Romer-EPTI: rotating-view motion-robust super-resolution EPTI for SNR-efficient distortion-free in-vivo mesoscale dMRI and microstructure imaging.

Purpose: To overcome the major challenges in dMRI acquisition, including low SNR, distortion/blurring, and motion vulnerability. Methods: A novel Romer-EPTI technique is developed to provide distortion-free dMRI with significant SNR gain, high motion-robustness, sharp spatial resolution, and simultaneous multi-TE imaging. It introduces a ROtating-view Motion-robust supEr-Resolution technique (Romer) combined with a distortion/blurring-free EPTI encoding. Romer enhances SNR by a simultaneous multi-thick-slice acquisition with rotating-view encoding, while providing high motion-robustness through a motion-aware super-resolution reconstruction, which also incorporates slice-profile and real-value diffusion, to resolve high-isotropic-resolution volumes. The in-plane encoding is performed using distortion/blurring-free EPTI, which further improves effective spatial resolution and motion robustness by preventing not only T2/T2*-blurring but also additional blurring resulting from combining encoded volumes with inconsistent geometries caused by dynamic distortions. Self-navigation was incorporated to enable efficient phase correction. Additional developments include strategies to address slab-boundary artifacts, achieve minimal TE for SNR gain at 7T, and achieve high robustness to strong phase variations at high b-values. Results: Using Romer-EPTI, we demonstrate distortion-free whole-brain mesoscale in-vivo dMRI at both 3T (500-µm-iso) and 7T (485-µm-iso) for the first time, with high SNR efficiency (e.g., 25×), and high image quality free from distortion and slab-boundary artifacts with minimal blurring. Motion experiments demonstrate Romer-EPTI's high motion-robustness and ability to recover sharp images in the presence of motion. Romer-EPTI also demonstrates significant SNR gain and robustness in high b-value (b=5000s/mm2) and time-dependent dMRI. Conclusion: Romer-EPTI significantly improves SNR, motion-robustness, and image quality, providing a highly efficient acquisition for high-resolution dMRI and microstructure imaging.

Attentive Continuous Generative Self-training for Unsupervised Domain Adaptive Medical Image Translation.

Self-training is an important class of unsupervised domain adaptation (UDA) approaches that are used to mitigate the problem of domain shift, when applying knowledge learned from a labeled source domain to unlabeled and heterogeneous target domains. While self-training-based UDA has shown considerable promise on discriminative tasks, including classification and segmentation, through reliable pseudo-label filtering based on the maximum softmax probability, there is a paucity of prior work on self-training-based UDA for generative tasks, including image modality translation. To fill this gap, in this work, we seek to develop a generative self-training (GST) framework for domain adaptive image translation with continuous value prediction and regression objectives. Specifically, we quantify both aleatoric and epistemic uncertainties within our GST using variational Bayes learning to measure the reliability of synthesized data. We also introduce a self-attention scheme that de-emphasizes the background region to prevent it from dominating the training process. The adaptation is then carried out by an alternating optimization scheme with target domain supervision that focuses attention on the regions with reliable pseudo-labels. We evaluated our framework on two cross-scanner/center, inter-subject translation tasks, including tagged-to-cine magnetic resonance (MR) image translation and T1-weighted MR-to-fractional anisotropy translation. Extensive validations with unpaired target domain data showed that our GST yielded superior synthesis performance in comparison to adversarial training UDA methods.

Attentive continuous generative self-training for unsupervised domain adaptive medical image translation.

