Image reconstruction by domain-transform manifold learning.

Image reconstruction is essential for imaging applications across the physical and life sciences, including optical and radar systems, magnetic resonance imaging, X-ray computed tomography, positron emission tomography, ultrasound imaging and radio astronomy. During image acquisition, the sensor encodes an intermediate representation of an object in the sensor domain, which is subsequently reconstructed into an image by an inversion of the encoding function. Image reconstruction is challenging because analytic knowledge of the exact inverse transform may not exist a priori, especially in the presence of sensor non-idealities and noise. Thus, the standard reconstruction approach involves approximating the inverse function with multiple ad hoc stages in a signal processing chain, the composition of which depends on the details of each acquisition strategy, and often requires expert parameter tuning to optimize reconstruction performance. Here we present a unified framework for image reconstruction-automated transform by manifold approximation (AUTOMAP)-which recasts image reconstruction as a data-driven supervised learning task that allows a mapping between the sensor and the image domain to emerge from an appropriate corpus of training data. We implement AUTOMAP with a deep neural network and exhibit its flexibility in learning reconstruction transforms for various magnetic resonance imaging acquisition strategies, using the same network architecture and hyperparameters. We further demonstrate that manifold learning during training results in sparse representations of domain transforms along low-dimensional data manifolds, and observe superior immunity to noise and a reduction in reconstruction artefacts compared with conventional handcrafted reconstruction methods. In addition to improving the reconstruction performance of existing acquisition methodologies, we anticipate that AUTOMAP and other learned reconstruction approaches will accelerate the development of new acquisition strategies across imaging modalities.

Motion guidance lines for robust data consistency-based retrospective motion correction in 2D and 3D MRI.

PURPOSE: To develop a robust retrospective motion-correction technique based on repeating k-space guidance lines for improving motion correction in Cartesian 2D and 3D brain MRI. METHODS: The motion guidance lines are inserted into the standard sequence orderings for 2D turbo spin echo and 3D MPRAGE to inform a data consistency-based motion estimation and reconstruction, which can be guided by a low-resolution scout. The extremely limited number of required guidance lines are repeated during each echo train and discarded in the final image reconstruction. Thus, integration within a standard k-space acquisition ordering ensures the expected image quality/contrast and motion sensitivity of that sequence. RESULTS: Through simulation and in vivo 2D multislice and 3D motion experiments, we demonstrate that respectively 2 or 4 optimized motion guidance lines per shot enables accurate motion estimation and correction. Clinically acceptable reconstruction times are achieved through fully separable on-the-fly motion optimizations (˜1 s/shot) using standard scanner GPU hardware. CONCLUSION: The addition of guidance lines to scout accelerated motion estimation facilitates robust retrospective motion correction that can be effectively introduced without perturbing standard clinical protocols and workflows.

Validation of a highly accelerated post-contrast wave-controlled aliasing in parallel imaging (CAIPI) 3D-T1 MPRAGE compared to standard 3D-T1 MPRAGE for detection of intracranial enhancing lesions on 3-T MRI.

OBJECTIVES: High-resolution post-contrast T1-weighted imaging is a workhorse sequence in the evaluation of neurological disorders. The T1-MPRAGE sequence has been widely adopted for the visualization of enhancing pathology in the brain. However, this three-dimensional (3D) acquisition is lengthy and prone to motion artifact, which often compromises diagnostic quality. The goal of this study was to compare a highly accelerated wave-controlled aliasing in parallel imaging (CAIPI) post-contrast 3D T1-MPRAGE sequence (Wave-T1-MPRAGE) with the standard 3D T1-MPRAGE sequence for visualizing enhancing lesions in brain imaging at 3 T. METHODS: This study included 80 patients undergoing contrast-enhanced brain MRI. The participants were scanned with a standard post-contrast T1-MPRAGE sequence (acceleration factor [R] = 2 using GRAPPA parallel imaging technique, acquisition time [TA] = 5 min 18 s) and a prototype post-contrast Wave-T1-MPRAGE sequence (R = 4, TA = 2 min 32 s). Two neuroradiologists performed a head-to-head evaluation of both sequences and rated the visualization of enhancement, sharpness, noise, motion artifacts, and overall diagnostic quality. A 15% noninferiority margin was used to test whether post-contrast Wave-T1-MPRAGE was noninferior to standard T1-MPRAGE. Inter-rater and intra-rater agreement were calculated. Quantitative assessment of CNR/SNR was performed. RESULTS: Wave-T1-MPRAGE was noninferior to standard T1-MPRAGE for delineating enhancing lesions with unanimous agreement in all cases between raters. Wave-T1-MPRAGE was noninferior in the perception of noise (p < 0.001), motion artifact (p < 0.001), and overall diagnostic quality (p < 0.001). CONCLUSION: High-accelerated post-contrast Wave-T1-MPRAGE enabled a two-fold reduction in acquisition time compared to the standard sequence with comparable performance for visualization of enhancing pathology and equivalent perception of noise, motion artifacts and overall diagnostic quality without loss of clinically important information. KEY POINTS: • Post-contrast wave-controlled aliasing in parallel imaging (CAIPI) T1-MPRAGE accelerated the acquisition of three-dimensional (3D) high-resolution post-contrast images by more than two-fold. • Post-contrast Wave-T1-MPRAGE was noninferior to standard T1-MPRAGE with unanimous agreement between reviewers (100% in 80 cases) for the visualization of intracranial enhancing lesions. • Wave-T1-MPRAGE was equivalent to the standard sequence in the perception of noise in 94% (75 of 80) of cases and was preferred in 16% (13 of 80) of cases for decreased motion artifact.

