection-onto-sets algorithm with singular value thresholding is described.

RESULTS

Reconstruction results are demonstrated for retrospectively and prospectively undersampled, multichannel Cartesian data having no calibration signals. Additionally, non-Cartesian data reconstruction is presented. Finally, improved image quality is demonstrated by combining SAKE with wavelet-based compressed sensing.

CONCLUSION

Because estimation of coil sensitivity information is not needed, the proposed method could potentially benefit MR applications where acquiring accurate calibration data is limiting or not possible at all.

Rapid single-breath-hold 3D late gadolinium enhancement cardiac MRI using a stack-of-spirals acquisition

PURPOSE

To develop a rapid single-breath-hold 3D late gadolinium enhancement (LGE) magnetic resonance imaging (MRI) method, and demonstrate its feasibility in cardiac patients.

MATERIALS AND METHODS

An inversion recovery dual-density 3D stack-of-spirals imaging sequence was developed. The spiral acquisition was 2-fold accelerated by self-consistent parallel imaging reconstruction (SPIRiT), which resulted in a total scan time of 12 heartbeats. Field map-based linear off-resonance correction was incorporated to the SPIRiT reconstruction. The 3D spiral LGE scans were performed in 15 patients who were referred for clinically ordered cardiac MR examinations that included the standard 2D multislice LGE imaging. Image sharpness and overall quality were qualitatively assessed based on 5-point scales.

RESULTS

Scar-induced hyper-LGE was identified in 4 out of the 15 patients by both 3D spiral and 2D multislice LGE tests. On average over all datasets (n = 15), the image sharpness scores were 3.9 (3D spiral) and 4.0 (2D multislice), and the image quality scores were 4.1 (3D spiral) and 4.0 (2D multislice) with no significant difference in both metrics (paired t-test; P > 0.1). The average scar contrast enhancement ratios were 0.72 and 0.75 in 3D and 2D images, respectively (n = 4). The average difference of fractional scar volumes measured in 3D and 2D images was 4.3% (n = 3).

CONCLUSION

Stack-of-spiral acquisition combined with non-Cartesian SPIRiT parallel imaging enables rapid 3D LGE MRI in a 12 heartbeat-long breath-hold.J.

High spatial and temporal resolution retrospective cine cardiovascular magnetic resonance from shortened free breathing real-time acquisitions

BACKGROUND

Cine cardiovascular magnetic resonance (CMR) is challenging in patients who cannot perform repeated breath holds. Real-time, free-breathing acquisition is an alternative, but image quality is typically inferior. There is a clinical need for techniques that achieve similar image quality to the segmented cine using a free breathing acquisition. Previously, high quality retrospectively gated cine images have been reconstructed from real-time acquisitions using parallel imaging and motion correction. These methods had limited clinical applicability due to lengthy acquisitions and volumetric measurements obtained with such methods have not previously been evaluated systematically.

METHODS

This study introduces a new retrospective reconstruction scheme for real-time cine imaging which aims to shorten the required acquisition. A real-time acquisition of 16-20s per acquired slice was inputted into a retrospective cine reconstruction algorithm, which employed non-rigid registration to remove respiratory motion and SPIRiT non-linear reconstruction with temporal regularization to fill in missing data. The algorithm was used to reconstruct cine loops with high spatial (1.3-1.8 × 1.8-2.1 mm²) and temporal resolution (retrospectively gated, 30 cardiac phases, temporal resolution 34.3 ± 9.1 ms). Validation was performed in 15 healthy volunteers using two different acquisition resolutions (256 × 144/192 × 128 matrix sizes). For each subject, 9 to 12 short axis and 3 long axis slices were imaged with both segmented and real-time acquisitions. The retrospectively reconstructed real-time cine images were compared to a traditional segmented breath-held acquisition in terms of image quality scores. Image quality scoring was performed by two experts using a scale between 1 and 5 (poor to good). For every subject, LAX and three SAX slices were selected and reviewed in the random order. The reviewers were blinded to the reconstruction approach and acquisition protocols and scores were given to segmented and retrospective cine series. Volumetric measurements of cardiac function were also compared by manually tracing the myocardium for segmented and retrospective cines.