Self-training is an important class of unsupervised domain adaptation (UDA) approaches that are used to mitigate the problem of domain shift, when applying knowledge learned from a labeled source domain to unlabeled and heterogeneous target domains. While self-training-based UDA has shown considerable promise on discriminative tasks, including classification and segmentation, through reliable pseudo-label filtering based on the maximum softmax probability, there is a paucity of prior work on self-training-based UDA for generative tasks, including image modality translation. To fill this gap, in this work, we seek to develop a generative self-training (GST) framework for domain adaptive image translation with continuous value prediction and regression objectives. Specifically, we quantify both aleatoric and epistemic uncertainties within our GST using variational Bayes learning to measure the reliability of synthesized data. We also introduce a self-attention scheme that de-emphasizes the background region to prevent it from dominating the training process. The adaptation is then carried out by an alternating optimization scheme with target domain supervision that focuses attention on the regions with reliable pseudo-labels. We evaluated our framework on two cross-scanner/center, inter-subject translation tasks, including tagged-to-cine magnetic resonance (MR) image translation and T1-weighted MR-to-fractional anisotropy translation. Extensive validations with unpaired target domain data showed that our GST yielded superior synthesis performance in comparison to adversarial training UDA methods.

3D Echo Planar Time-resolved Imaging (3D-EPTI) for ultrafast multi-parametric quantitative MRI.

Multi-parametric quantitative MRI has shown great potential to improve the sensitivity and specificity of clinical diagnosis and to enhance our understanding of complex brain processes, but suffers from long scan time especially at high spatial resolution. To address this longstanding challenge, we introduce a novel approach, termed 3D Echo Planar Time-resolved Imaging (3D-EPTI), which significantly increases the acceleration capacity of MRI sampling, and provides high acquisition efficiency for multi-parametric MRI. This is achieved by exploiting the spatiotemporal correlation of MRI data at multiple timescales through new encoding strategies within and between efficient continuous readouts. Specifically, an optimized spatiotemporal CAIPI encoding within the readouts combined with a radial-block sampling strategy across the readouts enables an acceleration rate of 800 fold in the k-t space. A subspace reconstruction was employed to resolve thousands of high-quality multi-contrast images. We have demonstrated the ability of 3D-EPTI to provide robust and repeatable whole-brain simultaneous T1, T2, T2*, PD and B1+ mapping at high isotropic resolution within minutes (e.g., 1-mm isotropic resolution in 3 minutes), and to enable submillimeter multi-parametric imaging to study detailed brain structures.

Measuring Strain in Diffusion-Weighted Data Using Tagged Magnetic Resonance Imaging.

Accurate strain measurement in a deforming organ has been essential in motion analysis using medical images. In recent years, internal tissue's in vivo motion and strain computation has been mostly achieved through dynamic magnetic resonance (MR) imaging. However, such data lack information on tissue's intrinsic fiber directions, preventing computed strain tensors from being projected onto a direction of interest. Although diffusion-weighted MR imaging excels at providing fiber tractography, it yields static images unmatched with dynamic MR data. This work reports an algorithm workflow that estimates strain values in the diffusion MR space by matching corresponding tagged dynamic MR images. We focus on processing a dataset of various human tongue deformations in speech. The geometry of tongue muscle fibers is provided by diffusion tractography, while spatiotemporal motion fields are provided by tagged MR analysis. The tongue's deforming shapes are determined by segmenting a synthetic cine dynamic MR sequence generated from tagged data using a deep neural network. Estimated motion fields are transformed into the diffusion MR space using diffeomorphic registration, eventually leading to strain values computed in the direction of muscle fibers. The method was tested on 78 time volumes acquired during three sets of specific tongue deformations including both speech and protrusion motion. Strain in the line of action of seven internal tongue muscles was extracted and compared both intra- and inter-subject. Resulting compression and stretching patterns of individual muscles revealed the unique behavior of individual muscles and their potential activation pattern.

Generative Self-training for Cross-domain Unsupervised Tagged-to-Cine MRI Synthesis.