Scout accelerated motion estimation and reduction (SAMER).

PURPOSE: To demonstrate a navigator/tracking-free retrospective motion estimation technique that facilitates clinically acceptable reconstruction time. METHODS: Scout accelerated motion estimation and reduction (SAMER) uses a single 3-5 s, low-resolution scout scan and a novel sequence reordering to independently determine motion states by minimizing the data-consistency error in a SENSE plus motion forward model. This eliminates time-consuming alternating optimization as no updates to the imaging volume are required during the motion estimation. The SAMER approach was assessed quantitatively through extensive simulation and was evaluated in vivo across multiple motion scenarios and clinical imaging contrasts. Finally, SAMER was synergistically combined with advanced encoding (Wave-CAIPI) to facilitate rapid motion-free imaging. RESULTS: The highly accelerated scout provided sufficient information to achieve accurate motion trajectory estimation (accuracy ~0.2 mm or degrees). The novel sequence reordering improved the stability of the motion parameter estimation and image reconstruction while preserving the clinical imaging contrast. Clinically acceptable computation times for the motion estimation (~4 s/shot) are demonstrated through a fully separable (non-alternating) motion search across the shots. Substantial artifact reduction was demonstrated in vivo as well as corresponding improvement in the quantitative error metric. Finally, the extension of SAMER to Wave-encoding enabled rapid high-quality imaging at up to R = 9-fold acceleration. CONCLUSION: SAMER significantly improved the computational scalability for retrospective motion estimation and correction.

External Dynamic InTerference Estimation and Removal (EDITER) for low field MRI.

PURPOSE: Point-of-care MRI requires operation outside of Faraday shielded rooms normally used to block image-degrading electromagnetic interference (EMI). To address this, we introduce the EDITER method (External Dynamic InTerference Estimation and Removal), an external sensor-based method to retrospectively remove image artifacts from time-varying external interference sources. THEORY AND METHODS: The method acquires data from multiple EMI detectors (tuned receive coils as well as untuned electrodes placed on the body) simultaneously with the primary MR coil during and between image data acquisition. We calculate impulse response functions dynamically that map the data from the detectors to the time varying artifacts then remove the transformed detected EMI from the MR data. Performance of the EDITER algorithm was assessed in phantom and in vivo imaging experiments in an 80 mT portable brain MRI in a controlled EMI environment and with an open 47.5 mT MRI scanner in an uncontrolled EMI setting. RESULTS: In the controlled setting, the effectiveness of the EDITER technique was demonstrated for specific types of introduced EMI sources with up to a 97% reduction of structured EMI and up to 76% reduction of broadband EMI in phantom experiments. In the uncontrolled EMI experiments, we demonstrate EMI reductions of up to 99% using an electrode and pick-up coil in vivo. We demonstrate up to a nine-fold improvement in image SNR with the method. CONCLUSION: The EDITER technique is a flexible and robust method to improve image quality in portable MRI systems with minimal passive shielding and could reduce the reliance of MRI on shielded rooms and allow for truly portable MRI.

Optimization of magnetization transfer contrast for EPI FLAIR brain imaging.

