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

SANTIS: Sampling-Augmented Neural neTwork with Incoherent Structure for MR image reconstruction

PURPOSE

To develop and evaluate a novel deep learning-based reconstruction framework called SANTIS (Sampling-Augmented Neural neTwork with Incoherent Structure) for efficient MR image reconstruction with improved robustness against sampling pattern discrepancy.

METHODS

With a combination of data cycle-consistent adversarial network, end-to-end convolutional neural network mapping, and data fidelity enforcement for reconstructing undersampled MR data, SANTIS additionally utilizes a sampling-augmented training strategy by extensively varying undersampling patterns during training, so that the network is capable of learning various aliasing structures and thereby removing undersampling artifacts more effectively and robustly. The performance of SANTIS was demonstrated for accelerated knee imaging and liver imaging using a Cartesian trajectory and a golden-angle radial trajectory, respectively. Quantitative metrics were used to assess its performance against different references. The feasibility of SANTIS in reconstructing dynamic contrast-enhanced images was also demonstrated using transfer learning.

RESULTS

Compared to conventional reconstruction that exploits image sparsity, SANTIS achieved consistently improved reconstruction performance (lower errors and greater image sharpness). Compared to standard learning-based methods without sampling augmentation (e.g., training with a fixed undersampling pattern), SANTIS provides comparable reconstruction performance, but significantly improved robustness, against sampling pattern discrepancy. SANTIS also achieved encouraging results for reconstructing liver images acquired at different contrast phases.

CONCLUSION

By extensively varying undersampling patterns, the sampling-augmented training strategy in SANTIS can remove undersampling artifacts more robustly. The novel concept behind SANTIS can particularly be useful for improving the robustness of deep learning-based image reconstruction against discrepancy between training and inference, an important, but currently less explored, topic.

Applications of magnetic resonance imaging in chemical engineering

While there are many techniques to study phenomena that occur in chemical engineering applications, magnetic resonance imaging (MRI) receives increasing scientific interest. Its non-invasive nature and wealth of parameters with the ability to generate functional images and contrast favors the use of MRI for many purposes, in particular investigations of dynamic phenomena, since it is very sensitive to motion. Recent progress in flow-MRI has led to shorter acquisition times and enabled studies of transient phenomena. Reactive systems can easily be imaged if NMR parameters such as relaxation change along the reaction coordinate. Moreover, materials and devices can be examined, such as batteries by mapping the magnetic field around them.

Self-calibrating wave-encoded 3D turbo spin echo imaging using subspace model based autofocusing

PURPOSE

To develop a self-calibrating approach for the estimation of wave point spread function (PSF) and coil sensitivities from the subsampled wave-encoded k-space, and evaluate its performance for wave-encoded 3D turbo spin echo (TSE) imaging.

METHODS

A low rank subspace parametric model was demonstrated in simulation to improve the representation for practical wave encoding k-space trajectories with aperiodicity, and an autofocus metric for the entire imaging volume was used to calibrate the wave PSF in a 2-stage manner from coarse to refined estimation. The coil sensitivities can be extracted from the shifted central region of wave PSF corrected subsampled k-space, and further used with wave PSF for wave-encoded parallel imaging (PI) reconstruction. The wave encoding gradients were integrated into the 3D TSE sequence considering eddy current reduction aspects and maintaining of the Carr-Purcell-Meiboom-Gill condition. Phantom and in vivo brain experiments were performed to evaluate the accuracy of wave PSF self-calibration and to compare the PI reconstruction performance between wave and Cartesian encoding scheme.

RESULTS

The self-calibrated wave PSF, estimated from different k-space undersampling patterns can robustly correct the wave encoding induced image artifacts given sufficient central autocalibration data. The self-calibrating wave-encoded PI reconstruction has demonstrated its improved performance in reduced aliasing artifacts and noise amplification in comparison to the Cartesian-encoded PI reconstruction results for 3D TSE imaging.

CONCLUSION

The proposed self-calibrating wave-encoded method allows robust calibration of wave PSF and coil sensitivities from the subsampled k-space, and improves the overall image quality for accelerated 3D TSE imaging.

Non-Cartesian slice-GRAPPA and slice-SPIRiT reconstruction methods for multiband spiral cardiac MRI

PURPOSE

Spiral MRI has advantages for cardiac imaging, and multiband (MB) spiral MRI of the heart shows promise. However, current reconstruction methods for MB spiral imaging have limitations. We sought to develop improved reconstruction methods for MB spiral cardiac MRI.

METHODS

Two reconstruction methods were developed. The first is non-Cartesian slice-GRAPPA (NCSG), which uses phase demodulation and gridding operations before application of a Cartesian slice-separating kernel. The second method, slice-SPIRiT, formulates the reconstruction as a minimization problem that enforces in-plane coil consistency and consistency with the acquired MB data, and uses through-plane coil sensitivity information in the iterative solution. These methods were compared with conjugate-gradient SENSE in phantoms and volunteers. Temporal alternation of CAIPIRINHA (controlled aliasing in parallel imaging results in higher acceleration) phase and the use of a temporal filter were also investigated.

RESULTS

Phantom experiments with 3 simultaneous slices (MB = 3) showed that mean artifact power was highest for conjugate-gradient SENSE, lower for NCSG, and lowest for slice-SPIRiT. For volunteer cine imaging (MB = 3, N = 5), the artifact power was 0.182 ± 0.037, 0.148 ± 0.036, and 0.139 ± 0.034 for conjugate-gradient SENSE, NCSG, and slice-SPIRiT, respectively (P < .05, analysis of variance). Temporal alternation of CAIPIRINHA reduced artifacts for both NCSG and slice-SPIRiT.

CONCLUSION

The NCSG and slice-SPIRiT methods provide more accurate reconstructions for MB spiral cine imaging compared with conjugate-gradient SENSE. These methods hold promise for non-Cartesian MB imaging.

A 3D k-space Fourier encoding and reconstruction framework for simultaneous multi-slab acquisition

PURPOSE

To propose a novel 3D k-space Fourier encoding and reconstruction framework for simultaneous multi-slab (SMSlab) acquisition and demonstrate its efficacy in high-resolution imaging.

METHODS

First, it is illustrated in theory how the inter-slab gap interferes with the formation of the SMSlab 3D k-space. Then, joint RF and gradient encoding are applied to remove the inter-slab gap interference and form a SMSlab 3D k-space. In vivo experiments are performed to validate the proposed theory. Acceleration in the proposed SMSlab 3D k-space is also evaluated.

RESULTS

High-resolution (1.0 mm isotropic) images can be reconstructed using the proposed SMSlab 3D framework. Controlled aliasing in parallel imaging sampling and 2D GRAPPA reconstruction can also be applied in the SMSlab 3D k-space. Compared with conventional multi-slab acquisition, SMSlab exhibits better SNR maintainability (such as lower g-factors), especially at high acceleration factors.

CONCLUSION

It is demonstrated that the joint application of RF and gradient encoding enables SMSlab within a 3D Fourier encoding framework. Images with high isotropic resolution can be reconstructed, and further acceleration is also applicable. The proposed SMSlab 3D k-space can be valuable for both high-resolution and high-efficiency diffusion and functional MRI.

