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Compressed Sensing Magnetic Resonance Imaging (CS-MRI) significantly accelerates MR acquisition at a sampling rate much lower than the Nyquist criterion. A major challenge for CS-MRI lies in solving the severely ill-posed inverse problem to reconstruct aliasing-free MR images from the sparse ${k}$ -space data. Conventional methods typically optimize an energy function, producing restoration of high quality, but their iterative numerical solvers unavoidably bring extremely large time consumption. Recent deep techniques provide fast restoration by either learning direct prediction to final reconstruction or plugging learned modules into the energy optimizer. Nevertheless, these data-driven predictors cannot guarantee the reconstruction following principled constraints underlying the domain knowledge so that the reliability of their reconstruction process is questionable. In this paper, we propose a deep framework assembling principled modules for CS-MRI that fuses learning strategy with the iterative solver of a conventional reconstruction energy. This framework embeds an optimal condition checking mechanism, fostering efficient and reliable reconstruction. We also apply the framework to three practical tasks, i.e., complex-valued data reconstruction, parallel imaging and reconstruction with Rician noise. Extensive experiments on both benchmark and manufacturer-testing images demonstrate that the proposed method reliably converges to the optimal solution more efficiently and accurately than the state-of-the-art in various scenarios. [ABSTRACT FROM AUTHOR]

Individual channel ultra-high field (7T) phase images have to be phase offset corrected prior to the mapping of magnetic susceptibility of tissue. Whilst numerous methods have been proposed for gradient recalled echo MRI phase offset correction, it remains unclear how they affect quantitative magnetic susceptibility values derived from phase images. Methods already proposed either employ a single or multiple echo time MRI data. In terms of the latter, offsets can be derived using an ultra-short echo time acquisition, or by estimating the offset based on two echo points with the assumption of linear phase evolution with echo time. Our evaluation involved 32 channel multi-echo time 7T GRE (Gradient Recalled Echo) and ultra-short echo time PETRA (Pointwise Encoding Time Reduction with Radial Acquisition) MRI data collected for a susceptibility phantom and three human brains. The combined phase images generated using four established offset correction methods (two single and two multiple echo time) were analysed, followed by an assessment of quantitative susceptibility values obtained for a phantom and human brains. The effectiveness of each method in removing the offsets was shown to reduce with increased echo time, decreased signal intensity and reduced overlap in coil sensitivity profiles. Quantitative susceptibility values and how they change with echo time were found to be method specific. Phase offset correction methods based on single echo time data have a tendency to produce more accurate and less noisy quantitative susceptibility maps in comparison with methods employing multiple echo time data. [ABSTRACT FROM AUTHOR]

Purpose: To develop a strategy for training a physics‐guided MRI reconstruction neural network without a database of fully sampled data sets. Methods: Self‐supervised learning via data undersampling (SSDU) for physics‐guided deep learning reconstruction partitions available measurements into two disjoint sets, one of which is used in the data consistency (DC) units in the unrolled network and the other is used to define the loss for training. The proposed training without fully sampled data is compared with fully supervised training with ground‐truth data, as well as conventional compressed‐sensing and parallel imaging methods using the publicly available fastMRI knee database. The same physics‐guided neural network is used for both proposed SSDU and supervised training. The SSDU training is also applied to prospectively two‐fold accelerated high‐resolution brain data sets at different acceleration rates, and compared with parallel imaging. Results: Results on five different knee sequences at an acceleration rate of 4 shows that the proposed self‐supervised approach performs closely with supervised learning, while significantly outperforming conventional compressed‐sensing and parallel imaging, as characterized by quantitative metrics and a clinical reader study. The results on prospectively subsampled brain data sets, in which supervised learning cannot be used due to lack of ground‐truth reference, show that the proposed self‐supervised approach successfully performs reconstruction at high acceleration rates (4, 6, and 8). Image readings indicate improved visual reconstruction quality with the proposed approach compared with parallel imaging at acquisition acceleration. Conclusion: The proposed SSDU approach allows training of physics‐guided deep learning MRI reconstruction without fully sampled data, while achieving comparable results with supervised deep learning MRI trained on fully sampled data. [ABSTRACT FROM AUTHOR]

Purpose: To advance research in the field of machine learning for MR image reconstruction with an open challenge. Methods: We provided participants with a dataset of raw k‐space data from 1,594 consecutive clinical exams of the knee. The goal of the challenge was to reconstruct images from these data. In order to strike a balance between realistic data and a shallow learning curve for those not already familiar with MR image reconstruction, we ran multiple tracks for multi‐coil and single‐coil data. We performed a two‐stage evaluation based on quantitative image metrics followed by evaluation by a panel of radiologists. The challenge ran from June to December of 2019. Results: We received a total of 33 challenge submissions. All participants chose to submit results from supervised machine learning approaches. Conclusions: The challenge led to new developments in machine learning for image reconstruction, provided insight into the current state of the art in the field, and highlighted remaining hurdles for clinical adoption. [ABSTRACT FROM AUTHOR]

Background and Purpose: Brain magnetic resonance imaging (MRI) examinations using high-resolution 3D post-contrast sequences offer increased sensitivity for the detection of metastases in the central nervous system but are usually long exams. We evaluated whether the diagnostic performance of a highly accelerated Wave-controlled aliasing in parallel imaging (Wave-CAIPI) post-contrast 3D T1 SPACE sequence was non-inferior to the standard high-resolution 3D T1 SPACE sequence for the evaluation of brain metastases.Materials and Methods: Thirty-three patients undergoing evaluation for brain metastases were prospectively evaluated with a standard post-contrast 3D T1 SPACE sequence and an optimized Wave-CAIPI 3D T1 SPACE sequence, which was three times faster than the standard sequence. Two blinded neuroradiologists performed a head-to-head comparison to evaluate the visualization of pathology, perception of artifacts, and the overall diagnostic quality. Wave–CAIPI post-contrast T1 SPACE was tested for non-inferiority relative to standard T1 SPACE using a 15% non-inferiority margin.Results: Wave–CAIPI post-contrast T1 SPACE was non-inferior to the standard T1 SPACE for visualization of enhancing lesions (P < 0.01) and offered equivalent diagnostic quality performance and only marginally higher background noise compared to the standard sequence.Conclusions: Our findings suggest that Wave-CAIPI post-contrast T1 SPACE provides equivalent visualization of pathology and overall diagnostic quality with three times reduced scan time compared to the standard 3D T1 SPACE.