PURPOSE: To evaluate the impact of magnetization transfer (MT) on brain tissue contrast in turbo-spin-echo (TSE) and EPI fluid-attenuated inversion recovery (FLAIR) images, and to optimize an MT-prepared EPI FLAIR pulse sequence to match the tissue contrast of a clinical reference TSE FLAIR protocol. METHODS: Five healthy volunteers underwent 3T brain MRI, including single slice TSE FLAIR, multi-slice TSE FLAIR, EPI FLAIR without MT-preparation, and MT-prepared EPI FLAIR with variations of the MT-preparation parameters, including number of preparation pulses, pulse amplitude, and resonance offset. Automated co-registration and gray matter (GM) versus white matter (WM) segmentation was performed using a T1-MPRAGE acquisition, and the GM versus WM signal intensity ratio (contrast ratio) was calculated for each FLAIR acquisition. RESULTS: Without MT preparation, EPI FLAIR showed poor tissue contrast (contrast ratio = 0.98), as did single slice TSE FLAIR. Multi-slice TSE FLAIR provided high tissue contrast (contrast ratio = 1.14). MT-prepared EPI FLAIR closely approximated the contrast of the multi-slice TSE FLAIR images for two combinations of the MT-preparation parameters (contrast ratio = 1.14). Optimized MT-prepared EPI FLAIR provided a 50% reduction in scan time compared to the reference TSE FLAIR acquisition. CONCLUSION: Optimized MT-prepared EPI FLAIR provides comparable brain tissue contrast to the multi-slice TSE FLAIR images used in clinical practice.

An artificial intelligence-accelerated 2-minute multi-shot echo planar imaging protocol for comprehensive high-quality clinical brain imaging.

PURPOSE: We introduce and validate an artificial intelligence (AI)-accelerated multi-shot echo-planar imaging (msEPI)-based method that provides T1w, T2w, T2∗ , T2-FLAIR, and DWI images with high SNR, high tissue contrast, low specific absorption rates (SAR), and minimal distortion in 2 minutes. METHODS: The rapid imaging technique combines a novel machine learning (ML) scheme to limit g-factor noise amplification and improve SNR, a magnetization transfer preparation module to provide clinically desirable contrast, and high per-shot EPI undersampling factors to reduce distortion. The ML training and image reconstruction incorporates a tunable parameter for controlling the level of denoising/smoothness. The performance of the reconstruction method is evaluated across various acceleration factors, contrasts, and SNR conditions. The 2-minute protocol is directly compared to a 10-minute clinical reference protocol through deployment in a clinical setting, where five representative cases with pathology are examined. RESULTS: Optimization of custom msEPI sequences and protocols was performed to balance acquisition efficiency and image quality compared to the five-fold longer clinical reference. Training data from 16 healthy subjects across multiple contrasts and orientations were used to produce ML networks at various acceleration levels. The flexibility of the ML reconstruction was demonstrated across SNR levels, and an optimized regularization was determined through radiological review. Network generalization toward novel pathology, unobserved during training, was illustrated in five clinical case studies with clinical reference images provided for comparison. CONCLUSION: The rapid 2-minute msEPI-based protocol with tunable ML reconstruction allows for advantageous trade-offs between acquisition speed, SNR, and tissue contrast when compared to the five-fold slower standard clinical reference exam.

Clinical validation of Wave-CAIPI susceptibility-weighted imaging for routine brain MRI at 1.5 T.

OBJECTIVES: Wave-CAIPI (Controlled Aliasing in Parallel Imaging) enables dramatic reduction in acquisition time of 3D MRI sequences such as 3D susceptibility-weighted imaging (SWI) but has not been clinically evaluated at 1.5 T. We sought to compare highly accelerated Wave-CAIPI SWI (Wave-SWI) with two alternative standard sequences, conventional three-dimensional SWI and two-dimensional T2*-weighted Gradient-Echo (T2*w-GRE), in patients undergoing routine brain MRI at 1.5 T. METHODS: In this study, 172 patients undergoing 1.5 T brain MRI were scanned with a more commonly used susceptibility sequence (standard SWI or T2*w-GRE) and a highly accelerated Wave-SWI sequence. Two radiologists blinded to the acquisition technique scored each sequence for visualization of pathology, motion and signal dropout artifacts, image noise, visualization of normal anatomy (vessels and basal ganglia mineralization), and overall diagnostic quality. Superiority testing was performed to compare Wave-SWI to T2*w-GRE, and non-inferiority testing with 15% margin was performed to compare Wave-SWI to standard SWI. RESULTS: Wave-SWI performed superior in terms of visualization of pathology, signal dropout artifacts, visualization of normal anatomy, and overall image quality when compared to T2*w-GRE (all p < 0.001). Wave-SWI was non-inferior to standard SWI for visualization of normal anatomy and pathology, signal dropout artifacts, and overall image quality (all p < 0.001). Wave-SWI was superior to standard SWI for motion artifact (p < 0.001), while both conventional susceptibility sequences were superior to Wave-SWI for image noise (p < 0.001). CONCLUSIONS: Wave-SWI can be performed in a 1.5 T clinical setting with robust performance and preservation of diagnostic quality. KEY POINTS: • Wave-SWI accelerated the acquisition of 3D high-resolution susceptibility images in 70% of the acquisition time of the conventional T2*GRE. • Wave-SWI performed superior to T2*w-GRE for visualization of pathology, signal dropout artifacts, and overall diagnostic image quality. • Wave-SWI was noninferior to standard SWI for visualization of normal anatomy and pathology, signal dropout artifacts, and overall diagnostic image quality.