Improving GRAPPA reconstruction using joint nonlinear kernel mapped and phase conjugated virtual coils

To improve the reconstruction condition and alleviate the noise amplification of GRAPPA reconstruction by aggregating the phase conjugated and nonlinear kernel mapped coils with the original physical coil. Nonlinear GRAPPA (NL-GRAPPA) is a kernel-based non-iterative approach which can reduce noise-induced error in GRAPPA reconstruction. And virtual conjugate coil (VCC) embeds the conjugate symmetric property of k-space into GRAPPA data synthesis to improve reconstruction condition. This work proposed NL-VCC-GRAPPA to jointly utilize the nonlinear mapped virtual coil and phase conjugated virtual coil to further reduce noise amplification in parallel imaging. In vivo static and dynamic 2D imaging accelerated by uniform undersampling schemes were performed to evaluate the proposed method in terms of visual image quality, root-mean-square-error (RMSE), and geometry factor (g-factor). The effects of acceleration factors, calibration data size and kernel shape on the proposed model were also separately analyzed and discussed. The proposed method illustrated improved visual image quality evidenced by reduced retrospective RMSE and prospective g-factor comparing with conventional GRAPPA and the recently proposed iterative SENSE-LORAKS reconstructions. Although a larger amount of calibration data and smaller kernel size were required to stabilize the calibration of fourfold extended kernel for the proposed method, it was non-iterative and relatively insensitive to parameter adjustment in the applications. The proposed NL-VCC-extension to conventional GRAPPA brings visible improvements for imaging scenarios accelerated by the widely available uniform undersampling schemes in a practically efficient manner without iteration.

ACCELERATED CORONARY MRI USING 3D SPIRIT-RAKI WITH SPARSITY REGULARIZATION

Coronary MRI is a non-invasive radiation-free imaging tool for the diagnosis of coronary artery disease. One of its limitations is the long scan time, due to the need for high resolution imaging in the presence of respiratory and cardiac motions. Machine learning (ML) methods have been recently utilized to accelerate MRI. In particular, a scan-specific ML technique, called Robust Artifical-neural-network for k-space Interpolation (RAKI) has shown promise in cardiac MRI. However, it requires uniform undersampling. In this study, we sought to extend this approach to arbitrary sampling patterns, using coil self-consistency. This technique, called SPIRiT-RAKI, utilizes scan-specific convolutional neural networks to nonlinearly enforce coil self-consistency. Additionally, regularization terms can also be incorporated. SPIRiT-RAKI was used to accelerate right coronary MRI. Reconstructions were compared to SPIRiT for different undersampling patterns and acceleration rates. Results show SPIRiT-RAKI reduces residual aliasing and blurring artifacts compared to SPIRiT.

Projection onto Epigraph Sets for Rapid Self-Tuning Compressed Sensing MRI

The compressed sensing (CS) framework leverages the sparsity of MR images to reconstruct from the undersampled acquisitions. CS reconstructions involve one or more regularization parameters that weigh sparsity in transform domains against fidelity to acquired data. While parameter selection is critical for reconstruction quality, the optimal parameters are subject and dataset specific. Thus, commonly practiced heuristic parameter selection generalizes poorly to independent datasets. Recent studies have proposed to tune parameters by estimating the risk of removing significant image coefficients. Line searches are performed across the parameter space to identify the parameter value that minimizes this risk. Although effective, these line searches yield prolonged reconstruction times. Here, we propose a new self-tuning CS method that uses computationally efficient projections onto epigraph sets of the l₁ and total-variation norms to simultaneously achieve parameter selection and regularization. In vivo demonstrations are provided for balanced steady-state free precession, time-of-flight, and T1-weighted imaging. The proposed method achieves an order of magnitude improvement in computational efficiency over line-search methods while maintaining near-optimal parameter selection.

Statistically Segregated k-Space Sampling for Accelerating Multiple-Acquisition MRI

A central limitation of multiple-acquisition magnetic resonance imaging (MRI) is the degradation in scan efficiency as the number of distinct datasets grows. Sparse recovery techniques can alleviate this limitation via randomly undersampled acquisitions. A frequent sampling strategy is to prescribe for each acquisition a different random pattern drawn from a common sampling density. However, naive random patterns often contain gaps or clusters across the acquisition dimension that, in turn, can degrade reconstruction quality or reduce scan efficiency. To address this problem, a statistically segregated sampling method is proposed for multiple-acquisition MRI. This method generates multiple patterns sequentially while adaptively modifying the sampling density to minimize k-space overlap across patterns. As a result, it improves incoherence across acquisitions while still maintaining similar sampling density across the radial dimension of k-space. Comprehensive simulations and in vivo results are presented for phase-cycled balanced steady-state free precession and multi-echo [Formula: see text]-weighted imaging. Segregated sampling achieves significantly improved quality in both Fourier and compressed-sensing reconstructions of multiple-acquisition datasets.

Sparsity and locally low rank regularization for MR fingerprinting

PURPOSE

Develop a sparse and locally low rank (LLR) regularized reconstruction to accelerate MR fingerprinting (MRF).

METHODS

Recent works have introduced low rank reconstructions to MRF, based on temporal compression operators learned from the MRF dictionary. In other MR applications, LLR regularization has been introduced to exploit temporal redundancy in local regions of the image. Here, we propose to include spatial sparsity and LLR regularization terms in the MRF reconstruction. This approach, so called SLLR-MRF, further reduces aliasing in the time-point images and enables higher acceleration factors. The proposed approach was evaluated in simulations, T1 /T2 phantom acquisition, and in vivo brain acquisitions in 5 healthy subjects with different undersampling factors. Acceleration was also used in vivo to enable acquisitions with higher in-plane spatial resolution in comparable scan time.

RESULTS

Simulations, phantom, and in vivo results show that low rank MRF reconstructions with high acceleration factors (<875 time-point images, 1 radial spoke per time-point) have residual aliasing artifacts that propagate into the parametric maps. The artifacts are reduced with the proposed SLLR-MRF resulting in considerable improvements in precision, without changes in accuracy. In vivo results show improved parametric maps for the proposed SLLR-MRF, potentially enabling MRF acquisitions with 1 radial spoke per time-point in approximately 2.6 s (~600 time-point images) for 2 × 2 mm and 9.6 s (1750 time-point images) for 1 × 1 mm in-plane resolution.

CONCLUSION

The proposed SLLR-MRF reconstruction further improves parametric map quality compared with low rank MRF, enabling shorter scan times and/or increased spatial resolution.

High-dimensionality undersampled patch-based reconstruction (HD-PROST) for accelerated multi-contrast MRI

PURPOSE

To develop a new high-dimensionality undersampled patch-based reconstruction (HD-PROST) for highly accelerated 2D and 3D multi-contrast MRI.

METHODS

HD-PROST jointly reconstructs multi-contrast MR images by exploiting the highly redundant information, on a local and non-local scale, and the strong correlation shared between the multiple contrast images. This is achieved by enforcing multi-dimensional low-rank in the undersampled images. 2D magnetic resonance fingerprinting (MRF) phantom and in vivo brain acquisitions were performed to evaluate the performance of HD-PROST for highly accelerated simultaneous T1 and T2 mapping. Additional in vivo experiments for reconstructing multiple undersampled 3D magnetization transfer (MT)-weighted images were conducted to illustrate the impact of HD-PROST for high-resolution multi-contrast 3D imaging.