Macaque monkeys are an important animal model where invasive investigations can lead to a better understanding of the cortical organization of primates including humans. However, the tools and methods for noninvasive image acquisition (e.g. MRI RF coils and pulse sequence protocols) and image data preprocessing have lagged behind those developed for humans. To resolve the structural and functional characteristics of the smaller macaque brain, high spatial, temporal, and angular resolutions combined with high signal-to-noise ratio are required to ensure good image quality. To address these challenges, we developed a macaque 24-channel receive coil for 3-T MRI with parallel imaging capabilities. This coil enables adaptation of the Human Connectome Project (HCP) image acquisition protocols to the in-vivo macaque brain. In addition, we adapted HCP preprocessing methods to the macaque brain, including spatial minimal preprocessing of structural, functional MRI (fMRI), and diffusion MRI (dMRI). The coil provides the necessary high signal-to-noise ratio and high efficiency in data acquisition, allowing four- and five-fold accelerations for dMRI and fMRI. Automated FreeSurfer segmentation of cortex, reconstruction of cortical surface, removal of artefacts and nuisance signals in fMRI, and distortion correction of dMRI all performed well, and the overall quality of basic neurobiological measures was comparable with those for the HCP. Analyses of functional connectivity in fMRI revealed high sensitivity as compared with those from publicly shared datasets. Tractography-based connectivity estimates correlated with tracer connectivity similarly to that achieved using ex-vivo dMRI. The resulting HCP-style in vivo macaque MRI data show considerable promise for analyzing cortical architecture and functional and structural connectivity using advanced methods that have previously only been available in studies of the human brain.

MR Fingerprinting (MRF) is a relatively new imaging framework capable of providing accurate and simultaneous quantification of multiple tissue properties for improved tissue characterization and disease diagnosis. While 2D MRF has been widely available, extending the method to 3D MRF has been an actively pursued area of research as a 3D approach can provide a higher spatial resolution and better tissue characterization with an inherently higher signal-to-noise ratio. However, 3D MRF with a high spatial resolution requires lengthy acquisition times, especially for a large volume, making it impractical for most clinical applications. In this study, a high-resolution 3D MR Fingerprinting technique, combining parallel imaging and deep learning, was developed for rapid and simultaneous quantification of T1 and T2 relaxation times. Parallel imaging was first applied along the partition-encoding direction to reduce the amount of acquired data. An advanced convolutional neural network was then integrated with the MRF framework to extract features from the MRF signal evolution for improved tissue characterization and accelerated mapping. A modified 3D-MRF sequence was also developed in the study to acquire data to train the deep learning model that can be directly applied to prospectively accelerate 3D MRF scans. Our results of quantitative T1 and T2 maps demonstrate that improved tissue characterization can be achieved using the proposed method as compared to prior methods. With the integration of parallel imaging and deep learning techniques, whole-brain (26 × 26 × 18 cm3) quantitative T1 and T2 mapping with 1-mm isotropic resolution were achieved in ~7 min. In addition, a ~7-fold improvement in processing time to extract tissue properties was also accomplished with the deep learning approach as compared to the standard template matching method. All of these improvements make high-resolution whole-brain quantitative MR imaging feasible for clinical applications.

Abstract Background In diffusion-weighted magnetic resonance imaging (DWI) using single-shot echo planar imaging (ss-EPI), both reduced field-of-view (FOV) excitation and sensitivity encoding (SENSE) alone can increase in-plane resolution to some degree. However, when the two techniques are combined to further increase resolution without pronounced geometric distortion, the resulted images are often corrupted by high level of noise and artifact due to the numerical restriction in SENSE. Hence, this study is aimed to provide a reconstruction method to deal with this problem. Methods The proposed reconstruction method was developed and implemented to deal with the high level of noise and artifact in the combination of reduced FOV imaging and traditional SENSE, in which all the imaging data were considered jointly by incorporating the motion induced phase variations among excitations. The in vivo human spine diffusion images from ten subjects were acquired at 1.5 T and reconstructed using the proposed method, and compared with SENSE magnitude average results for a range of reduction factors in reduced FOV. These images were evaluated by two radiologists using visual scores (considering distortion, noise and artifact levels) from 1 to 10. Results The proposed method was able to reconstruct images with greatly reduced noise and artifact compared to SENSE magnitude average. The mean g-factors were maintained close to 1 along with enhanced signal-to-noise ratio efficiency. The image quality scores of the proposed method were significantly higher (P

Low-rank matrix completion with phase constraints have been applied to the single-channel magnetic resonance imaging (MRI) reconstruction. In this paper, the reconstruction of sparse parallel imaging with smooth phase in each coil is formulated as the completion of a low-rank data matrix, which is modeled by the k-space neighborhoods and symmetric property of samples. The proposed algorithm is compared with a calibrationless parallel MRI reconstruction method based on both simulation data and real data. The experiment results show the proposed method has better performance in terms of MRI imaging enhancement, scanning time reduction, and denoising capability.

Purpose: Cardiac diffusion tensor imaging using EPI readout is prone to image distortions in the presence of field inhomogeneities. In this work, a framework to analyze and correct image distortions in cardiac diffusion tensor imaging is presented. Methods: A multi‐coil reconstruction framework was implemented to enable field map‐based off‐resonance correction. Numerical simulations were used to examine reconstruction performance for EPI phase‐encode directions blip up‐down and down‐up for different degrees of off‐resonance gradients and varying field map resolution. The impact of coil encoding was analyzed using the g‐factor and normalized RMSE. Finally, the proposed method was tested on free‐breathing in vivo cardiac diffusion tensor imaging data acquired in healthy subjects at 3 Tesla. Results: Depending on the local field map gradient strength and polarity and the selected phase‐encode direction, field inhomogeneities lead to either local spatial compression or stretching with standard image reconstruction. Although spatial compression results in loss of image resolution upon field map‐based reconstruction, spatial stretching can be recovered once multiple receive coils are utilized. Multi‐coil reconstruction was found to reduce the normalized RMSE from 34.3% to 8.1% for image compression, and 33.6% to 1.8% for image stretching, with resulting average g‐factors 14.7 ± 2.9 and 1.2 ± 0.1, respectively. In vivo, multi‐coil field map‐based reconstruction yielded improved alignment of angle maps with anatomical cine data. Conclusion: Multi‐coil, field map‐based image reconstruction for echo‐planar cardiac diffusion tensor imaging allows accurate image reconstruction provided that the phase‐encode direction and polarity is chosen to principally align with the direction and polarity of the prominent gradients of field inhomogeneities. [ABSTRACT FROM AUTHOR]