Evaluation of highly accelerated wave controlled aliasing in parallel imaging (Wave-CAIPI) susceptibility-weighted imaging in the non-sedated pediatric setting: a pilot study.

BACKGROUND: Susceptibility-weighted imaging (SWI) is highly sensitive for intracranial hemorrhagic and mineralized lesions but is associated with long scan times. Wave controlled aliasing in parallel imaging (Wave-CAIPI) enables greater acceleration factors and might facilitate broader application of SWI, especially in motion-prone populations. OBJECTIVE: To compare highly accelerated Wave-CAIPI SWI to standard SWI in the non-sedated pediatric outpatient setting, with respect to the following variables: estimated scan time, image noise, artifacts, visualization of normal anatomy and visualization of pathology. MATERIALS AND METHODS: Twenty-eight children (11 girls, 17 boys; mean age ± standard deviation [SD] = 128.3±62 months) underwent 3-tesla (T) brain MRI, including standard three-dimensional (3-D) SWI sequence followed by a highly accelerated Wave-CAIPI SWI sequence for each subject. We rated all studies using a predefined 5-point scale and used the Wilcoxon signed rank test to assess the difference for each variable between sequences. RESULTS: Wave-CAIPI SWI provided a 78% and 67% reduction in estimated scan time using the 32- and 20-channel coils, respectively, corresponding to estimated scan time reductions of 3.5 min and 3 min, respectively. All 28 children were imaged without anesthesia. Inter-reader agreement ranged from fair to substantial (k=0.67 for evaluation of pathology, 0.55 for anatomical contrast, 0.3 for central noise, and 0.71 for artifacts). Image noise was rated higher in the central brain with wave SWI (P<0.01), but not in the peripheral brain. There was no significant difference in the visualization of normal anatomical structures and visualization of pathology between the standard and wave SWI sequences (P=0.77 and P=0.79, respectively). CONCLUSION: Highly accelerated Wave-CAIPI SWI of the brain can provide similar image quality to standard SWI, with estimated scan time reduction of 3-3.5 min depending on the radiofrequency coil used, with fewer motion artifacts, at a cost of mild but perceptibly increased noise in the central brain.

Region-optimized virtual (ROVir) coils: Localization and/or suppression of spatial regions using sensor-domain beamforming.

PURPOSE: In many MRI scenarios, magnetization is often excited from spatial regions that are not of immediate interest. Excitation of uninteresting magnetization can complicate the design of efficient imaging methods, leading to either artifacts or acquisitions that are longer than necessary. While there are many hardware- and sequence-based approaches for suppressing unwanted magnetization, this paper approaches this longstanding problem from a different and complementary angle, using beamforming to suppress signals from unwanted regions without modifying the acquisition hardware or pulse sequence. Unlike existing beamforming approaches, we use a spatially invariant sensor-domain approach that can be applied directly to raw data to facilitate image reconstruction. THEORY AND METHODS: We use beamforming to linearly mix a set of original coils into a set of region-optimized virtual (ROVir) coils. ROVir coils optimize a signal-to-interference ratio metric, are easily calculated using simple generalized eigenvalue decomposition methods, and provide coil compression. RESULTS: ROVir coils were compared against existing coil-compression methods, and were demonstrated to have substantially better signal suppression capabilities. In addition, examples were provided in a variety of different application contexts (including brain MRI, vocal tract MRI, and cardiac MRI; accelerated Cartesian and non-Cartesian imaging; and outer volume suppression) that demonstrate the strong potential of this kind of approach. CONCLUSION: The beamforming-based ROVir framework is simple to implement, has promising capabilities to suppress unwanted MRI signal, and is compatible with and complementary to existing signal suppression methods. We believe that this general approach could prove useful across a wide range of different MRI applications.