RESULTS

Mean image quality scores were similar for short axis and long axis views for both tested resolutions. Short axis scores were 4.52/4.31 (high/low matrix sizes) for breath-hold vs. 4.54/4.56 for real-time (paired t-test, P = 0.756/0.011). Long axis scores were 4.09/4.37 vs. 3.99/4.29 (P = 0.475/0.463). Mean ejection fraction was 60.8/61.4 for breath-held acquisitions vs. 60.3/60.3 for real-time acquisitions (P = 0.439/0.093). No significant differences were seen in end-diastolic volume (P = 0.460/0.268) but there was a trend towards a small overestimation of end-systolic volume of 2.0/2.5 ml, which did not reach statistical significance (P = 0.052/0.083).

CONCLUSIONS

Real-time free breathing CMR can be used to obtain high quality retrospectively gated cine images in 16-20s per slice. Volumetric measurements and image quality scores were similar in images from breath-held segmented and free breathing, real-time acquisitions. Further speedup of image reconstruction is still needed.

High-frequency subband compressed sensing MRI using quadruplet sampling

PURPOSE

To present and validate a new method that formalizes a direct link between k-space and wavelet domains to apply separate undersampling and reconstruction for high- and low-spatial-frequency k-space data.

THEORY AND METHODS

High- and low-spatial-frequency regions are defined in k-space based on the separation of wavelet subbands, and the conventional compressed sensing problem is transformed into one of localized k-space estimation. To better exploit wavelet-domain sparsity, compressed sensing can be used for high-spatial-frequency regions, whereas parallel imaging can be used for low-spatial-frequency regions. Fourier undersampling is also customized to better accommodate each reconstruction method: random undersampling for compressed sensing and regular undersampling for parallel imaging.

RESULTS

Examples using the proposed method demonstrate successful reconstruction of both low-spatial-frequency content and fine structures in high-resolution three-dimensional breast imaging with a net acceleration of 11-12.

CONCLUSION

The proposed method improves the reconstruction accuracy of high-spatial-frequency signal content and avoids incoherent artifacts in low-spatial-frequency regions. This new formulation also reduces the reconstruction time due to the smaller problem size.

Clinical performance of contrast enhanced abdominal pediatric MRI with fast combined parallel imaging compressed sensing reconstruction

PURPOSE

To deploy clinically, a combined parallel imaging compressed sensing method with coil compression that achieves a rapid image reconstruction, and assess its clinical performance in contrast-enhanced abdominal pediatric MRI.

MATERIALS AND METHODS

With Institutional Review Board approval and informed patient consent/assent, 29 consecutive pediatric patients were recruited. Dynamic contrast-enhanced MRI was acquired on a 3 Tesla scanner using a dedicated 32-channel pediatric coil and a three-dimensional SPGR sequence, with pseudo-random undersampling at a high acceleration (R = 7.2). Undersampled data were reconstructed with three methods: a traditional parallel imaging method and a combined parallel imaging compressed sensing method with and without coil compression. The three sets of images were evaluated independently and blindly by two radiologists at one siting, for overall image quality and delineation of anatomical structures. Wilcoxon tests were performed to test the hypothesis that there was no significant difference in the evaluations, and interobserver agreement was analyzed.

RESULTS

Fast reconstruction with coil compression did not deteriorate image quality. The mean score of structural delineation of the fast reconstruction was 4.1 on a 5-point scale, significantly better (P < 0.05) than traditional parallel imaging (mean score 3.1). Fair to substantial interobserver agreement was reached in structural delineation assessment.

CONCLUSION

A fast combined parallel imaging compressed sensing method is feasible in a pediatric clinical setting. Preliminary results suggest it may improve structural delineation over parallel imaging.

Parallel imaging acceleration of spiral Fourier velocity encoded MRI using SPIRiT

This paper demonstrates parallel imaging acceleration of spiral Fourier velocity encoded MRI using the iterative self-consistent parallel imaging reconstruction (SPIRiT) technique. Magnitude images and time-velocity distributions obtained with image domain SPIRiT and sum-of-squares reconstruction are compared, for 2-fold and 4-fold undersampling. We show that SPIRiT is able to reduce spatial aliasing from undersampled time-velocity distributions, with good results for 2-fold undersampling, and moderately good results for 4-fold undersampling.

Monte Carlo SURE-based parameter selection for parallel magnetic resonance imaging reconstruction

PURPOSE

Regularizing parallel magnetic resonance imaging (MRI) reconstruction significantly improves image quality but requires tuning parameter selection. We propose a Monte Carlo method for automatic parameter selection based on Stein's unbiased risk estimate that minimizes the multichannel k-space mean squared error (MSE). We automatically tune parameters for image reconstruction methods that preserve the undersampled acquired data, which cannot be accomplished using existing techniques.