Background and Purpose: Brain magnetic resonance imaging (MRI) examinations using high-resolution 3D post-contrast sequences offer increased sensitivity for the detection of metastases in the central nervous system but are usually long exams. We evaluated whether the diagnostic performance of a highly accelerated Wave-controlled aliasing in parallel imaging (Wave-CAIPI) post-contrast 3D T1 SPACE sequence was non-inferior to the standard high-resolution 3D T1 SPACE sequence for the evaluation of brain metastases. Materials and Methods: Thirty-three patients undergoing evaluation for brain metastases were prospectively evaluated with a standard post-contrast 3D T1 SPACE sequence and an optimized Wave-CAIPI 3D T1 SPACE sequence, which was three times faster than the standard sequence. Two blinded neuroradiologists performed a head-to-head comparison to evaluate the visualization of pathology, perception of artifacts, and the overall diagnostic quality. Wave–CAIPI post-contrast T1 SPACE was tested for non-inferiority relative to standard T1 SPACE using a 15% non-inferiority margin. Results: Wave–CAIPI post-contrast T1 SPACE was non-inferior to the standard T1 SPACE for visualization of enhancing lesions (P < 0.01) and offered equivalent diagnostic quality performance and only marginally higher background noise compared to the standard sequence. Conclusions: Our findings suggest that Wave-CAIPI post-contrast T1 SPACE provides equivalent visualization of pathology and overall diagnostic quality with three times reduced scan time compared to the standard 3D T1 SPACE. [ABSTRACT FROM AUTHOR]

Arrays of coils are commonly used in MRI both for reception and in parallel transmission to alleviate radiofrequency field inhomogeneities at high fields. Most designs typically overlap loop elements by a critical area (approximately 10%) to minimize mutual inductive couplings. With this geometrical constraint, loop sizes have to be reduced to accommodate large numbers of coils for a given coverage. However, the contribution of coil noise to total noise increases as each coil size decreases, which reduces overall signal-to-noise ratio (SNR), especially in deeper regions of the sample volume. Here we propose arrays designs using elements that overlap much more (over-overlapped), and using numerical calculations we investigate their performance compared to two kinds of conventionally overlapped arrays (one with the same coil size but smaller coil number, and one with the same coil number but smaller coil size). Our simulation results show that the over-overlapped array can considerably increase the central SNR when coil noise dominates. [ABSTRACT FROM AUTHOR]

Purpose: To develop a deep learning‐based Bayesian estimation for MRI reconstruction. Methods: We modeled the MRI reconstruction problem with Bayes's theorem, following the recently proposed PixelCNN++ method. The image reconstruction from incomplete k‐space measurement was obtained by maximizing the posterior possibility. A generative network was utilized as the image prior, which was computationally tractable, and the k‐space data fidelity was enforced by using an equality constraint. The stochastic backpropagation was utilized to calculate the descent gradient in the process of maximum a posterior, and a projected subgradient method was used to impose the equality constraint. In contrast to the other deep learning reconstruction methods, the proposed one used the likelihood of prior as the training loss and the objective function in reconstruction to improve the image quality. Results: The proposed method showed an improved performance in preserving image details and reducing aliasing artifacts, compared with GRAPPA, ℓ1‐ESPRiT, model‐based deep learning architecture for inverse problems (MODL), and variational network (VN), last two were state‐of‐the‐art deep learning reconstruction methods. The proposed method generally achieved more than 3 dB peak signal‐to‐noise ratio improvement for compressed sensing and parallel imaging reconstructions compared with the other methods. Conclusions: The Bayesian estimation significantly improved the reconstruction performance, compared with the conventional ℓ1‐sparsity prior in compressed sensing reconstruction tasks. More importantly, the proposed reconstruction framework can be generalized for most MRI reconstruction scenarios. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this work was to provide parallel imaging capability for the human head in a gradient insert of 33‐cm inner diameter within the related constraints of space, encoding ambiguity, and eddy current immunity. Methods: Eddy current behavior of the 8‐channel transmit–receive array coil was investigated via heating and field deviation measurements. RF performance was evaluated using S‐parameters, noise statistics, B1 maps, and g‐factor maps. In vivo images of a human head and knee were acquired with Cartesian readout and TE below 0.45 ms. Results: Under intense gradient use, the shield was heated up to 55°C and other coil structures to 40°C. After standard preemphasis calibration, eddy current‐related field distortions caused by the developed RF coil were smaller than for a commercial receive‐only coil. In the ambiguous regions of the gradient, B1+ is 20 dB lower than in the center of the FOV. Coupling between elements is below −15 dB, and noise correlation is less than 0.31 when the coil is loaded with a head. Power efficiency was 0.52 ± 0.02 μT/√W, and the SD of the flip angle was below 10% in central slices of the brain. 2D, up to fourfold acceleration causes less than 30% noise amplification. The RF coil can be used during full gradient performance. Conclusion: Based on the described features, the presented coil enables parallel imaging in the high‐performance gradient insert. [ABSTRACT FROM AUTHOR]