RESULTS

In the 2D MRF phantom study, HD-PROST provided accurate and precise estimation of the T1 and T2 values in comparison to gold standard spin echo acquisitions. HD-PROST achieved good quality maps for the in vivo 2D MRF experiments in comparison to conventional low-rank inversion reconstruction. T1 and T2 values of white matter and gray matter were in good agreement with those reported in the literature for MRF acquisitions with reduced number of time point images (500 time point images, ~2.5 s scan time). For in vivo MT-weighted 3D acquisitions (6 different contrasts), HD-PROST achieved similar image quality than the fully sampled reference image for an undersampling factor of 6.5-fold.

CONCLUSION

HD-PROST enables multi-contrast 2D and 3D MR images in a short acquisition time without compromising image quality. Ultimately, this technique may increase the potential of conventional parameter mapping.

Common Information Enhanced Reconstruction for Accelerated High-resolution Multi-shot Diffusion Imaging

PURPOSE

Multi-shot technique can effectively achieve high-resolution diffusion weighted images, but the acquisition time of multi-shot technique is prolonged, especially for multiple direction diffusion encoding. Thus, increasing acquisition efficiency is highly desirable for high-resolution diffusion tensor imaging (DTI). In this study, based on the assumption that different diffusion directions share the common information, image ratio constrained reconstruction (IRCR) combined with iterative self-consistent parallel imaging reconstruction (SPIRiT) is proposed to improve data sampling efficiency and image reconstruction fidelity for high-resolution DTI.

THEORY AND METHODS

The proposed reconstruction framework is named Common Information Enhanced Reconstruction (CIER). Inter-image correlation among different direction diffusion-weighted images is used through common information, which is an isotropic component and structure, for improving the performance of reconstruction. The framework consists of three steps. (i) Pre-processing: three intermediate multi-shot images, low-resolution composite image, high-resolution composite image and low-resolution diffusion weighted image, are generated based on the SPIRiT method. (ii) IRCR: the initial high-resolution diffusion weighted image is calculated from the images in step (i) based on that the ratio map between high-resolution images is approximated by the ratio map between the corresponding low-resolution images. (iii) Final SPIRiT reconstruction: the final image is generated with the image from IRCR as initialization by considering data consistency only in the SPIRiT calculation. A specific implementation based on multishot variable density spiral (VDS) DTI is used to demonstrate the method.

RESULTS

The proposed CIER method was compared with the traditional reconstruction methods, conjugate gradient SENSE (CG-SENSE), L1-regularized SPIRiT (L1-SPIRiT), and anisotropic-sparsity SPIRiT (AS-SPIRiT) in brain DTI at acceleration factors of 3 to 7. CIER provided better diffusion image quality than other methods shown by both qualitative and quantitative results, especially at higher undersampling acceleration factors.

CONCLUSION

CIER offers better diffusion image quality at higher undersampling acceleration factors for high-resolution DTI. Both qualitative and quantitative results prove that common information can be used to improve sampling efficiency and maintain the image quality of diffusion-weighted images.

A GRAPPA algorithm for arbitrary 2D/3D non-Cartesian sampling trajectories with rapid calibration

PURPOSE

GRAPPA is a popular reconstruction method for Cartesian parallel imaging, but is not easily extended to non-Cartesian sampling. We introduce a general and practical GRAPPA algorithm for arbitrary non-Cartesian imaging.

METHODS

We formulate a general GRAPPA reconstruction by associating a unique kernel with each unsampled k-space location with a distinct constellation, that is, local sampling pattern. We calibrate these generalized kernels using the Fourier transform phase shift property applied to fully gridded or separately acquired Cartesian Autocalibration signal (ACS) data. To handle the resulting large number of different kernels, we introduce a fast calibration algorithm based on nonuniform FFT (NUFFT) and adoption of circulant ACS boundary conditions. We applied our method to retrospectively under-sampled rotated stack-of-stars/spirals in vivo datasets, and to a prospectively under-sampled rotated stack-of-spirals functional MRI acquisition with a finger-tapping task.

RESULTS

We reconstructed all datasets without performing any trajectory-specific manual adaptation of the method. For the retrospectively under-sampled experiments, our method achieved image quality (i.e., error and g-factor maps) comparable to conjugate gradient SENSE (cg-SENSE) and SPIRiT. Functional activation maps obtained from our method were in good agreement with those obtained using cg-SENSE, but required a shorter total reconstruction time (for the whole time-series): 3 minutes (proposed) vs 15 minutes (cg-SENSE).

CONCLUSIONS

This paper introduces a general 3D non-Cartesian GRAPPA that is fast enough for practical use on today's computers. It is a direct generalization of original GRAPPA to non-Cartesian scenarios. The method should be particularly useful in dynamic imaging where a large number of frames are reconstructed from a single set of ACS data.

High-resolution lung MRI with Ultrashort-TE: 1.5 or 3 Tesla?

PURPOSE

To assess the influence of magnetic field strength and additionally of acquisition and reconstruction parameters on the quality of high-resolution lung MRI, using a prototype Ultrashort-TE (UTE) sequence.

MATERIALS AND METHODS

This prospective study received ethical approval and all participants provided written informed consent. From January to February 2018, images were obtained in 10 healthy volunteers at 1.5 T and 3 T with a prototypical free-breathing UTE spiral 3D-GRE sequence with volumetric interpolation (VIBE) sequence and near-millimeter resolution. Five sequences were acquired to assess the effects of magnetic field strength (1.5 vs 3 T), voxel resolution (1.2 vs 1.0mm³), number of spiral interleaves (464 vs 264) and iterative reconstruction (iterative self-consistent parallel imaging reconstruction [SPIRiT] versus Non-Uniform Fourier Transform [NUFFT]) on image quality. Image quality was assessed by two independent observers. They evaluated the proportion of detected airways from the trachea down to the subsegmental level and placed ROI in the lung parenchyma, airways and vessels to calculate signal-to noise (SNR) and contrast-to-noise (CNR) ratios. Continuous variables were expressed as mean ± standard deviation and were compared by t-test.

RESULTS

Nearly complete visualization of the segmental bronchi (94 ± 12 to 99 ± 3%) was obtained with all sequences. Acquisition at 3 T (p < 0.001), use of a fewer spiral interleaves (p < 0.001) and NUFFT reconstruction (p < 0.001) all resulted in a significantly lower visibility of the subsegmental bronchi, while a smaller voxel size improved their visibility (p = 0.001). SNR and CNR were significantly lower at 3 T (140.2 ± 19.9 vs 190.2 ± 34.8, p < 0.001; and 5.7 ± 2.4vs 10.8 ± 2.8, p < 0.001, respectively).

CONCLUSIONS

Using equivalent acquisition and reconstruction parameters, image quality was lower at 3 T than at 1.5 T with decreased visibility of the subsegmental bronchi and lower SNR and CNR values.

Free-breathing cine imaging with motion-corrected reconstruction at 3T using SPiral Acquisition with Respiratory correction and Cardiac Self-gating (SPARCS)

PURPOSE

To develop a continuous-acquisition cardiac self-gated spiral pulse sequence and a respiratory motion-compensated reconstruction strategy for free-breathing cine imaging.