Self-training based unsupervised domain adaptation (UDA) has shown great potential to address the problem of domain shift, when applying a trained deep learning model in a source domain to unlabeled target domains. However, while the self-training UDA has demonstrated its effectiveness on discriminative tasks, such as classification and segmentation, via the reliable pseudo-label selection based on the softmax discrete histogram, the self-training UDA for generative tasks, such as image synthesis, is not fully investigated. In this work, we propose a novel generative self-training (GST) UDA framework with continuous value prediction and regression objective for cross-domain image synthesis. Specifically, we propose to filter the pseudo-label with an uncertainty mask, and quantify the predictive confidence of generated images with practical variational Bayes learning. The fast test-time adaptation is achieved by a round-based alternative optimization scheme. We validated our framework on the tagged-to-cine magnetic resonance imaging (MRI) synthesis problem, where datasets in the source and target domains were acquired from different scanners or centers. Extensive validations were carried out to verify our framework against popular adversarial training UDA methods. Results show that our GST, with tagged MRI of test subjects in new target domains, improved the synthesis quality by a large margin, compared with the adversarial training UDA methods.

Accelerated in Vivo Cardiac Diffusion-Tensor MRI Using Residual Deep Learning-based Denoising in Participants with Obesity.

PURPOSE: To develop and assess a residual deep learning algorithm to accelerate in vivo cardiac diffusion-tensor MRI (DT-MRI) by reducing the number of averages while preserving image quality and DT-MRI parameters. MATERIALS AND METHODS: In this prospective study, a denoising convolutional neural network (DnCNN) for DT-MRI was developed; a total of 26 participants, including 20 without obesity (body mass index [BMI] < 30 kg/m2; mean age, 28 years ± 3 [standard deviation]; 11 women) and six with obesity (BMI ≥ 30 kg/m2; mean age, 48 years ± 11; five women), were recruited from June 19, 2019, to July 29, 2020. DT-MRI data were constructed at four averages (4Av), two averages (2Av), and one average (1Av) without and with the application of the DnCNN (4AvDnCNN, 2AvDnCNN, 1AvDnCNN). All data were compared against the reference DT-MRI data constructed at eight averages (8Av). Image quality, characterized by using the signal-to-noise ratio (SNR) and structural similarity index (SSIM), and the DT-MRI parameters of mean diffusivity (MD), fractional anisotropy (FA), and helix angle transmurality (HAT) were quantified. RESULTS: No differences were found in image quality or DT-MRI parameters between the accelerated 4AvDnCNN DT-MRI and the reference 8Av DT-MRI data for the SNR (29.1 ± 2.7 vs 30.5 ± 2.9), SSIM (0.97 ± 0.01), MD (1.3 µm2/msec ± 0.1 vs 1.31 µm2/msec ± 0.11), FA (0.32 ± 0.05 vs 0.30 ± 0.04), or HAT (1.10°/% ± 0.13 vs 1.11°/% ± 0.09). The relationship of a higher MD and lower FA and HAT in individuals with obesity compared with individuals without obesity in reference 8Av DT-MRI measurements was retained in 4AvDnCNN and 2AvDnCNN DT-MRI measurements but was not retained in 4Av or 2Av DT-MRI measurements. CONCLUSION: Cardiac DT-MRI can be performed at an at least twofold-accelerated rate by using DnCNN to preserve image quality and DT-MRI parameter quantification.Keywords: Adults, Cardiac, Obesity, Technology Assessment, MR-Diffusion Tensor Imaging, Heart, Tissue CharacterizationSupplemental material is available for this article.© RSNA, 2021.

A deep joint sparse non-negative matrix factorization framework for identifying the common and subject-specific functional units of tongue motion during speech.