The neuroimaging of nonhuman primates (NHPs) realised with magnetic resonance imaging (MRI) plays an important role in understanding brain structures and functions, as well as neurodegenerative diseases and pathological disorders. Theoretically, an ultrahigh field MRI (≥7 T) is capable of providing a higher signal‐to‐noise ratio (SNR) for better resolution; however, the lack of appropriate radiofrequency (RF) coils for 9.4 T monkey MRI undermines the benefits provided by a higher field strength. In particular, the standard volume birdcage coil at 9.4 T generates typical destructive interferences in the periphery of the brain, which reduces the SNR in the neuroscience‐focused cortex region. Also, the standard birdcage coil is not capable of performing parallel imaging. Consequently, extended scan durations may cause unnecessary damage due to overlong anaesthesia. In this work, assisted by numerical simulations, an eight‐channel receive RF coil array was specially designed and manufactured for imaging NHPs at 9.4 T. The structure and geometry of the proposed receive array was optimised with numerical simulations, so that the SNR enhancement region was particularly focused on monkey brain. Validated with rhesus monkey and cynomolgus monkey brain images acquired from a 9.4 T MRI scanner, the proposed receive array outperformed standard birdcage coil with higher SNR, mean diffusivity and fractional anisotropy values, as well as providing better capability for parallel imaging. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Purpose: A locally segmented parallel imaging reconstruction method is proposed that efficiently utilizes sensitivity distribution of multichannel receiver coil. Theory and Methods: A method of locally segmenting a MR signal is introduced to maximize the differences in sensitivity between receiver channels. A 1D Fourier transformation of the undersampled k‐space data is performed along the readout direction, which generates a hybrid 2D space. The hybrid space is partitioned into localized segments along the readout direction. In every localized segment, kernels representing relation between adjacent signals are estimated from autocalibration signals, and data at unsampled points are estimated using the kernels. Then, the images are reconstructed from full k‐space data that consists of the sampled data and the estimated data at unsampled points. Results: In a computer simulation and in vivo experiments, the locally segmented reconstruction method produced fewer residual artifacts compared to the conventional parallel imaging reconstruction methods with the same kernel geometry. The performance gain of the proposed method comes from maximizing encoding capability of receiver channels, thus resulting in the accurately estimated kernel weights that reflect the relation between adjacent signals. Conclusion: The proposed spatial segmentation method maximally utilizes differences in the sensitivity of receiver channels to reconstruct images with reduced artifacts. [ABSTRACT FROM AUTHOR]

This work proposes the Temporal Differences (TED) Compressed Sensing (CS) method for accelerating thermal monitoring in MR‐guided High‐Intensity Focused Ultrasound (MRgHIFU) treatments. TED combines k‐space subsampling, parallel imaging, and a unique CS recovery of the temporal differences between pre‐heating and post‐heating multi‐coil data. TED was validated through retrospective experiments with (i) two phantom datasets acquired with 1.5 T and 3 T MRgHIFU systems from different vendors, (ii) data from an in vivo animal model experiment, and (iii) four datasets from clinical in vivo MRgHIFU treatments of prostate cancer in humans. TED produced highly accurate temperature change maps from subsampled k‐space data for all datasets. For the clinical in vivo data, an analysis of 105 time frames showed that the average TED reconstruction error is 1.06‐1.67 °C. Furthermore, TED consistently outperforms two state‐of‐the‐art methods, l1‐SPIRiT and the K‐space Hybrid Method, and offers errors that are significantly lower, by 29% or more. Moreover, TED offers robust performance over a range of its tunable parameters, stability across MRgHIFU systems from different vendors, and a short runtime of 1.7 s. In summary, TED enables k‐space subsampling while retaining high‐temperature mapping accuracy. [ABSTRACT FROM AUTHOR]

Purpose: To test a new parallel imaging strategy for acceleration of hyperpolarized 13C MR acquisitions based on a 3D blipped stack‐of‐spirals trajectory and conjugate‐gradient SENSE reconstruction with precalibrated sensitivities. Methods: The blipped stack‐of‐spirals trajectory was developed for an acceleration factor of 4, based on an undersampled stack‐of‐spirals with gradient blips during spiral readout. The trajectory was developed with volumetric coverage of a large FOV and with high spatial resolution. High temporal resolution was attained through spectral‐spatial excitation and 4 excitations per volume. The blipped stack‐of‐spirals was evaluated in simulations and phantom experiments. Next, the method was evaluated for kidney and cardiac imaging in 2 separate healthy pigs. Results: Simulation and phantom results showed successful acquisition and reconstruction, but also revealed reconstruction challenges for certain locations and for wide signal sources. For the kidney experiment, the accelerated acquisition showed high similarity to 2 separately acquired fully sampled data sets with matched spatial and temporal resolution, respectively. For the cardiac experiment, the accelerated acquisition proved able to map each metabolite in 3 dimensions within a single cardiac cycle. Conclusion: The proposed method demonstrated effective mapping of metabolism in both kidneys and the heart of healthy pigs. Limitations seen in phantom experiments, may be irrelevant for most clinical applications, but should be kept in mind as well as reconstruction challenges related to residual aliasing. All in all, we show that the blipped stack‐of‐spirals is a relevant parallel imaging method for hyperpolarized human imaging, facilitating better insights into metabolism compared with nonaccelerated acquisition. [ABSTRACT FROM AUTHOR]

Purpose: To introduce a modified 3D stack‐of‐spirals trajectory and efficient SENSE reconstruction for improved through‐plane undersampling, while maintaining SNR efficiency and other benefits of spiral acquisitions. Methods: A novel spiral staircase trajectory is introduced. This trajectory is a modified stack of spirals, in which spiral arms are distributed between partitions along kz. The trajectory maintains the efficient separable reconstruction with a Cartesian fast Fourier transform along the kz direction, followed by a 2D slice‐by‐slice gridding reconstruction. An additional intermediate step introduces a phase correction to collapse the spiral arms into the prescribed slice planes. For data undersampled through plane, this produces aliasing with reduced coherence, controlled by the arm‐ordering. Undersampled data can then be reconstructed with reduced g‐factor using a conjugate gradient–based iterative SENSE algorithm. Results: The trajectory significantly improves g‐factor for through‐plane accelerated acquisitions. Improvement manifests through both reduced overall g‐factor and reduced structure in the g‐factor maps. In the presented experiments, the mean g‐factor decreased from 1.26 to 0.93 and the maximum g‐factor decreased from 3.89 to 1.15 for R = 2 spiral staircase when compared with stack of spirals, and the mean g‐factor decreased from 2.51 to 0.94 and the maximum g‐factor decreased from 8.26 to 1.35 for R = 3 spiral staircase when compared with stack of spirals. Conclusion: The novel spiral staircase trajectory offers improved aliasing characteristics for through‐plane parallel imaging acceleration in 3D spiral acquisitions. [ABSTRACT FROM AUTHOR]