METHODS

Cine data were acquired continuously on a 3T scanner for 8 seconds per slice without ECG gating or breath-holding, using a golden-angle gradient echo spiral pulse sequence. Cardiac motion information was extracted by applying principal component analysis on the gridded 8 × 8 k-space center data. Respiratory motion was corrected by rigid registration on each heartbeat. Images were reconstructed using a low-rank and sparse (L+S) technique. This strategy was evaluated in 37 healthy subjects and 8 subjects undergoing clinical cardiac MR studies. Image quality was scored (1-5 scale) in a blinded fashion by 2 experienced cardiologists. In 13 subjects with whole-heart coverage, left ventricular ejection fraction (LVEF) from SPiral Acquisition with Respiratory correction and Cardiac Self-gating (SPARCS) was compared to that from a standard ECG-gated breath-hold balanced steady-state free precession (bSSFP) cine sequence.

RESULTS

The self-gated signal was successfully extracted in all cases and demonstrated close agreement with the acquired ECG signal (mean bias, -0.22 ms). The mean image score across all subjects was 4.0 for reconstruction using the L+S model. There was good agreement between the LVEF derived from SPARCS and the gold-standard bSSFP technique.

CONCLUSION

SPARCS successfully images cardiac function without the need for ECG gating or breath-holding. With an 8-second data acquisition per slice, whole-heart cine images with clinically acceptable spatial and temporal resolution and image quality can be acquired in <90 seconds of free-breathing acquisition.

Accelerated interleaved spiral-IDEAL imaging of hyperpolarized 129 Xe for parametric gas exchange mapping in humans

PURPOSE

To demonstrate the feasibility of mapping gas exchange with single breath-hold hyperpolarized (HP) ¹²⁹ Xe in humans, acquiring parametric maps of lung physiology. The potential benefit of acceleration using parallel imaging for this application is also explored.

METHODS

Six healthy volunteers were scanned with a modified spiral-IDEAL sequence to acquire gas exchange-weighted images using a single dose of ¹²⁹ Xe. These images were fit with the model of xenon exchange (MOXE) on a voxel-wise basis calculating parametric maps of lung physiology, specifically: air-capillary barrier thickness (δ), alveolar septal thickness (d), capillary transit time (t_x ), pulmonary hematocrit (HCT), and alveolar surface area-to-volume ratio (SVR). An accelerated version of the sequence was also tested in subset of 4 volunteers and compared to the fully sampled (FS) results.

RESULTS

Mean image-wide values calculated from MOXE parametric maps derived from FS dissolved ¹²⁹ Xe spiral-IDEAL images were: δ = 0.89 ± 0.17 μm, d = 7.5 ± 0.5 μm, t_x = 1.1 ± 0.2s, HCT = 28.8 ± 2.3%, and SVR = 140 ± 16 cm^-1 , in good agreement with previously published values based on whole-lung spectroscopy of healthy human subjects. Parallel imaging sufficiently reduces artifacting in accelerated images, but increases disagreement with MOXE parameters derived from FS data with mean voxel-wise unsigned relative differences of: δ = 39 ± 9%, d = 22 ± 3%, t_x = 117 ± 43%, HCT = 11 ± 2%, and SVR = 31 ± 12%.

CONCLUSION

Dissolved HP ¹²⁹ Xe spiral-IDEAL imaging for gas exchange mapping is feasible in humans using a single breath-hold. Accelerated gas exchange mapping is also shown to be feasible but requires further improvements to increase quantitative accuracy.

Volumetric abdominal perfusion measurement using a pseudo-randomly sampled 3D fast-spin-echo (FSE) arterial spin labeling (ASL) sequence and compressed sensing reconstruction

PURPOSE

To improve image quality and spatial coverage for abdominal perfusion imaging by implementing an arterial spin labeling (ASL) sequence that combines variable-density 3D fast-spin-echo (FSE) with Cartesian trajectory and compressed-sensing (CS) reconstruction.

METHODS

A volumetric FSE sequence was modified to include background-suppressed pseudo-continuous ASL labeling and to support variable-density (VD) Poisson-disk sampling for acceleration. We additionally explored the benefits of center oversampling and variable outer k-space sampling. Fourteen healthy volunteers were scanned on a 3T scanner to test acceleration factors as well as the various sampling schemes described under synchronized-breathing to limit motion issues. A CS reconstruction was implemented using the BART toolbox to reconstruct perfusion-weighted ASL volumes, assessing the impact of acceleration, different reconstruction, and sampling strategies on image quality.

RESULTS

CS acceleration is feasible with ASL, and a strong renal perfusion signal could be observed even at very high acceleration rates (≈15). We have shown that ASL k-space complex subtraction was desirable before CS reconstruction. Although averaging of multiple highly accelerated images helped to reduce artifacts from physiologic fluctuations, superior image quality was achieved by interleaving of different highly undersampled pseudo-random spatial sampling patterns and using 4D-CS reconstruction. Combination of these enhancements produces high-quality ASL volumes in under 5 min.

CONCLUSIONS

High-quality isotropic ASL abdominal perfusion volumes can be obtained in healthy volunteers with a VD-FSE and CS reconstruction. This lays the groundwork for future developments toward whole abdomen free-breathing non-contrast perfusion imaging.

Compressed sensing MRI: a review from signal processing perspective

Magnetic resonance imaging (MRI) is an inherently slow imaging modality, since it acquires multi-dimensional k-space data through 1-D free induction decay or echo signals. This often limits the use of MRI, especially for high resolution or dynamic imaging. Accordingly, many investigators has developed various acceleration techniques to allow fast MR imaging. For the last two decades, one of the most important breakthroughs in this direction is the introduction of compressed sensing (CS) that allows accurate reconstruction from sparsely sampled k-space data. The recent FDA approval of compressed sensing products for clinical scans clearly reflect the maturity of this technology. Therefore, this paper reviews the basic idea of CS and how this technology have been evolved for various MR imaging problems.

Virtual slice concept for improved simultaneous multi-slice MRI employing an extended leakage constraint

PURPOSE

To develop a novel, simultaneous multi-slice (SMS) reconstruction that extends an inter-slice leakage constraint to intra-slice aliasing with a virtual slice concept for artifact reduction.

METHODS

Inter-slice leakage constraint has been used for SMS reconstruction that mitigates leakage artifacts from the adjacent slices. In this work, the leakage constraint is extended to more general framework that includes SMS and parallel MRI as special cases by viewing intra-slice aliasing artifacts from undersampling as virtual slices while imposing data fidelity to ensure the measurement consistency. In this way, the reconstruction makes it feasible to directly estimate the individual slices from the undersampled SMS acquisition as a one-step method. The performance of the extended method is evaluated with data acquired using 2D GRE and EPI sequences.

RESULTS

Compared to a two-step method that performs slice unaliasing followed by inplane unaliasing, the proposed one-step method reduces aliasing artifacts by employing the extended leakage constraint while lowering the noise amplification by improving the conditioning for the inverse problem.