Intelligible speech is produced by creating varying internal local muscle groupings-i.e., functional units-that are generated in a systematic and coordinated manner. There are two major challenges in characterizing and analyzing functional units. First, due to the complex and convoluted nature of tongue structure and function, it is of great importance to develop a method that can accurately decode complex muscle coordination patterns during speech. Second, it is challenging to keep identified functional units across subjects comparable due to their substantial variability. In this work, to address these challenges, we develop a new deep learning framework to identify common and subject-specific functional units of tongue motion during speech. Our framework hinges on joint deep graph-regularized sparse non-negative matrix factorization (NMF) using motion quantities derived from displacements by tagged Magnetic Resonance Imaging. More specifically, we transform NMF with sparse and graph regularizations into modular architectures akin to deep neural networks by means of unfolding the Iterative Shrinkage-Thresholding Algorithm to learn interpretable building blocks and associated weighting map. We then apply spectral clustering to common and subject-specific weighting maps from which we jointly determine the common and subject-specific functional units. Experiments carried out with simulated datasets show that the proposed method achieved on par or better clustering performance over the comparison methods.Experiments carried out with in vivo tongue motion data show that the proposed method can determine the common and subject-specific functional units with increased interpretability and decreased size variability.

Optimized 64-channel array configurations for accelerated simultaneous multislice acquisitions in 3T cardiac MRI.

PURPOSE: Three 64-channel cardiac coils with different detector array configurations were designed and constructed to evaluate acceleration capabilities in simultaneous multislice (SMS) imaging for 3T cardiac MRI. METHODS: Three 64-channel coil array configurations obtained from a simulation-guided design approach were constructed and systematically evaluated regarding their encoding capabilities for accelerated SMS cardiac acquisitions at 3T. Array configuration AUni-sized consists of uniformly distributed equally sized loops in an overlapped arrangement, BGapped uses a gapped array design with symmetrically distributed equally sized loops, and CDense has non-uniform loop density and size, where smaller elements were centered over the heart and larger elements were placed surrounding the target region. To isolate the anatomic variation from differences in the coil configurations, all three array coils were built with identical semi-adjustable housing segments. The arrays' performance was compared using bench-level measurements and imaging performance tests, including signal-to-noise ratio (SNR) maps, array element noise correlation, and SMS acceleration capabilities. Additionally, all cardiac array coils were evaluated on a healthy volunteer. RESULTS: The array configuration CDense with the non-uniformly distributed loop density showed the best overall cardiac imaging performance in both SNR and SMS encoding power, when compared to the other constructed arrays. The diffusion weighted cardiac acquisitions on a healthy volunteer support the favorable accelerated SNR performance of this array configuration. CONCLUSION: Our results indicate that optimized highly parallel cardiac arrays, such as the 64-channel coil with a non-uniform loop size and density improve highly accelerated SMS cardiac MRI in comparison to symmetrically distributed loop array designs.

Diffusion interactions between crossing fibers of the brain.

PURPOSE: Recent observations of several preferred orientations of diffusion in deep white matter may indicate either (a) that axons in different directions are independently bundled in thick sheets and function noninteractively, or more interestingly, (b) that the axons are closely interwoven and would exhibit branching and sharp turns. This study aims to investigate whether the dependence of dMRI Q-ball signal on the interpulse time Δ can decode the smaller-than-voxel-size brain structure, in particular, to distinguish scenarios (a) and (b). METHODS: High-resolution Q-ball images of a healthy brain taken with b=8000  s/mm2 for 3 different values of Δ were analyzed. The exchange of water molecules between crossing fibers was characterized by the fourth Fourier coefficient f4(Δ) of the signal profile in the plane of crossing. To interpret the empirical results, a model consisting of differently oriented parallel sheets of cylinders was developed. Diffusion of water molecules inside and outside cylinders was simulated by the Monte Carlo method. RESULTS: Simulations predict that f4(Δ) , agreeing with the empirical results, must increase with Δ for large b-values, but may peak at a typical Δ that depends on the thickness of the cylinder sheets for intermediate b-values. Thus, the thickness of axon layers in voxels with 2 predominant orientations can be detected from empirical f4(Δ) taken at smaller b-values. CONCLUSION: Based on the simulation results, recommendations are made on how to design a dMRI experiment with optimal b-value and range of Δ in order to measure the thickness of axon sheets in the white matter, hence to distinguish (a) and (b).