Purpose: To enable rigid body motion‐tolerant parallel volumetric magnetic resonance imaging by retrospective head motion correction on a variety of spatiotemporal scales and imaging sequences. Theory and methods: Tolerance against rigid body motion is based on distributed and incoherent sampling orders for boosting a joint retrospective motion estimation and reconstruction framework. Motion resilience stems from the encoding redundancy in the data, as generally provided by the coil array. Hence, it does not require external sensors, navigators or training data, so the methodology is readily applicable to sequences using 3D encodings. Results: Simulations are performed showing full inter‐shot corrections for usual levels of in vivo motion, large number of shots, standard levels of noise and moderate acceleration factors. Feasibility of inter‐ and intra‐shot corrections is shown under controlled motion in vivo. Practical efficacy is illustrated by high‐quality results in most corrupted of 208 volumes from a series of 26 clinical pediatric examinations collected using standard protocols. Conclusions: The proposed framework addresses the rigid motion problem in volumetric anatomical brain scans with sufficient encoding redundancy which has enabled reliable pediatric examinations without sedation. [ABSTRACT FROM AUTHOR]

The aim of this work is to implement real-time 3D MR thermometry for high intensity focused ultrasound (HIFU) monitoring. Volumetric MR thermometry was implemented based on a 3D echo-shifted sequence with short TR to improve temperature sensitivity. The 3D acquisition was accelerated in two phase encoding directions with controlled aliasing in volumetric parallel imaging (CAIPIRINHA). Image reconstruction was run in an open source reconstruction platform (Gadgetron). Phantom experiments showed the proposed volumetric thermometry was comparable to the fiber optical thermometer. In-vivo animal experiments in rabbit thigh showed that the temperature error before and after 4× acceleration was less than 0.65 °C. Finally, real-time 3D thermometry with temporal resolution ~3 s and spatial resolution 2 × 2 × 5 mm3 (spatial coverage 192 × 192 × 80 mm3) was achieved with Gadgetron reconstruction. Real-time temperature monitoring was achieved in-vivo by using parallel imaging accelerated 3D echo-shifted sequence with Gadgetron reconstruction. [ABSTRACT FROM AUTHOR]

Purpose: To assess whether artifacts in multi‐slice multi‐echo spin echo neck imaging, thought to be caused by brief motion events such as swallowing, can be corrected by reacquiring corrupted central k‐space data and estimating the remainder with parallel imaging. Methods: A single phase‐encode line (ky = 0, phase‐encode direction anteroposterior) navigator echo was used to identify motion‐corrupted data and guide the online reacquisition. If motion corruption was detected in the 7 central k‐space lines, they were replaced with reacquired data. Subsequently, GRAPPA reconstruction was trained on the updated central portion of k‐space and then used to estimate the remaining motion‐corrupted k‐space data from surrounding uncorrupted data. Similar compressed sensing‐based approaches have been used previously to compensate for respiration in cardiac imaging. The g‐factor noise amplification was calculated for the parallel imaging reconstruction of data acquired with a 10‐channel neck coil. The method was assessed in scans with 9 volunteers and 12 patients. Results: The g‐factor analysis showed that GRAPPA reconstruction of 2 adjacent motion‐corrupted lines causes high noise amplification; therefore, the number of 2‐line estimations should be limited. In volunteer scans, median ghosting reduction of 24% was achieved with 2 adjacent motion‐corrupted lines correction, and image quality was improved in 2 patient scans that had motion corruption close to the center of k‐space. Conclusion: Motion‐corrupted echo‐trains can be identified with a navigator echo. Combined reacquisition and parallel imaging estimation reduced motion artifacts in multi‐slice MESE when there were brief motion events, especially when motion corruption was close to the center of k‐space. [ABSTRACT FROM AUTHOR]

Purpose: To assess the effect of compressed sensing (CS) on image quality and acquisition speed in routine brain magnetic resonance imaging (MRI). Methods: During a 2-month implementation period of CS, two senior neuroradiologists, one MRI physicist and one application specialist optimized the CS acceleration factor to reduce scan time and improve spatial resolution, while maintaining image quality. Afterwards, two neuroradiologists independently scored image quality on a 5-point Likert scale in 3‑dimensional (3D) fluid attenuation inversion recovery (FLAIR), 3D double inversion recovery (DIR), 3D T2, 3D T1, 3D T1 + gadoteric acid, axial T2, axial FLAIR, axial T2*, and 3D arterial time-of-flight MR angiography (art. TOF) sequences acquired during 1 week before (CS−) and after (CS+) the implementation of CS. Time of acquisition was recorded for all sequences. Results: A total of 51 CS− and 48 CS+ patients were included. The median scan time reduction was 29.3% (range 0.0–58.4%), median voxel size reduction was 10.5% (0.0–33.3%). The CS+ image quality was rated superior for 3D FLAIR (p < 0.001), 3D T2 (p = 0.001), and axial T2* sequences (p = 0.024). For all other sequences, no statistical difference in image quality was observed. Interreader agreement regarding image quality was good for all sequences (weighted Cohen's κ > 0.5). Conclusion: The use of CS saves considerable imaging time while allowing to increase spatial resolution in routine clinical brain MRI without loss in image quality. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-channel image reconstruction method, named DeepcomplexMRI, to accelerate parallel MR imaging with residual complex convolutional neural network. Different from most existing works which rely on the utilization of the coil sensitivities or prior information of predefined transforms, DeepcomplexMRI takes advantage of the availability of a large number of existing multi-channel groudtruth images and uses them as target data to train the deep residual convolutional neural network offline. In particular, a complex convolutional network is proposed to take into account the correlation between the real and imaginary parts of MR images. In addition, the k-space data consistency is further enforced repeatedly in between layers of the network. The evaluations on in vivo datasets show that the proposed method has the capability to recover the desired multi-channel images. Its comparison with state-of-the-art methods also demonstrates that the proposed method can reconstruct the desired MR images more accurately. • Proposed an end-to-end parallel imaging reconstruction framework. The proposed framework doesn't need any calculation of the sensitivity information to resolve the aliasing and correlations among the channels. • Both real-valued and complex-valued versions of our proposed framework have been investigated for parallel imaging with our code released to the public. • The method has been compared withquite a few state-of-the-art methods. Encouraging performances have been achieved by our proposed framework. [ABSTRACT FROM AUTHOR]