CONCLUSIONS

The proposed one-step method takes advantage of virtual slices as additional encoding power for improved image quality. We successfully demonstrated that the proposed one-step method minimizes a trade-off between aliasing artifacts and amplified noises over the two-step method.

MANTIS: Model-Augmented Neural neTwork with Incoherent k-space Sampling for efficient MR parameter mapping

PURPOSE

To develop and evaluate a novel deep learning-based image reconstruction approach called MANTIS (Model-Augmented Neural neTwork with Incoherent k-space Sampling) for efficient MR parameter mapping.

METHODS

MANTIS combines end-to-end convolutional neural network (CNN) mapping, incoherent k-space undersampling, and a physical model as a synergistic framework. The CNN mapping directly converts a series of undersampled images straight into MR parameter maps using supervised training. Signal model fidelity is enforced by adding a pathway between the undersampled k-space and estimated parameter maps to ensure that the parameter maps produced synthesized k-space consistent with the acquired undersampling measurements. The MANTIS framework was evaluated on the T₂ mapping of the knee at different acceleration rates and was compared with 2 other CNN mapping methods and conventional sparsity-based iterative reconstruction approaches. Global quantitative assessment and regional T₂ analysis for the cartilage and meniscus were performed to demonstrate the reconstruction performance of MANTIS.

RESULTS

MANTIS achieved high-quality T₂ mapping at both moderate (R = 5) and high (R = 8) acceleration rates. Compared to conventional reconstruction approaches that exploited image sparsity, MANTIS yielded lower errors (normalized root mean square error of 6.1% for R = 5 and 7.1% for R = 8) and higher similarity (structural similarity index of 86.2% at R = 5 and 82.1% at R = 8) to the reference in the T₂ estimation. MANTIS also achieved superior performance compared to direct CNN mapping and a 2-step CNN method.

CONCLUSION

The MANTIS framework, with a combination of end-to-end CNN mapping, signal model-augmented data consistency, and incoherent k-space sampling, is a promising approach for efficient and robust estimation of quantitative MR parameters.

Unenhanced Velocity-Selective MR Angiography (VS-MRA): Initial Clinical Evaluation in Patients With Peripheral Artery Disease

BACKGROUND

Safe and accurate imaging of the peripheral arterial system is important for diagnosis and treatment planning of patients with peripheral artery disease (PAD).

PURPOSE

To evaluate image quality and diagnostic performance of unenhanced magnetic resonance angiography (MRA) based on velocity-selective (VS) magnetization preparation (termed VS-MRA).

STUDY TYPE

Prospective.

POPULATION

Thirty-one symptomatic PAD patients underwent VS-MRA. Twenty-four of them underwent clinical digital subtraction angiography (DSA) examination, 18.8 ± 5.2 days after the MR scans.

FIELD STRENGTH/SEQUENCE

1.5T MRI that included VS-MRA (homemade research sequence) and phase-contrast flow imaging (clinical sequence).

ASSESSMENT

Image quality (0: nondiagnostic, 3: excellent) and stenosis severity (0: normal, 3: occlusion) of VS-MRA images were assessed independently by three reviewers. Arterial signal-to-noise-ratio (SNR) and artery-to-muscle contrast-to-noise ratio (CNR) were calculated.

STATISTICAL TESTS

The sensitivity and specificity of VS-MRA were calculated for the detection of significant stenosis (>50%) with DSA as the reference standard. Interobserver agreement among the three reviewers was evaluated by using Cohen κ-statistics.

RESULTS

The image quality score of VS-MRA was 2.7 ± 0.5 for Reader 1, 2.8 ± 0.5 for Reader 2, and 2.8 ± 0.4 for Reader 3; SNR and CNR were 37.8 ± 12.5 and 30.5 ± 11.8, respectively. Segment-based analysis revealed that VS-MRA had sensitivities of 85.3%, 74.5%, and 78.4%, respectively, for the three reviewers, and specificities of 93.5%, 96.8%, and 95.2%. The interobserver agreement for the stenosis grading was good, as demonstrated by Cohen κ values of 0.76 (Reader 1 vs. Reader 2), 0.82 (Reader 1 vs. Reader 3), and 0.79 (Reader 2 vs. Reader 3).

DATA CONCLUSION

Unenhanced VS-MRA allows clear depiction of the peripheral arteries and accurate stenosis grading, as evidenced by high image quality scores and strong agreement with DSA.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:744-751.

Efficient Dynamic Parallel MRI Reconstruction for the Low-Rank Plus Sparse Model

The low-rank plus sparse (L+S) decomposition model enables the reconstruction of under-sampled dynamic parallel magnetic resonance imaging (MRI) data. Solving for the low-rank and the sparse components involves non-smooth composite convex optimization, and algorithms for this problem can be categorized into proximal gradient methods and variable splitting methods. This paper investigates new efficient algorithms for both schemes. While current proximal gradient techniques for the L+S model involve the classical iterative soft thresholding algorithm (ISTA), this paper considers two accelerated alternatives, one based on the fast iterative shrinkage-thresholding algorithm (FISTA), and the other with the recent proximal optimized gradient method (POGM). In the augmented Lagrangian (AL) framework, we propose an efficient variable splitting scheme based on the form of the data acquisition operator, leading to simpler computation than the conjugate gradient (CG) approach required by existing AL methods. Numerical results suggest faster convergence of the efficient implementations for both frameworks, with POGM providing the fastest convergence overall and the practical benefit of being free of algorithm tuning parameters.

Evaluation of compressed sensing MRI for accelerated bowel motility imaging

Background

To investigate the feasibility of compressed sensing and parallel imaging (CS-PI)-accelerated bowel motility magnetic resonance imaging (MRI) and to compare its image quality and diagnostic quality to conventional sensitivity encoding (SENSE) accelerated scans.

Methods

Bowel MRI was performed in six volunteers using a three-dimensional balanced fast field-echo sequence. Static scans were performed after the administration of a spasmolytic agent to prevent bowel motion artefacts. Fully sampled reference scans and multiple prospectively 3× to 7× undersampled CS-PI and SENSE scans were acquired. Additionally, fully sampled CS-PI and SENSE scans were retrospectively undersampled and reconstructed. Dynamic scans were performed using 5× to 7× accelerated scans in the presence of bowel motion. Retrospectively, undersampled scans were compared to fully sampled scans using structural similarity indices. All reconstructions were visually assessed for image quality and diagnostic quality by two radiologists.

Results

For static imaging, the performance of CS-PI was lower than that of fully sampled and SENSE scans: the diagnostic quality was assessed as adequate or good for 100% of fully sampled scans, 95% of SENSE, but only for 55% of CS-PI scans. For dynamic imaging, CS-PI image quality was scored similar to SENSE at high acceleration. Diagnostic quality of all scans was scored as adequate or good; 55% of CS-PI and 83% of SENSE scans were scored as good.

Conclusion

Compared to SENSE, current implementation of CS-PI performed less or equally good in terms of image quality and diagnostic quality. CS-PI did not show advantages over SENSE for three-dimensional bowel motility imaging.

Whole-heart spiral simultaneous multi-slice first-pass myocardial perfusion imaging

PURPOSE

To develop and evaluate a simultaneous multislice (SMS) spiral perfusion pulse sequence with whole-heart coverage.