Comparison of ultrafast wave-controlled aliasing in parallel imaging (CAIPI) magnetization-prepared rapid acquisition gradient echo (MP-RAGE) and standard MP-RAGE in non-sedated children: initial clinical experience.

BACKGROUND: Fast magnetic resonance imaging (MRI) sequences are advantageous in pediatric imaging as they can lessen child discomfort, decrease motion artifact and improve scanner availability. OBJECTIVE: To evaluate the feasibility of an ultrafast wave-CAIPI (controlled aliasing in parallel imaging) MP-RAGE (magnetization-prepared rapid gradient echo) sequence for brain imaging of awake pediatric patients. MATERIALS AND METHODS: Each MRI included a standard MP-RAGE sequence and an ultrafast wave-MP-RAGE sequence. Two neuroradiologists evaluated both sequences in terms of artifacts, noise, anatomical contrast and pathological contrast. A predefined 5-point scale was used by two independent pediatric neuroradiologists. A Wilcoxon signed-rank test was used to evaluate the difference between sequences for each variable. RESULTS: Twenty-four patients (14 males; mean age: 11.5±4.5 years, range: 1 month to 17.8 years) were included. Wave-CAIPI MP-RAGE provided a 77% reduction in scan time using a 32-channel coil and a 70% reduction using a 20-channel coil. Visualization of the pathology, artifacts and pathological enhancement (including parenchymal, leptomeningeal and dural enhancement) was not significantly different between standard MP-RAGE and wave-CAIPI MP-RAGE (all P>0.05). For central (P<0.001) and peripheral (P<0.001) noise, and the evaluation of the anatomical structures (P<0.001), the observers favored standard MP-RAGE over wave-CAIPI MP-RAGE. CONCLUSION: Ultrafast brain imaging with wave-CAIPI MP-RAGE is feasible in awake pediatric patients, providing a substantial reduction in scan time at a cost of subjectively increased image noise.

Joint multi-contrast variational network reconstruction (jVN) with application to rapid 2D and 3D imaging.

PURPOSE: To improve the image quality of highly accelerated multi-channel MRI data by learning a joint variational network that reconstructs multiple clinical contrasts jointly. METHODS: Data from our multi-contrast acquisition were embedded into the variational network architecture where shared anatomical information is exchanged by mixing the input contrasts. Complementary k-space sampling across imaging contrasts and Bunch-Phase/Wave-Encoding were used for data acquisition to improve the reconstruction at high accelerations. At 3T, our joint variational network approach across T1w, T2w and T2-FLAIR-weighted brain scans was tested for retrospective under-sampling at R = 6 (2D) and R = 4 × 4 (3D) acceleration. Prospective acceleration was also performed for 3D data where the combined acquisition time for whole brain coverage at 1 mm isotropic resolution across three contrasts was less than 3 min. RESULTS: Across all test datasets, our joint multi-contrast network better preserved fine anatomical details with reduced image-blurring when compared to the corresponding single-contrast reconstructions. Improvement in image quality was also obtained through complementary k-space sampling and Bunch-Phase/Wave-Encoding where the synergistic combination yielded the overall best performance as evidenced by exemplary slices and quantitative error metrics. CONCLUSION: By leveraging shared anatomical structures across the jointly reconstructed scans, our joint multi-contrast approach learnt more efficient regularizers, which helped to retain natural image appearance and avoid over-smoothing. When synergistically combined with advanced encoding techniques, the performance was further improved, enabling up to R = 16-fold acceleration with good image quality. This should help pave the way to very rapid high-resolution brain exams.

Network Accelerated Motion Estimation and Reduction (NAMER): Convolutional neural network guided retrospective motion correction using a separable motion model.