THEORY

We derive a weighted MSE criterion appropriate for data-preserving regularized parallel imaging reconstruction and the corresponding weighted Stein's unbiased risk estimate. We describe a Monte Carlo approximation of the weighted Stein's unbiased risk estimate that uses two evaluations of the reconstruction method per candidate parameter value.

METHODS

We reconstruct images using the denoising sparse images from GRAPPA using the nullspace method (DESIGN) and L1 iterative self-consistent parallel imaging (L1 -SPIRiT). We validate Monte Carlo Stein's unbiased risk estimate against the weighted MSE. We select the regularization parameter using these methods for various noise levels and undersampling factors and compare the results to those using MSE-optimal parameters.

RESULTS

Our method selects nearly MSE-optimal regularization parameters for both DESIGN and L1 -SPIRiT over a range of noise levels and undersampling factors.

CONCLUSION

The proposed method automatically provides nearly MSE-optimal choices of regularization parameters for data-preserving nonlinear parallel MRI reconstruction methods.

Highly-accelerated Bloch-Siegert |B1+| mapping using joint autocalibrated parallel image reconstruction

PURPOSE

To reconstruct accurate single- and multichannel Bloch-Siegert transmit radiofrequency (|B(1)(+)|) field maps from highly accelerated data.

THEORY AND METHODS

The approach is based on the fact that the |B(1)(+)|-to-phase encoding pulse for each transmit coil and off-resonance frequency applies a unique phase shift to the same underlying image. This enables joint reconstruction of all images in a Bloch-Siegert acquisition from an augmented set of virtual receive coils, using any autocalibrated parallel imaging reconstruction method.

RESULTS

Simulations with an eight channel transmit/receive array head coil at 7T show that accurate |B(1)(+)| maps can be produced at acceleration factors of 16× and 6× for Cartesian and spiral sampling, respectively. A phantom experiment with a six channel transverse electromagnetic (TEM) transceive array coil allowed accurate reconstruction at 16× acceleration. 7T in vivo experiments performed using 32 channel receive and two-channel transmit coils further demonstrate the proposed method's ability to produce high-quality |B(1)(+)| maps at accelerations of 32× and 8× for Cartesian and spiral trajectories, respectively. Reconstruction accuracy is improved using disjoint k-space sampling patterns between acquisitions.

CONCLUSION

The proposed approach allows high acceleration factors in Bloch-Siegert |B(1)(+)| mapping and can significantly reduce the scan time requirements for mapping the |B(1)(+)| fields of transmit arrays.

Sparsity-promoting calibration for GRAPPA accelerated parallel MRI reconstruction

The amount of calibration data needed to produce images of adequate quality can prevent auto-calibrating parallel imaging reconstruction methods like generalized autocalibrating partially parallel acquisitions (GRAPPA) from achieving a high total acceleration factor. To improve the quality of calibration when the number of auto-calibration signal (ACS) lines is restricted, we propose a sparsity-promoting regularized calibration method that finds a GRAPPA kernel consistent with the ACS fit equations that yields jointly sparse reconstructed coil channel images. Several experiments evaluate the performance of the proposed method relative to unregularized and existing regularized calibration methods for both low-quality and underdetermined fits from the ACS lines. These experiments demonstrate that the proposed method, like other regularization methods, is capable of mitigating noise amplification, and in addition, the proposed method is particularly effective at minimizing coherent aliasing artifacts caused by poor kernel calibration in real data. Using the proposed method, we can increase the total achievable acceleration while reducing degradation of the reconstructed image better than existing regularized calibration methods.

High spatial and temporal resolution dynamic contrast-enhanced magnetic resonance angiography using compressed sensing with magnitude image subtraction

PURPOSE

We propose a compressed-sensing (CS) technique based on magnitude image subtraction for high spatial and temporal resolution dynamic contrast-enhanced MR angiography (CE-MRA).

METHODS

Our technique integrates the magnitude difference image into the CS reconstruction to promote subtraction sparsity. Fully sampled Cartesian 3D CE-MRA datasets from 6 volunteers were retrospectively under-sampled and three reconstruction strategies were evaluated: k-space subtraction CS, independent CS, and magnitude subtraction CS. The techniques were compared in image quality (vessel delineation, image artifacts, and noise) and image reconstruction error. Our CS technique was further tested on seven volunteers using a prospectively under-sampled CE-MRA sequence.