METHODS

An orthogonal set of phase cycling angles following a Hadamard pattern was incorporated into a golden-angle (GA) variable density spiral perfusion sequence to perform SMS imaging at different multiband (MB) factors. Images were reconstructed using an SMS extension of L1-SPIRiT that we have termed SMS-L1-SPIRiT. The proposed sequence was evaluated in 40 subjects (10 each for MB factors of 1, 2, 3, and 4). Images were blindly graded by 2 cardiologists on a 5-point scale (5, excellent). To quantitatively evaluate the reconstruction performance against images acquired without SMS, the MB =1 data were used to retrospectively simulate data acquired at MB factors of 2 to 4.

RESULTS

Analysis of the SMS point-spread function for the desired slice showed that the proposed sampling strategy significantly canceled the main-lobe energy of the other slices and has low side-lobe energy resulting in an incoherent temporal aliasing pattern when rotated by the GA. Retrospective experiments demonstrated the SMS-L1-SPIRiT method removed aliasing from the interfering slices and showed excellent agreement with the ground-truth MB =1 images. Clinical evaluation demonstrated high-quality perfusion images with average image-quality scores of 4.3 ± 0.5 (MB =2), 4.2 ± 0.5 (MB =3), and 4.4 ± 0.4 (MB =4) with no significant quality difference in image quality between MB factors (P = 0.38).

CONCLUSION

SMS spiral perfusion at MB factors 2, 3, and 4 produces high-quality perfusion images with whole-heart coverage in a clinical setting with high sampling efficiency.

Eigenvector-based SPIRiT Parallel MR Imaging Reconstruction based on ℓp pseudo-norm Joint Total Variation

Parallel Magnetic Resonance (MR) imaging is a well-established acceleration technique based on the spatial sensitivities of array receivers. Eigenvector-based SPIRiT (ESPIRiT) is a new parallel MR imaging reconstruction method that combines the advantages of the SENSE and GRAPPA methods. It estimates multiple sets of the sensitivity maps from the calibration matrix that is constructed from the auto-calibration data. To improve the quality of the reconstructed image, we introduced the Total Variation (TV) and ℓ_p pseudo-norm Joint TV (ℓ_pJTV) regularization terms to the ESPIRiT model for parallel MR imaging reconstruction, which were solved by using the Operator Splitting (OS) method. The resulting denoising problems with the TV and ℓ_pJTV regularization terms were solved by exploiting the Majorization Minimization method. Simulation experiments on two in vivo data sets demonstrated that the proposed OS algorithm with the TV regularization term (OSTV) and OS algorithm with the ℓ_pJTV regularization term (OSℓ_pJTV) outperformed the conventional method with the ℓ₁ regularization term in terms of SNR and NRMSE. And the OSℓ_pJTV algorithm was slightly superior to the OSTV algorithm with the TV regularization term.

Accelerated multi-contrast high isotropic resolution 3D intracranial vessel wall MRI using a tailored k-space undersampling and partially parallel reconstruction strategy

OBJECTIVE

To develop a 3D multi-contrast IVW protocol with 0.5-mm isotropic resolution and a scan time of 5 min per sequence.

MATERIALS AND METHODS

Pre-contrast T1w VISTA, DANTE prepared PDw VISTA, SNAP, and post-contrast T1w VISTA were accelerated using cartesian undersampling with target ordering method (CUSTOM) and self-supporting tailored k-space estimation for parallel imaging reconstruction (STEP). CUSTOM + STEP IVW was compared to full-sample IVW, SENSE-accelerated IVW, and CUSTOM + zero-filled Fourier reconstruction in normal volunteers and subjects with intracranial atherosclerotic disease (ICAD). Image quality, vessel delineation, CSF suppression, and blood suppression were compared.

RESULTS

CUSTOM + STEP vessel wall delineation was comparable to full-sample IVW and better than SENSE IVW for vessel wall delineation on T1w VISTA and luminal contrast on SNAP. Average image quality and wall depiction were significantly improved using STEP reconstruction compared with zero-filled Fourier reconstruction, with no significant difference in CSF or blood suppression.

CONCLUSIONS

CUSTOM + STEP allowed multi-contrast 3D 0.5-mm isotropic IVW within 30 min. Although some quantitative and qualitative scores for CUSTOM - STEP were lower than fully sampled IVW, CUSTOM + STEP provided comparable vessel wall delineation as full-sample IVW and was superior to SENSE. CUSTOM + STEP IVW was well tolerated by patients and showed good delineation of ICAD plaque.

Incorporating reference guided priors into calibrationless parallel imaging reconstruction

PURPOSE

To propose and evaluate a new calibrationless parallel imaging method aimed at further improving the reconstruction accuracy of the accelerated multi-channel MR images.

METHOD

We introduce a new calibrationless parallel imaging method. On top of exploiting joint sparsity cross channels of the target image to be reconstructed, it incorporates similar priors on the grey-level intensity and edge orientation, which both come from a high-spatial resolution reference image that can be easily obtained in many clinical MRI scenarios. The mixed l₂-l₁ norm is used to enforce joint sparsity and a multi-scale gradient operator is applied to extract fine edges from the reference image. Additionally, this optimization problem can be solved via a non-linear conjugate gradient algorithm with line search in this work.

RESULTS

The proposed method is compared with the existing state-of-the-art auto-calibration and calibrationless parallel imaging techniques. The experiments on different in-vivo brain MR datasets show that the proposed method has the superior performance in terms of both artifact suppression and detail preservation.

CONCLUSION

The reference guided calibrationless parallel imaging method can significantly improve the performance of joint reconstruction of target channel images. Even when the reduction factor is high, it can keep edge structures well.

KerNL: Kernel-Based Nonlinear Approach to Parallel MRI Reconstruction

The conventional calibration-based parallel imaging method assumes a linear relationship between the acquired multi-channel k-space data and the unacquired missing data, where the linear coefficients are estimated using some auto-calibration data. In this paper, we first analyze the model errors in the conventional calibration-based methods and demonstrate the nonlinear relationship. Then, a much more general nonlinear framework is proposed for auto-calibrated parallel imaging. In this framework, kernel tricks are employed to represent the general nonlinear relationship between acquired and unacquired k-space data without increasing the computational complexity. Identification of the nonlinear relationship is still performed by solving linear equations. Experimental results demonstrate that the proposed method can achieve reconstruction quality superior to GRAPPA and NL-GRAPPA at high net reduction factors.

Scan-specific robust artificial-neural-networks for k-space interpolation (RAKI) reconstruction: Database-free deep learning for fast imaging

PURPOSE

To develop an improved k-space reconstruction method using scan-specific deep learning that is trained on autocalibration signal (ACS) data.

THEORY

Robust artificial-neural-networks for k-space interpolation (RAKI) reconstruction trains convolutional neural networks on ACS data. This enables nonlinear estimation of missing k-space lines from acquired k-space data with improved noise resilience, as opposed to conventional linear k-space interpolation-based methods, such as GRAPPA, which are based on linear convolutional kernels.

METHODS

The training algorithm is implemented using a mean square error loss function over the target points in the ACS region, using a gradient descent algorithm. The neural network contains 3 layers of convolutional operators, with 2 of these including nonlinear activation functions. The noise performance and reconstruction quality of the RAKI method was compared with GRAPPA in phantom, as well as in neurological and cardiac in vivo data sets.