PURPOSE: We introduce and validate a scalable retrospective motion correction technique for brain imaging that incorporates a machine learning component into a model-based motion minimization. METHODS: A convolutional neural network (CNN) trained to remove motion artifacts from 2D T2 -weighted rapid acquisition with refocused echoes (RARE) images is introduced into a model-based data-consistency optimization to jointly search for 2D motion parameters and the uncorrupted image. Our separable motion model allows for efficient intrashot (line-by-line) motion correction of highly corrupted shots, as opposed to previous methods which do not scale well with this refinement of the motion model. Final image generation incorporates the motion parameters within a model-based image reconstruction. The method is tested in simulations and in vivo motion experiments of in-plane motion corruption. RESULTS: While the convolutional neural network alone provides some motion mitigation (at the expense of introduced blurring), allowing it to guide the iterative joint-optimization both improves the search convergence and renders the joint-optimization separable. This enables rapid mitigation within shots in addition to between shots. For 2D in-plane motion correction experiments, the result is a significant reduction of both image space root mean square error in simulations, and a reduction of motion artifacts in the in vivo motion tests. CONCLUSION: The separability and convergence improvements afforded by the combined convolutional neural network+model-based method shows the potential for meaningful postacquisition motion mitigation in clinical MRI.

Highly accelerated multishot echo planar imaging through synergistic machine learning and joint reconstruction.

PURPOSE: To introduce a combined machine learning (ML)- and physics-based image reconstruction framework that enables navigator-free, highly accelerated multishot echo planar imaging (msEPI) and demonstrate its application in high-resolution structural and diffusion imaging. METHODS: Single-shot EPI is an efficient encoding technique, but does not lend itself well to high-resolution imaging because of severe distortion artifacts and blurring. Although msEPI can mitigate these artifacts, high-quality msEPI has been elusive because of phase mismatch arising from shot-to-shot variations which preclude the combination of the multiple-shot data into a single image. We utilize deep learning to obtain an interim image with minimal artifacts, which permits estimation of image phase variations attributed to shot-to-shot changes. These variations are then included in a joint virtual coil sensitivity encoding (JVC-SENSE) reconstruction to utilize data from all shots and improve upon the ML solution. RESULTS: Our combined ML + physics approach enabled Rinplane × multiband (MB) = 8- × 2-fold acceleration using 2 EPI shots for multiecho imaging, so that whole-brain T2 and T2 * parameter maps could be derived from an 8.3-second acquisition at 1 × 1 × 3-mm3 resolution. This has also allowed high-resolution diffusion imaging with high geometrical fidelity using 5 shots at Rinplane × MB = 9- × 2-fold acceleration. To make these possible, we extended the state-of-the-art MUSSELS reconstruction technique to simultaneous multislice encoding and used it as an input to our ML network. CONCLUSION: Combination of ML and JVC-SENSE enabled navigator-free msEPI at higher accelerations than previously possible while using fewer shots, with reduced vulnerability to poor generalizability and poor acceptance of end-to-end ML approaches.

Highly-accelerated volumetric brain examination using optimized wave-CAIPI encoding.

BACKGROUND: Rapid volumetric imaging protocols could better utilize limited scanner resources. PURPOSE: To develop and validate an optimized 6-minute high-resolution volumetric brain MRI examination using Wave-CAIPI encoding. STUDY TYPE: Prospective. POPULATION/SUBJECTS: Ten healthy subjects and 20 patients with a variety of intracranial pathologies. FIELD STRENGTH/SEQUENCE: At 3 T, MPRAGE, T2 -weighted SPACE, SPACE FLAIR, and SWI were acquired at 9-fold acceleration using Wave-CAIPI and for comparison at 2-4-fold acceleration using conventional GRAPPA. ASSESSMENT: Extensive simulations were performed to optimize the Wave-CAIPI protocol and minimize both g-factor noise amplification and potential T1 /T2 blurring artifacts. Moreover, refinements in the autocalibrated reconstruction of Wave-CAIPI were developed to ensure high-quality reconstructions in the presence of gradient imperfections. In a randomized and blinded fashion, three neuroradiologists assessed the diagnostic quality of the optimized 6-minute Wave-CAIPI exam and compared it to the roughly 3× slower GRAPPA accelerated protocol using both an individual and head-to-head analysis. STATISTICAL TEST: A noninferiority test was used to test whether the diagnostic quality of Wave-CAIPI was noninferior to the GRAPPA acquisition, with a 15% noninferiority margin. RESULTS: Among all sequences, Wave-CAIPI achieved negligible g-factor noise amplification (gavg ≤ 1.04) and burring artifacts from T1 /T2 relaxation. Improvements of our autocalibration approach for gradient imperfections enabled increased robustness to gradient mixing imperfections in tilted-field of view (FOV) prescriptions as well as variations in gradient and analog-to-digital converter (ADC) sampling rates. In the clinical evaluation, Wave-CAIPI achieved similar mean scores when compared with GRAPPA (MPRAGE: ØW = 4.03, ØG = 3.97; T2 w SPACE: ØW = 4.00, ØG = 4.00; SPACE FLAIR: ØW = 3.97, ØG = 3.97; SWI: ØW = 3.93, ØG = 3.83) and was statistically noninferior (N = 30, P < 0.05 for all sequences). DATA CONCLUSION: The proposed volumetric brain exam retained comparable image quality when compared with the much longer conventional protocol. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:961-974.