RESULTS

Compared with k-space subtraction and independent CS, our magnitude subtraction CS provides significantly better vessel delineation and less noise at 4× acceleration, and significantly less reconstruction error at 4× and 8× (P < 0.05 for all). On a 1-4 point image quality scale in vessel delineation, our technique scored 3.8 ± 0.4 at 4×, 2.8 ± 0.4 at 8×, and 2.3 ± 0.6 at 12× acceleration. Using our CS sequence at 12× acceleration, we were able to acquire dynamic CE-MRA with higher spatial and temporal resolution than current clinical TWIST protocol while maintaining comparable image quality (2.8 ± 0.5 vs. 3.0 ± 0.4, P = NS).

CONCLUSION

Our technique is promising for dynamic CE-MRA.

Real-time 3D magnetic resonance imaging of the pharyngeal airway in sleep apnea

PURPOSE

To investigate the feasibility of real-time 3D magnetic resonance imaging (MRI) with simultaneous recording of physiological signals for identifying sites of airway obstruction during natural sleep in pediatric patients with sleep-disordered breathing.

METHODS

Experiments were performed using a three-dimensional Fourier transformation (3DFT) gradient echo sequence with prospective undersampling based on golden-angle radial spokes, and L1-norm regularized iterative self-consistent parallel imaging (L1-SPIRiT) reconstruction. This technique was demonstrated in three healthy adult volunteers and five pediatric patients with sleep-disordered breathing. External airway occlusion was used to induce partial collapse of the upper airway on inspiration and test the effectiveness of the proposed imaging method. Apneic events were identified using information available from synchronized recording of mask pressure and respiratory effort.

RESULTS

Acceptable image quality was obtained in seven of eight subjects. Temporary airway collapse induced via inspiratory loading was successfully imaged in all three volunteers, with average airway volume reductions of 63.3%, 52.5%, and 33.7%. Central apneic events and associated airway narrowing/closure were identified in two pediatric patients. During central apneic events, airway obstruction was observed in the retropalatal region in one pediatric patient.

CONCLUSION

Real-time 3D MRI of the pharyngeal airway with synchronized recording of physiological signals is feasible and may provide valuable information about the sites and nature of airway narrowing/collapse during natural sleep.

Non-contrast-enhanced renal and abdominal MR angiography using velocity-selective inversion preparation

Non-contrast-enhanced MR angiography is a promising alternative to the established contrast-enhanced approach as it reduces patient discomfort and examination costs and avoids the risk of nephrogenic systemic fibrosis. Inflow-sensitive slab-selective inversion recovery imaging has been used with great promise, particularly for abdominal applications, but has limited craniocaudal coverage due to inflow time constraints. In this work, a new non-contrast-enhanced MR angiography method using velocity-selective inversion preparation is developed and applied to renal and abdominal angiography. Based on the excitation k-space formalism and Shinnar-Le-Roux transform, a velocity-selective excitation pulse is designed that inverts stationary tissues and venous blood while preserving inferiorly flowing arterial blood. As the magnetization of the arterial blood in the abdominal aorta and iliac arteries is well preserved during the magnetization preparation, artery visualization over a large abdominal field of view is achievable with an inversion delay time that is chosen for optimal background suppression. Healthy volunteer tests demonstrate that the proposed method significantly increases the extent of visible arteries compared with the slab-selective approach, covering renal arteries through iliac arteries over a craniocaudal field of view of 340 mm.

Suppressing multi-channel ultra-low-field MRI measurement noise using data consistency and image sparsity

Ultra-low-field (ULF) MRI (B₀ = 10–100 µT) typically suffers from a low signal-to-noise ratio (SNR). While SNR can be improved by pre-polarization and signal detection using highly sensitive superconducting quantum interference device (SQUID) sensors, we propose to use the inter-dependency of the k-space data from highly parallel detection with up to tens of sensors readily available in the ULF MRI in order to suppress the noise. Furthermore, the prior information that an image can be sparsely represented can be integrated with this data consistency constraint to further improve the SNR. Simulations and experimental data using 47 SQUID sensors demonstrate the effectiveness of this data consistency constraint and sparsity prior in ULF-MRI reconstruction.