RESULTS

Phantom imaging shows that the proposed RAKI method outperforms GRAPPA at high (≥4) acceleration rates, both visually and quantitatively. Quantitative cardiac imaging shows improved noise resilience at high acceleration rates (rate 4:23% and rate 5:48%) over GRAPPA. The same trend of improved noise resilience is also observed in high-resolution brain imaging at high acceleration rates.

CONCLUSION

The RAKI method offers a training database-free deep learning approach for MRI reconstruction, with the potential to improve many existing reconstruction approaches, and is compatible with conventional data acquisition protocols.

Motion-compensated reconstruction of magnetic resonance images from undersampled data

Magnetic resonance imaging of patients who find difficulty lying still or holding their breath can be challenging. Unresolved intra-frame motion yields blurring artifacts and limits spatial resolution. To correct for intra-frame non-rigid motion, such as in pediatric body imaging, this paper describes a multi-scale technique for joint estimation of the motion occurring during the acquisition and of the desired uncorrupted image. This technique regularizes the motion coefficients to enforce invertibility and minimize numerical instability. This multi-scale approach takes advantage of variable-density sampling patterns used in accelerated imaging to resolve large motion from a coarse scale. The resulting method improves image quality for a set of two-dimensional reconstructions from data simulated with independently generated deformations, with statistically significant increases in both peak signal to error ratio and structural similarity index. These improvements are consistent across varying undersampling factors and severities of motion and take advantage of the variable density sampling pattern.

Navigator-Free EPI Ghost Correction With Structured Low-Rank Matrix Models: New Theory and Methods

Structured low-rank matrix models have previously been introduced to enable calibrationless MR image reconstruction from sub-Nyquist data, and such ideas have recently been extended to enable navigator-free echo-planar imaging (EPI) ghost correction. This paper presents a novel theoretical analysis which shows that, because of uniform subsampling, the structured low-rank matrix optimization problems for EPI data will always have either undesirable or non-unique solutions in the absence of additional constraints. This theory leads us to recommend and investigate problem formulations for navigator-free EPI that incorporate side information from either image-domain or k-space domain parallel imaging methods. The importance of using nonconvex low-rank matrix regularization is also identified. We demonstrate using phantom and in vivo data that the proposed methods are able to eliminate ghost artifacts for several navigator-free EPI acquisition schemes, obtaining better performance in comparison with the state-of-the-art methods across a range of different scenarios. Results are shown for both single-channel acquisition and highly accelerated multi-channel acquisition.

Simultaneous auto-calibration and gradient delays estimation (SAGE) in non-Cartesian parallel MRI using low-rank constraints

PURPOSE

To correct gradient timing delays in non-Cartesian MRI while simultaneously recovering corruption-free auto-calibration data for parallel imaging, without additional calibration scans.

METHODS

The calibration matrix constructed from multi-channel k-space data should be inherently low-rank. This property is used to construct reconstruction kernels or sensitivity maps. Delays between the gradient hardware across different axes and RF receive chain, which are relatively benign in Cartesian MRI (excluding EPI), lead to trajectory deviations and hence data inconsistencies for non-Cartesian trajectories. These in turn lead to higher rank and corrupted calibration information which hampers the reconstruction. Here, a method named Simultaneous Auto-calibration and Gradient delays Estimation (SAGE) is proposed that estimates the actual k-space trajectory while simultaneously recovering the uncorrupted auto-calibration data. This is done by estimating the gradient delays that result in the lowest rank of the calibration matrix. The Gauss-Newton method is used to solve the non-linear problem. The method is validated in simulations using center-out radial, projection reconstruction and spiral trajectories. Feasibility is demonstrated on phantom and in vivo scans with center-out radial and projection reconstruction trajectories.

RESULTS

SAGE is able to estimate gradient timing delays with high accuracy at a signal to noise ratio level as low as 5. The method is able to effectively remove artifacts resulting from gradient timing delays and restore image quality in center-out radial, projection reconstruction, and spiral trajectories.

CONCLUSION

The low-rank based method introduced simultaneously estimates gradient timing delays and provides accurate auto-calibration data for improved image quality, without any additional calibration scans.

Fast GPU Implementation of a Scan-Specific Deep Learning Reconstruction for Accelerated Magnetic Resonance Imaging

RAKI is a novel fast MRI image reconstruction algorithm that has been recently proposed, which gives satisfying results for highly accelerated MRI. However, due to RAKI reconstruction depends on multiple convolutional neural networks, implementing RAKI reconstruction is a time-consuming task. In this study, we present accelerate strategies for RAKI implementation aided by GPU parallel programming. Aiming at the characteristics of RAKI, we limited the iteration number of solving optimization problems in the network training stage, while maintaining the reconstruction results are visually satisfying. Further more, according to the independence between multiple networks, we parallelized the training tasks by CPU multiprocessing, which maximizes the performance by fully utilizing GPU resources. According to our experiments, these efforts gave more than 60x speed up compared with conventional, sequential implementation. With the ability of completing RAKI reconstruction in minutes, we are able to bring RAKI into practical applications.

Ultrafast 3D Bloch-Siegert B <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msubsup><mml:mrow/> <mml:mn>1</mml:mn> <mml:mo>+</mml:mo></mml:msubsup> </mml:math> -mapping using variational modeling

PURPOSE

Highly accelerated B <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msubsup><mml:mrow/> <mml:mn>1</mml:mn> <mml:mo>+</mml:mo></mml:msubsup> </mml:math> -mapping based on the Bloch-Siegert shift to allow 3D acquisitions even within a brief period of a single breath-hold.

THEORY AND METHODS

The B <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msubsup><mml:mrow/> <mml:mn>1</mml:mn> <mml:mo>+</mml:mo></mml:msubsup> </mml:math> dependent Bloch-Siegert phase shift is measured within a highly subsampled 3D-volume and reconstructed using a two-step variational approach, exploiting the different spatial distribution of morphology and B <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msubsup><mml:mrow/> <mml:mn>1</mml:mn> <mml:mo>+</mml:mo></mml:msubsup> </mml:math> -field. By appropriate variable substitution the basic non-convex optimization problem is transformed in a sequential solution of two convex optimization problems with a total generalized variation (TGV) regularization for the morphology part and a smoothness constraint for the B <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msubsup><mml:mrow/> <mml:mn>1</mml:mn> <mml:mo>+</mml:mo></mml:msubsup> </mml:math> -field. The method is evaluated on 3D in vivo data with retro- and prospective subsampling. The reconstructed B <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msubsup><mml:mrow/> <mml:mn>1</mml:mn> <mml:mo>+</mml:mo></mml:msubsup> </mml:math> -maps are compared to a zero-padded low resolution reconstruction and a fully sampled reference.

RESULTS

The reconstructed B <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msubsup><mml:mrow/> <mml:mn>1</mml:mn> <mml:mo>+</mml:mo></mml:msubsup> </mml:math> -field maps are in high accordance to the reference for all measurements with a mean error below 1% and a maximum of about 4% for acceleration factors up to 100. The minimal error for different sampling patterns was achieved by sampling a dense region in k-space center with acquisition times of around 10-12 s for 3D-acquistions.