Purpose: To develop a non‐Cartesian k‐space reconstruction method using self‐calibrated region‐specific interpolation kernels for highly accelerated acquisitions. Methods: In conventional non‐Cartesian GRAPPA with through‐time GRAPPA (TT‐GRAPPA), the use of region‐specific interpolation kernels has demonstrated improved reconstruction quality in dynamic imaging for highly accelerated acquisitions. However, TT‐GRAPPA requires the acquisition of a large number of separate calibration scans. To reduce the overall imaging time, we propose Self‐calibrated Interpolation of Non‐Cartesian data with GRAPPA (SING) to self‐calibrate region‐specific interpolation kernels from dynamic undersampled measurements. The SING method synthesizes calibration data to adapt to the distinct shape of each region‐specific interpolation kernel geometry, and uses a novel local k‐space regularization through an extension of TT‐GRAPPA. This calibration approach is used to reconstruct non‐Cartesian images at high acceleration rates while mitigating noise amplification. The reconstruction quality of SING is compared with conjugate‐gradient SENSE and TT‐GRAPPA in numerical phantoms and in vivo cine data sets. Results: In both numerical phantom and in vivo cine data sets, SING offers visually and quantitatively similar reconstruction quality to TT‐GRAPPA, and provides improved reconstruction quality over conjugate‐gradient SENSE. Furthermore, temporal fidelity in SING and TT‐GRAPPA is similar for the same acceleration rates. G‐factor evaluation over the heart shows that SING and TT‐GRAPPA provide similar noise amplification at moderate and high rates. Conclusion: The proposed SING reconstruction enables significant improvement of acquisition efficiency for calibration data, while matching the reconstruction performance of TT‐GRAPPA. [ABSTRACT FROM AUTHOR]

Purpose: To propose a parameter optimization framework on wave gradients of Wave‐CAIPI imaging for decreasing g‐factor penalty and reducing reconstruction artifacts. Theory and Methods: The influences of parameters on g‐factor are theoretically analyzed. The average g‐factor is chosen as a metric for parameter optimization, and then a fast calculation method is proposed to approximately and ultra‐fast calculate the average g‐factor. Based on this, a set of points in the function of the average g‐factor with respect to the wave gradient parameters is calculated, and the optimal wave gradient parameters are found according to these points. Results: In vivo human brain experiments were performed on 3T MR scanners for the comparison experiments. The results show that the proposed parameter optimization framework is able to efficiently obtain optimal wave gradient parameters, which can achieve decreased g‐factor penalty and less artifacts of reconstructions than the empirical parameters. Conclusion: The proposed parameter optimization framework is computationally efficient and can optimize the wave gradient parameters of Wave‐CAIPI imaging for better image quality than before. [ABSTRACT FROM AUTHOR]

Background: Single-shot diffusion-weighted echo-planar imaging (ssDW-EPI) acquired with parallel imaging and a multi-oblique scan plane may suffer from residual aliasing artifacts, resembling lesions on the calculated apparent diffusion coefficient (ADC) map.Purpose: To combine ssDW-EPI and virtual coil acquisition and develop a self-reference reconstruction method to eliminate the residual aliasing artifact on multi-oblique ssDW-EPI sequence with parallel imaging and multiple signal averaging.Study Type: Prospective.Subjects: Three healthy subjects and 50 stroke patients.Field Strength/sequence: Conventional ssDW-EPI with parallel imaging, and proposed ssDW-EPI with virtual coil acquisition at 1.5T.Assessment: The efficacy of the proposed method was evaluated in 50 stroke patients by comparing the ssDW-EPI with conventional parallel imaging reconstructions. The extent of residual aliasing artifacts were rated on a 5-point Likert scale by three independent raters. Only the data without residual aliasing artifacts on conventional ssDW-EPI were included for the assessment of signal-to-noise ratio (SNR), ghost-to-signal ratio (GSR), and ADC.Statistical Tests: The interobserver agreements for examining residual aliasing artifacts were measured by the intraclass correlation coefficient (ICC). A two-sample t-test was performed for comparing SNR, GSR, and ADC.Results: There was a perfect agreement (ICC = 1.00) in the examination of residual aliasing artifacts on images obtained using the proposed method. The incidence rates of the residual aliasing artifact on the ADC maps obtained from the scanner console and proposed method were 60% (ie, 30 out of 50) and 0%, respectively. The proposed method offers significantly lower GSR than conventional parallel imaging reconstruction (P < 0.001). There was no significant difference in SNR (P = 0.20-0.51) and ADC values (P = 0.20-0.94) between conventional parallel imaging reconstructions and the proposed method.Data Conclusion: It appears that our method could effectively eliminate artifacts and significantly improve the GSR of b = 0 T2 WI and b > 0 DWI, as well as permit ADC measurement consistent with conventional techniques. Our method may be beneficial to clinical assessment of the brain that utilizes multi-oblique ssDW-EPI.Level Of Evidence: 1 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2020;51:1442-1453. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

To design and validate a rapid Simultaneous Multi‐slice (SMS) Magnetic Resonance Elastography technique (MRE), which combines SMS acquisition, in‐plane undersampling and an existing rapid Magnetic Resonance Elastography (MREr) scheme to allow accelerated data acquisition in healthy volunteers and comparison against MREr. SMS‐MREr sequence was developed by incorporating SMS acquisition scheme into an existing MREr sequence that accelerates MRE acquisition by acquiring data during opposite phases of mechanical vibrations. The MREr sequence accelerated MRE acquisition by acquiring data during opposite phases of mechanical vibrations. Liver MRE was performed on 23 healthy subjects using MREr and SMS‐MREr sequences, and mean stiffness values were obtained for manually drawn regions of interest. Linear correlation and agreement between MREr‐ and SMS‐MREr‐based stiffness values were investigated. SMS‐MREr reduced the scan time by half relative to MREr, and allowed acquisition of four‐slice MRE data in a single 17‐second breath‐hold. Visual comparison suggested agreement between MREr and SMS‐MREr elastograms. A Pearson's correlation of 0.93 was observed between stiffness values derived from MREr and SMS‐MREr. Bland–Altman analysis demonstrated good agreement, with −0.08 kPa mean bias and narrow limits of agreement (95% CI: 0.23 to −0.39 kPa) between stiffness values obtained using MREr and SMS‐MREr. SMS can be combined with other fast MRE approaches to achieve further acceleration. This pushes the limit on the acceleration that can be achieved in MRE acquisition, and makes it possible to conduct liver MRE exams in a single breath‐hold. [ABSTRACT FROM AUTHOR]