Variable flip angle echo planar time-resolved imaging (vFA-EPTI) for fast high-resolution gradient echo myelin water imaging.

Myelin water imaging techniques based on multi-compartment relaxometry have been developed as an important tool to measure myelin concentration in vivo, but are limited by the long scan time of multi-contrast multi-echo acquisition. In this work, a fast imaging technique, termed variable flip angle Echo Planar Time-Resolved Imaging (vFA-EPTI), is developed to acquire multi-echo and multi-flip-angle gradient-echo data with significantly reduced acquisition time, providing rich information for multi-compartment analysis of gradient-echo myelin water imaging (GRE-MWI). The proposed vFA-EPTI method achieved 26 folds acceleration with good accuracy by utilizing an efficient continuous readout, optimized spatiotemporal encoding across echoes and flip angles, as well as a joint subspace reconstruction. An approach to estimate off-resonance field changes between different flip-angle acquisitions was also developed to ensure high-quality joint reconstruction across flip angles. The accuracy of myelin water fraction (MWF) estimate under high acceleration was first validated by a retrospective undersampling experiment using a lengthy fully-sampled data as reference. Prospective experiments were then performed where whole-brain MWF and multi-compartment quantitative maps were obtained in 5 min at 1.5 mm isotropic resolution and 24 min at 1 mm isotropic resolution at 3T. Additionally, ultra-high resolution data at 600 µm isotropic resolution were acquired at 7T, which show detailed structures within the cortex such as the line of Gennari, demonstrating the ability of the proposed method for submillimeter GRE-MWI that can be used to study cortical myeloarchitecture in vivo.

Free-breathing diffusion tensor MRI of the whole left ventricle using second-order motion compensation and multitasking respiratory motion correction.

PURPOSE: We aimed to develop a novel free-breathing cardiac diffusion tensor MRI (DT-MRI) approach, M2-MT-MOCO, capable of whole left ventricular coverage that leverages second-order motion compensation (M2) diffusion encoding and multitasking (MT) framework to efficiently correct for respiratory motion (MOCO). METHODS: Imaging was performed in 16 healthy volunteers and 3 heart failure patients with symptomatic dyspnea. The healthy volunteers were scanned to compare the accuracy of interleaved multislice coverage of the entire left ventricle with a single-slice acquisition and the accuracy of the free-breathing conventional MOCO and MT-MOCO approaches with reference breath-hold DT-MRI. Mean diffusivity (MD), fractional anisotropy (FA), helix angle transmurality (HAT), and intrascan repeatability were quantified and compared. RESULTS: In all subjects, free-breathing M2-MT-MOCO DT-MRI yielded DWI of the entire left ventricle without bulk motion-induced signal loss. No significant differences were seen in the global values of MD, FA, and HAT in the multislice and single-slice acquisitions. Furthermore, global quantification of MD, FA, and HAT were also not significantly different between the MT-MOCO and breath-hold, whereas conventional MOCO yielded significant differences in MD, FA, and HAT with MT-MOCO and FA with breath-hold. In heart failure patients, M2-MT-MOCO DT-MRI was feasible yielding higher MD, lower FA, and lower HAT compared with healthy volunteers. Substantial agreement was found between repeated scans across all subjects for MT-MOCO. CONCLUSION: M2-MT-MOCO enables free-breathing DT-MRI of the entire left ventricle in 10 min, while preserving quantification of myocardial microstructure compared to breath-held and single-slice acquisitions and is feasible in heart failure patients.

Identifying the Common and Subject-specific Functional Units of Speech Movements via a Joint Sparse Non-negative Matrix Factorization Framework.