MRI of fast-relaxing spins

MR imaging of extremely fast-relaxing spins is currently a topic of much interest due to recent technical innovations and groundbreaking studies demonstrating its utility in biomedical research applications. From a technical perspective, this article examines the different classes of pulse sequences currently available to image spins with ultra-short transverse relaxation times (T2 and T2*), with particular attention focused on the newest member of the class, sweep imaging with Fourier transformation (SWIFT).

Accelerated MR diffusion tensor imaging using distributed compressed sensing

PURPOSE

Diffusion tensor imaging (DTI) is known to suffer from long acquisition time in the orders of several minutes or even hours. Therefore, a feasible way to accelerate DTI data acquisition is highly desirable. In this article, the feasibility and efficacy of distributed compressed sensing to fast DTI is investigated by exploiting the joint sparsity prior in diffusion-weighted images.

METHODS

Fully sampled DTI datasets were obtained from both simulated phantom and experimental heart sample, with diffusion gradient applied in six directions. The k-space data were undersampled retrospectively with acceleration factors from 2 to 6. Diffusion-weighted images were reconstructed by solving an l2-l1 norm minimization problem. Reconstruction performance with varied signal-to-noise ratio and acceleration factors were evaluated by root-mean-square error and maps of reconstructed DTI indices.

RESULTS

Superiority of distributed compressed sensing over basic compressed sensing was confirmed with simulation, and the reconstruction accuracy was influenced by signal-to-noise ratio and acceleration factors. Experimental results demonstrate that DTI indices including fractional anisotropy, mean diffusivities, and orientation of primary eigenvector can be obtained with high accuracy at acceleration factors up to 4.

CONCLUSION

Distributed compressed sensing is shown to be able to accelerate DTI and may be used to reduce DTI acquisition time practically.

MRI of fast-relaxing spins

MR imaging of extremely fast-relaxing spins is currently a topic of much interest due to recent technical innovations and groundbreaking studies demonstrating its utility in biomedical research applications. From a technical perspective, this article examines the different classes of pulse sequences currently available to image spins with ultra-short transverse relaxation times (T2 and T2*), with particular attention focused on the newest member of the class, sweep imaging with Fourier transformation (SWIFT).

Accelerated T2*-compensated fat fraction quantification using a joint parallel imaging and compressed sensing framework

PURPOSE

To develop a T2*-compensated parallel imaging and compressed sensing framework for water-fat separation, and to demonstrate accelerated quantitative imaging of proton density fat fraction.

MATERIALS AND METHODS

The proposed method extends a previously developed framework for water-fat separation by additionally compensating for T2* decay. A two-stage estimation was formulated that first determines an approximation of the B0 field map and then jointly estimates and refines the R2* (=1/T2*) and B0 field maps, respectively. The method was tested using a set of water-fat phantoms as well as liver datasets that were acquired from seven asymptomatic adult volunteers. The fat fraction estimates were compared to those from a commonly used nonaccelerated water-fat imaging method and also to a sequential parallel imaging and water-fat imaging method.

RESULTS

The proposed method properly compensated for T2* decay to yield accurate fat fraction estimates in the water-fat phantoms. Further, linear regression analysis from the liver datasets showed that the proposed method accurately estimated fat fraction at acceleration factors that were higher than those achievable by the sequential parallel imaging and water-fat imaging method. Accurate fat fraction estimates were demonstrated at acceleration factors up to 4×, although some image artifacts were observed.

CONCLUSION

The proposed T2*-compensated parallel imaging and compressed sensing framework demonstrates the potential to further accelerate water-fat imaging while maintaining accurate estimates of proton density fat fraction.

Chemical shift encoded water-fat separation using parallel imaging and compressed sensing

Chemical shift encoded techniques have received considerable attention recently because they can reliably separate water and fat in the presence of off-resonance. The insensitivity to off-resonance requires that data be acquired at multiple echo times, which increases the scan time as compared to a single echo acquisition. The increased scan time often requires that a compromise be made between the spatial resolution, the volume coverage, and the tolerance to artifacts from subject motion. This work describes a combined parallel imaging and compressed sensing approach for accelerated water-fat separation. In addition, the use of multiscale cubic B-splines for B(0) field map estimation is introduced. The water and fat images and the B(0) field map are estimated via an alternating minimization. Coil sensitivity information is derived from a calculated k-space convolution kernel and l(1)-regularization is imposed on the coil-combined water and fat image estimates. Uniform water-fat separation is demonstrated from retrospectively undersampled data in the liver, brachial plexus, ankle, and knee as well as from a prospectively undersampled acquisition of the knee at 8.6x acceleration.