Multi‐contrast images are commonly acquired together to maximize complementary diagnostic information, albeit at the expense of longer scan times. A time‐efficient strategy to acquire high‐quality multi‐contrast images is to accelerate individual sequences and then reconstruct undersampled data with joint regularization terms that leverage common information across contrasts. However, these terms can cause features that are unique to a subset of contrasts to leak into the other contrasts. Such leakage‐of‐features may appear as artificial tissues, thereby misleading diagnosis. The goal of this study is to develop a compressive sensing method for multi‐channel multi‐contrast magnetic resonance imaging (MRI) that optimally utilizes shared information while preventing feature leakage. Joint regularization terms group sparsity and colour total variation are used to exploit common features across images while individual sparsity and total variation are also used to prevent leakage of distinct features across contrasts. The multi‐channel multi‐contrast reconstruction problem is solved via a fast algorithm based on Alternating Direction Method of Multipliers. The proposed method is compared against using only individual and only joint regularization terms in reconstruction. Comparisons were performed on single‐channel simulated and multi‐channel in‐vivo datasets in terms of reconstruction quality and neuroradiologist reader scores. The proposed method demonstrates rapid convergence and improved image quality for both simulated and in‐vivo datasets. Furthermore, while reconstructions that solely use joint regularization terms are prone to leakage‐of‐features, the proposed method reliably avoids leakage via simultaneous use of joint and individual terms, thereby holding great promise for clinical use. [ABSTRACT FROM AUTHOR]

Purpose: High‐resolution isotropic T2 mapping of the human brain with multi‐echo spin‐echo (MESE) acquisitions is challenging. When using a 2D sequence, the resolution is limited by the slice thickness. If used as a 3D acquisition, specific absorption rate limits are easily exceeded due to the high power deposition of nonselective refocusing pulses. A method to reconstruct 1‐mm3 isotropic T2 maps is proposed based on multiple 2D MESE acquisitions. Data were undersampled (10‐fold) to compensate for the prolonged scan time stemming from the super‐resolution acquisition. Theory and Methods: The proposed method integrates a classical super‐resolution with an iterative model‐based approach to reconstruct quantitative maps from a set of undersampled low‐resolution data. The method was tested on numerical and multipurpose phantoms, and in vivo data. T2 values were assessed with a region‐of‐interest analysis using a single‐slice spin‐echo and a fully sampled MESE acquisition in a phantom, and a MESE acquisition in healthy volunteers. Results: Numerical simulations showed that the best trade‐off between acceleration and number of low‐resolution datasets is 10‐fold acceleration with 4 acquisitions (acquisition time = 18 min). The proposed approach showed improved resolution over low‐resolution images for both phantom and brain. Region‐of‐interest analysis of the phantom compartments revealed that at shorter T2, the proposed method was comparable with the fully sampled MESE. For the volunteer data, the T2 values found in the brain structures were consistent across subjects (8.5‐13.1 ms standard deviation). Conclusion: The proposed method addresses the inherent limitations associated with high‐resolution T2 mapping and enables the reconstruction of 1 mm3 isotropic relaxation maps with a 10 times faster acquisition. [ABSTRACT FROM AUTHOR]

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To reduce artifacts and scan time of GRASE imaging by selecting an optimal sampling pattern and jointly reconstructing gradient echo and spin echo images. We jointly reconstruct images for the different echo types by considering these as additional virtual coil channels in the novel Autocalibrated Parallel Imaging Reconstruction with Sampling Pattern Optimization for GRASE (APIR4GRASE) method. Besides image reconstruction, we identify optimal sampling patterns for the acquisition. The selected optimal patterns were validated on phantom and in-vivo acquisitions. Comparison to the conventional GRASE without acceleration, and to the GRAPPA reconstruction with a single echo type was also performed. Using identified optimal sampling patterns, APIR4GRASE eliminated modulation artifacts in both phantom and in-vivo experiments; mean square error (MSE) was reduced by 78% and 94%, respectively, compared to the conventional GRASE with similar scan time. Both artifacts and g-factor were reduced compared to the GRAPPA reconstruction with a single echo type. APIR4GRASE substantially improves the speed and quality of GRASE imaging over the state-of-the-art, and is able to reconstruct both spin echo and gradient echo images. [ABSTRACT FROM AUTHOR]

Purpose: To investigate auto‐ and pre‐calibration coil profile estimation for parallel imaging reconstruction of hyperpolarized 13C MRI volumetric data. Methods: Parallel imaging reconstruction was studied with 3 different approaches for coil profile estimation: auto‐calibration, phantom calibration, and theoretic calibration. Acquisition was performed with a 3D stack‐of‐spirals sequence with spectral–spatial excitation and Cartesian undersampling. Parallel imaging reconstructions were done with conjugate gradient SENSE and 3D gridding with inhomogeneity correction. The approaches were compared in simulations with different SNR, through phantom experiments, and in an in vivo pig study focused on the kidneys. All imaging was done with a rigid home‐built 12‐channel 13C receive coil at 3T. Results: The phantom calibrated and theoretic approaches resulted in the best structural similarities in simulations and demonstrated higher image quality in the phantom experiments compared to the auto‐calibrated approach. In vivo mapping of pyruvate uptake and lactate conversion improved for accelerated acquisitions because of a better temporal resolution. From a practical and image quality point of view, use of theoretic coil profiles led to improved results compared to the other approaches. Conclusion: The success of the theoretic coil profile estimation demonstrates a negligible effect of load on sensitivity profiles at the carbon frequency at 3T. Through theoretic or phantom calibrated parallel imaging, accelerated 3D volumes could be reconstructed with sufficient sensitivity, temporal, and spatial resolution to map the metabolism of kidneys exemplifying abdominal organs. This approach overcomes a critical step in the clinical translation of parallel imaging in hyperpolarized 13C MR. [ABSTRACT FROM AUTHOR]