Wave-CAIPI for highly accelerated MP-RAGE imaging.

PURPOSE: To introduce a highly accelerated T1-weighted magnetization-prepared rapid gradient echo (MP-RAGE) acquisition that uses wave-controlled aliasing in parallel imaging (wave-CAIPI) encoding to retain high image quality. METHODS: Significant acceleration of the MP-RAGE sequence is demonstrated using the wave-CAIPI technique. Here, sinusoidal waveforms are used to spread aliasing in all three directions to improve the g-factor. Combined with a rapid (2 s) coil sensitivity acquisition and data-driven trajectory calibration, we propose an online integrated acquisition-reconstruction pipeline for highly efficient MP-RAGE imaging. RESULTS: The 9-fold accelerated MP-RAGE acquisition can be performed in 71 s, with a maximum and average g-factor of gmax = 1.27 and gavg = 1.06 at 3T. Compared with the state-of-the-art method controlled aliasing in parallel imaging results in higher acceleration (2D-CAIPIRINHA), this is a factor of 4.6/1.4 improvement in gmax /gavg . In addition, we demonstrate a 57 s acquisition at 7T with 12-fold acceleration. This acquisition has a g-factor performance of gmax = 1.15 and gavg = 1.04. CONCLUSION: Wave encoding overcomes the g-factor noise amplification penalty and allows for an order of magnitude acceleration of MP-RAGE acquisitions. Magn Reson Med 79:401-406, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

High-resolution in vivo diffusion imaging of the human brain with generalized slice dithered enhanced resolution: Simultaneous multislice (gSlider-SMS).

PURPOSE: To develop an efficient acquisition for high-resolution diffusion imaging and allow in vivo whole-brain acquisitions at 600- to 700-µm isotropic resolution. METHODS: We combine blipped-controlled aliasing in parallel imaging simultaneous multislice (SMS) with a novel slab radiofrequency (RF) encoding gSlider (generalized slice-dithered enhanced resolution) to form a signal-to-noise ratio-efficient volumetric simultaneous multislab acquisition. Here, multiple thin slabs are acquired simultaneously with controlled aliasing, and unaliased with parallel imaging. To achieve high resolution in the slice direction, the slab is volumetrically encoded using RF encoding with a scheme similar to Hadamard encoding. However, with gSlider, the RF-encoding bases are specifically designed to be highly independent and provide high image signal-to-noise ratio in each slab acquisition to enable self-navigation of the diffusion's phase corruption. Finally, the method is combined with zoomed imaging (while retaining whole-brain coverage) to facilitate low-distortion single-shot in-plane encoding with echo-planar imaging at high resolution. RESULTS: A 10-slices-per-shot gSlider-SMS acquisition was used to acquire whole-brain data at 660 and 760 µm isotropic resolution with b-values of 1500 and 1800 s/mm2 , respectively. Data were acquired on the Connectome 3 Tesla scanner with 64-channel head coil. High-quality data with excellent contrast were achieved at these resolutions, which enable the visualization of fine-scale structures. CONCLUSIONS: The gSlider-SMS approach provides a new, efficient way to acquire high-resolution diffusion data. Magn Reson Med 79:141-151, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Motion-robust sub-millimeter isotropic diffusion imaging through motion corrected generalized slice dithered enhanced resolution (MC-gSlider) acquisition.