CONCLUSIONS

The proposed variational approach enables highly accelerated 3D acquisitions of Bloch-Siegert data and thus full liver coverage in a single breath hold.

Wave-LORAKS: Combining wave encoding with structured low-rank matrix modeling for more highly accelerated 3D imaging

PURPOSE

Wave-CAIPI is a novel acquisition approach that enables highly accelerated 3D imaging. This paper investigates the combination of Wave-CAIPI with LORAKS-based reconstruction (Wave-LORAKS) to enable even further acceleration.

METHODS

LORAKS is a constrained image reconstruction framework that can impose spatial support, smooth phase, sparsity, and/or parallel imaging constraints. LORAKS requires minimal prior information, and instead uses the low-rank subspace structure of the raw data to automatically learn which constraints to impose and how to impose them. Previous LORAKS implementations addressed 2D image reconstruction problems. In this work, several recent advances in structured low-rank matrix recovery were combined to enable large-scale 3D Wave-LORAKS reconstruction with improved quality and computational efficiency. Wave-LORAKS was investigated by retrospective subsampling of two fully sampled Wave-encoded 3D MPRAGE datasets, and comparisons were made against existing Wave reconstruction approaches.

RESULTS

Results show that Wave-LORAKS can yield higher reconstruction quality with 16×-accelerated data than is obtained by traditional Wave-CAIPI with 9×-accerated data.

CONCLUSIONS

There are strong synergies between Wave encoding and LORAKS, which enables Wave-LORAKS to achieve higher acceleration and more flexible sampling compared to Wave-CAIPI.

Improving parallel imaging by jointly reconstructing multi-contrast data

PURPOSE

To develop parallel imaging techniques that simultaneously exploit coil sensitivity encoding, image phase prior information, similarities across multiple images, and complementary k-space sampling for highly accelerated data acquisition.

METHODS

We introduce joint virtual coil (JVC)-generalized autocalibrating partially parallel acquisitions (GRAPPA) to jointly reconstruct data acquired with different contrast preparations, and show its application in 2D, 3D, and simultaneous multi-slice (SMS) acquisitions. We extend the joint parallel imaging concept to exploit limited support and smooth phase constraints through Joint (J-) LORAKS formulation. J-LORAKS allows joint parallel imaging from limited autocalibration signal region, as well as permitting partial Fourier sampling and calibrationless reconstruction.

RESULTS

We demonstrate highly accelerated 2D balanced steady-state free precession with phase cycling, SMS multi-echo spin echo, 3D multi-echo magnetization-prepared rapid gradient echo, and multi-echo gradient recalled echo acquisitions in vivo. Compared to conventional GRAPPA, proposed joint acquisition/reconstruction techniques provide more than 2-fold reduction in reconstruction error.

CONCLUSION

JVC-GRAPPA takes advantage of additional spatial encoding from phase information and image similarity, and employs different sampling patterns across acquisitions. J-LORAKS achieves a more parsimonious low-rank representation of local k-space by considering multiple images as additional coils. Both approaches provide dramatic improvement in artifact and noise mitigation over conventional single-contrast parallel imaging reconstruction. Magn Reson Med 80:619-632, 2018. © 2018 International Society for Magnetic Resonance in Medicine.

Real-time cardiac MRI with radial acquisition and k-space variant reduced-FOV reconstruction

This work aims to demonstrate that radial acquisition with k-space variant reduced-FOV reconstruction can enable real-time cardiac MRI with an affordable computation cost. Due to non-uniform sampling, radial imaging requires k-space variant reconstruction for optimal performance. By converting radial parallel imaging reconstruction into the estimation of correlation functions with a previously-developed correlation imaging framework, Cartesian k-space may be reconstructed point-wisely based on parallel imaging relationship between every Cartesian datum and its neighboring radial samples. Furthermore, reduced-FOV correlation functions may be used to calculate a subset of Cartesian k-space data for image reconstruction within a small region of interest, making it possible to run real-time cardiac MRI with an affordable computation cost. In a stress cardiac test where the subject is imaged during biking with a heart rate of >100 bpm, this k-space variant reduced-FOV reconstruction is demonstrated in reference to several radial imaging techniques including gridding, GROG and SPIRiT. It is found that the k-space variant reconstruction outperforms gridding, GROG and SPIRiT in real-time imaging. The computation cost of reduced-FOV reconstruction is ~2 times higher than that of GROG. The presented work provides a practical solution to real-time cardiac MRI with radial acquisition and k-space variant reduced-FOV reconstruction in clinical settings.

Kernel Principal Component Analysis of Coil Compression in Parallel Imaging

A phased array with many coil elements has been widely used in parallel MRI for imaging acceleration. On the other hand, it results in increased memory usage and large computational costs for reconstructing the missing data from such a large number of channels. A number of techniques have been developed to linearly combine physical channels to produce fewer compressed virtual channels for reconstruction. A new channel compression technique via kernel principal component analysis (KPCA) is proposed. The proposed KPCA method uses a nonlinear combination of all physical channels to produce a set of compressed virtual channels. This method not only reduces the computational time but also improves the reconstruction quality of all channels when used. Taking the traditional GRAPPA algorithm as an example, it is shown that the proposed KPCA method can achieve better quality than both PCA and all channels, and at the same time the calculation time is almost the same as the existing PCA method.

Deep Residual Learning for Accelerated MRI Using Magnitude and Phase Networks

OBJECTIVE

Accelerated magnetic resonance (MR) image acquisition with compressed sensing (CS) and parallel imaging is a powerful method to reduce MR imaging scan time. However, many reconstruction algorithms have high computational costs. To address this, we investigate deep residual learning networks to remove aliasing artifacts from artifact corrupted images.

METHODS

The deep residual learning networks are composed of magnitude and phase networks that are separately trained. If both phase and magnitude information are available, the proposed algorithm can work as an iterative k-space interpolation algorithm using framelet representation. When only magnitude data are available, the proposed approach works as an image domain postprocessing algorithm.

RESULTS

Even with strong coherent aliasing artifacts, the proposed network successfully learned and removed the aliasing artifacts, whereas current parallel and CS reconstruction methods were unable to remove these artifacts.

CONCLUSION

Comparisons using single and multiple coil acquisition show that the proposed residual network provides good reconstruction results with orders of magnitude faster computational time than existing CS methods.

SIGNIFICANCE

The proposed deep learning framework may have a great potential for accelerated MR reconstruction by generating accurate results immediately.

3D hyperpolarized C-13 EPI with calibrationless parallel imaging

With the translation of metabolic MRI with hyperpolarized 13C agents into the clinic, imaging approaches will require large volumetric FOVs to support clinical applications. Parallel imaging techniques will be crucial to increasing volumetric scan coverage while minimizing RF requirements and temporal resolution. Calibrationless parallel imaging approaches are well-suited for this application because they eliminate the need to acquire coil profile maps or auto-calibration data. In this work, we explored the utility of a calibrationless parallel imaging method (SAKE) and corresponding sampling strategies to accelerate and undersample hyperpolarized 13C data using 3D blipped EPI acquisitions and multichannel receive coils, and demonstrated its application in a human study of [1-13C]pyruvate metabolism.