Efficiently acquiring multi-contrast magnetic resonance imaging data is crucial for patient comfort and clinical throughput. Developing scan acceleration methods tailored for specific applications drastically improves the value of an MRI examination. Here, we propose a novel method to control the aliasing of simultaneously acquired images of multiple spin echo coherence pathways with the goal of producing high quality multi-contrast images from a single acquisition. Modulating the radiofrequency phase of several pulses applied in brief succession also uniquely modulates the phase of spin echo coherence pathways. A method, termed magnetic resonance coherence pathway unraveling (MR-CPU), to control the aliasing of simultaneously acquired coherence pathway images is developed here along with parallel imaging-based reconstruction methods to separate them. MR-CPU was validated in phantom experiments and tested in vivo. High levels of correlation between reference pathway images and MR-CPU-derived coherence pathway images were found from the phantom experiments. Minimal artifacts arising from the separation of the overlapped coherence pathway images were observed in vivo. MR-CPU provides a novel mechanism through which to acquire and separate multiple overlapped coherence pathway images, thus adding to the diagnostic potential of an MRI exam without the penalty of additional scan time. [Display omitted] • Phase of spin echo pathways can be controlled via the phase of the RF pulses. • Modulating superimposed pathway phase introduces spatial encoding capabilities. • Separate spin echo pathway images were reconstructed from aliased signals. [ABSTRACT FROM AUTHOR]

Existing parallel MRI methods are limited by a fundamental trade-off in that suppressing noise introduces aliasing artifacts. Bayesian methods with an appropriately chosen image prior offer a promising alternative; however, previous methods with spatial priors assume that intensities vary smoothly over the entire image, resulting in blurred edges. Here we introduce an edge-preserving prior (EPP) that instead assumes that intensities are piecewise smooth, and propose a new approach to efficiently compute its Bayesian estimate. The estimation task is formulated as an optimization problem that requires a non-convex objective function to be minimized in a space with thousands of dimensions. As a result, traditional continuous minimization methods cannot be applied. This optimization task is closely related to some problems in the field of computer vision for which discrete optimization methods have been developed in the last few years. We adapt these algorithms, which are based on graph cuts, to address our optimization problem. The results of several parallel imaging experiments on brain and torso regions performed under challenging conditions with high acceleration factors are shown and compared with the results of conventional sensitivity encoding (SENSE) methods. An empirical analysis indicates that the proposed method visually improves overall quality compared to conventional methods.

The theory of Compressive Sensing (CS) has experienced a tremendous growth through continuous works of researchers from different cross platform domains of study. The strict realm of Shannon-Nyquist sampling theorem is compromised and an image can be reconstructed from fewer measurements than it was shown necessary to be, but with a trade-off in the efficiency. In biomedical signal processing, especially Magnetic Resonance Imaging (MRI), the potential applicability of CS is long observed. Since then quite a large number of research work in this field has been proposed, a few with experimental analysis, which establish its applicability in the domain of MRI. Since the topic is too broad, this review paper presents a discussion and summary of important works on different fields of CS-MRI. The challenges, limitations and advantages of different techniques of CS-MRI are studied and future trend/ direction is predicted.

Background: Deep learning (DL) can improve image quality by removing noise from accelerated MRI., Objective: To compare the quality of various accelerated imaging applications in knee MRI with and without DL., Method: We analyzed 44 knee MRI scans from 38 adult patients using the DL-reconstructed parallel acquisition technique (PAT) between May 2021 and April 2022. The participants underwent sagittal fat-saturated T2-weighted turbo-spin-echo accelerated imaging without DL (PAT-2 [2-fold parallel accelerated imaging], PAT-3, and PAT-4) and with DL (DL with PAT-3 [PAT-3DL] and PAT-4 [PAT-4DL]). Two readers independently evaluated subjective image quality (diagnostic confidence of knee joint abnormalities, subjective noise and sharpness, and overall image quality) using a 4-point grading system (1-4, 4=best). Objective image quality was assessed based on noise (noise power) and sharpness (edge rise distance)., Results: The mean acquisition times for PAT-2, PAT-3, PAT-4, PAT-3DL, and PAT-4DL sequences were 2:55, 2:04, 1:33, 2:04, and 1:33 min, respectively. Regarding subjective image quality, PAT-3DL and PAT-4DL scored higher than PAT-2. Objectively, DL-reconstructed imaging had significantly lower noise than PAT-3 and PAT-4 (P <0.001), but the results were not significantly different from those for PAT-2 (P >0.988). Objective image sharpness did not differ significantly among the imaging combinations (P =0.470). The inter-reader reliability ranged from good to excellent (κ = 0.761–0.832)., Conclusion: PAT-4DL imaging in knee MRI exhibits similar subjective image quality, objective noise, and sharpness levels compared with conventional PAT-2 imaging, with an acquisition time reduction of 47%., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Purpose: Proton MR spectroscopic imaging (MRSI) benefits from B0 ≥ 7T and multichannel receive coils, promising substantial resolution improvements. However, MRSI acquisition with high spatial resolution requires efficient acceleration and coil combination. To speed up the already‐fast sampling via concentric rings, we implemented additional, non‐Cartesian, hybrid through‐time/through‐k‐space (tt/tk)‐generalized autocalibrating partially parallel acquisition (GRAPPA). A new multipurpose interleaved calibration scan (interleaved MUSICAL) acquires reference data for both coil combination and PI. This renders the reconstruction process (especially PI) less sensitive to instabilities. Methods: Six healthy volunteers were scanned at 7T. Three calibration datasets for coil combination and PI were recorded: a) iMUSICAL, b) static MUSICAL as prescan, c) moved MUSICAL as prescan with misaligned head position. The coil combination performance, including motion sensitivity, of iMUSICAL was compared to MUSICAL for single‐slice free induction decay (FID)‐MRSI. Through‐time/through‐k‐space‐GRAPPA with constant/variable‐density undersampling was evaluated on the same data, comparing the three calibration datasets. Additionally, the proposed method was successfully applied to 3D whole‐brain FID‐MRSI. Results: Using iMUSICAL for coil combination yielded the highest signal‐to‐noise ratio (SNR) (+9%) and lowest Cramer‐Rao lower bounds (CRLBs) (‐6%) compared to both MUSICAL approaches, with similar metabolic map quality. Also, excellent mean g‐factors of 1.07 and low residual lipid aliasing were obtained when using iMUSICAL as calibration data for two‐fold, variable‐density undersampling, while significantly degraded metabolic maps were obtained using the misaligned MUSICAL calibration data. Conclusion: Through‐time/through‐k‐space‐GRAPPA can accelerate already time‐efficient non‐Cartesian spatial‐spectral 2D/3D‐MRSI encoding even further. Particularly promising results have been achieved using iMUSICAL as a robust, interleaved multipurpose calibration for MRSI reconstruction, without extra calibration prescan. [ABSTRACT FROM AUTHOR]