PURPOSE: To develop an efficient MR technique for ultra-high resolution diffusion MRI (dMRI) in the presence of motion. METHODS: gSlider is an SNR-efficient high-resolution dMRI acquisition technique. However, subject motion is inevitable during a prolonged scan for high spatial resolution, leading to potential image artifacts and blurring. In this study, an integrated technique termed Motion Corrected gSlider (MC-gSlider) is proposed to obtain high-quality, high-resolution dMRI in the presence of large in-plane and through-plane motion. A motion-aware reconstruction with spatially adaptive regularization is developed to optimize the conditioning of the image reconstruction under difficult through-plane motion cases. In addition, an approach for intra-volume motion estimation and correction is proposed to achieve motion correction at high temporal resolution. RESULTS: Theoretical SNR and resolution analysis validated the efficiency of MC-gSlider with regularization, and aided in selection of reconstruction parameters. Simulations and in vivo experiments further demonstrated the ability of MC-gSlider to mitigate motion artifacts and recover detailed brain structures for dMRI at 860 µm isotropic resolution in the presence of motion with various ranges. CONCLUSION: MC-gSlider provides motion-robust, high-resolution dMRI with a temporal motion correction sensitivity of 2 s, allowing for the recovery of fine detailed brain structures in the presence of large subject movements.

Design of sparse Halbach magnet arrays for portable MRI using a genetic algorithm.

Permanent magnet arrays offer several attributes attractive for the development of a low-cost portable MRI scanner for brain imaging. They offer the potential for a relatively lightweight, low to mid-field system with no cryogenics, a small fringe field, and no electrical power requirements or heat dissipation needs. The cylindrical Halbach array, however, requires external shimming or mechanical adjustments to produce B0 fields with standard MRI homogeneity levels (e.g., 0.1 ppm over FOV), particularly when constrained or truncated geometries are needed, such as a head-only magnet where the magnet length is constrained by the shoulders. For portable scanners using rotation of the magnet for spatial encoding with generalized projections, the spatial pattern of the field is important since it acts as the encoding field. In either a static or rotating magnet, it will be important to be able to optimize the field pattern of cylindrical Halbach arrays in a way that retains construction simplicity. To achieve this, we present a method for designing an optimized cylindrical Halbach magnet using the genetic algorithm to achieve either homogeneity (for standard MRI applications) or a favorable spatial encoding field pattern (for rotational spatial encoding applications). We compare the chosen designs against a standard, fully populated sparse Halbach design, and evaluate optimized spatial encoding fields using point-spread-function and image simulations. We validate the calculations by comparing to the measured field of a constructed magnet. The experimentally implemented design produced fields in good agreement with the predicted fields, and the genetic algorithm was successful in improving the chosen metrics. For the uniform target field, an order of magnitude homogeneity improvement was achieved compared to the un-optimized, fully populated design. For the rotational encoding design the resolution uniformity is improved by 95% compared to a uniformly populated design.

Diffusion Tractography of the Entire Left Ventricle by Using Free-breathing Accelerated Simultaneous Multisection Imaging.

Purpose To develop a clinically feasible whole-heart free-breathing diffusion-tensor (DT) magnetic resonance (MR) imaging approach with an imaging time of approximately 15 minutes to enable three-dimensional (3D) tractography. Materials and Methods The study was compliant with HIPAA and the institutional review board and required written consent from the participants. DT imaging was performed in seven healthy volunteers and three patients with pulmonary hypertension by using a stimulated echo sequence. Twelve contiguous short-axis sections and six four-chamber sections that covered the entire left ventricle were acquired by using simultaneous multisection (SMS) excitation with a blipped-controlled aliasing in parallel imaging readout. Rate 2 and rate 3 SMS excitation was defined as two and three times accelerated in the section axis, respectively. Breath-hold and free-breathing images with and without SMS acceleration were acquired. Diffusion-encoding directions were acquired sequentially, spatiotemporally registered, and retrospectively selected by using an entropy-based approach. Myofiber helix angle, mean diffusivity, fractional anisotropy, and 3D tractograms were analyzed by using paired t tests and analysis of variance. Results No significant differences (P > .63) were seen between breath-hold rate 3 SMS and free-breathing rate 2 SMS excitation in transmural myofiber helix angle, mean diffusivity (mean ± standard deviation, [0.89 ± 0.09] × 10-3 mm2/sec vs [0.9 ± 0.09] × 10-3 mm2/sec), or fractional anisotropy (0.43 ± 0.05 vs 0.42 ± 0.06). Three-dimensional tractograms of the left ventricle with no SMS and rate 2 and rate 3 SMS excitation were qualitatively similar. Conclusion Free-breathing DT imaging of the entire human heart can be performed in approximately 15 minutes without section gaps by using SMS excitation with a blipped-controlled aliasing in parallel imaging readout, followed by spatiotemporal registration and entropy-based retrospective image selection. This method may lead to clinical translation of whole-heart DT imaging, enabling broad application in patients with cardiac disease. © RSNA, 2016 Online supplemental material is available for this article.