The tongue is capable of producing intelligible speech because of successful orchestration of muscle groupings-i.e., functional units-of the highly complex muscles over time. Due to the different motions that tongues produce, functional units are transitional structures which transform muscle activity to surface tongue geometry and they vary significantly from one subject to another. In order to compare and contrast the location and size of functional units in the presence of such substantial inter-person variability, it is essential to study both common and subject-specific functional units in a group of people carrying out the same speech task. In this work, a new normalization technique is presented to simultaneously identify the common and subject-specific functional units defined in the tongue when tracked by tagged magnetic resonance imaging. To achieve our goal, a joint sparse non-negative matrix factorization framework is used, which learns a set of building blocks and subject-specific as well as common weighting matrices from motion quantities extracted from displacements. A spectral clustering technique is then applied to the subject-specific and common weighting matrices to determine the subject-specific functional units for each subject and the common functional units across subjects. Our experimental results using in vivo tongue motion data show that our approach is able to identify the common and subject-specific functional units with reduced size variability of tongue motion during speech.

Echo planar time-resolved imaging with subspace reconstruction and optimized spatiotemporal encoding.

PURPOSE: To develop new encoding and reconstruction techniques for fast multi-contrast/quantitative imaging. METHODS: The recently proposed Echo Planar Time-resolved Imaging (EPTI) technique can achieve fast distortion- and blurring-free multi-contrast/quantitative imaging. In this work, a subspace reconstruction framework is developed to improve the reconstruction accuracy of EPTI at high encoding accelerations. The number of unknowns in the reconstruction is significantly reduced by modeling the temporal signal evolutions using low-rank subspace. As part of the proposed reconstruction approach, a B0 -update algorithm and a shot-to-shot B0 variation correction method are developed to enable the reconstruction of high-resolution tissue phase images and to mitigate artifacts from shot-to-shot phase variations. Moreover, the EPTI concept is extended to 3D k-space for 3D GE-EPTI, where a new "temporal-variant" of CAIPI encoding is proposed to further improve performance. RESULTS: The effectiveness of the proposed subspace reconstruction was demonstrated first in 2D GESE EPTI, where the reconstruction achieved higher accuracy when compared to conventional B0 -informed GRAPPA. For 3D GE-EPTI, a retrospective undersampling experiment demonstrates that the new temporal-variant CAIPI encoding can achieve up to 72× acceleration with close to 2× reduction in reconstruction error when compared to conventional spatiotemporal-CAIPI encoding. In a prospective undersampling experiment, high-quality whole-brain T2∗ and tissue phase maps at 1 mm isotropic resolution were acquired in 52 seconds at 3T using 3D GE-EPTI with temporal-variant CAIPI encoding. CONCLUSION: The proposed subspace reconstruction and optimized temporal-variant CAIPI encoding can further improve the performance of EPTI for fast quantitative mapping.

Propeller echo-planar time-resolved imaging with dynamic encoding (PEPTIDE).

PURPOSE: To develop a motion-robust extension to the recently developed echo-planar time-resolved imaging (EPTI) approach, referred to as PROPELLER EPTI with dynamic encoding (PEPTIDE), by incorporating rotations into the rapid, multishot acquisition to enable shot-to-shot motion correction. METHODS: Echo-planar time-resolved imaging is a multishot EPI-based approach that allows extremely rapid acquisition of distortion-free and blurring-free multicontrast imaging and quantitative mapping. By combining k-space encoding rotations into the EPTI sampling strategy to repeatedly sample the low-resolution k-space center, PEPTIDE enables significant tolerance to shot-to-shot motion and B0 phase variations. Retrospective PEPTIDE data sets are created through a combination of in vivo EPTI data sets with rotationally acquired protocols, to enable direct comparison of the 2 methods and their robustness to identical motion. The PEPTIDE data sets are also prospectively acquired and again compared with EPTI, in the presence of true subject motion. RESULTS: The PEPTIDE approach is shown to be motion-robust to even severe subject motion (demonstrated > 30° in-plane rotation, alongside translational and through-plane motion), while maintaining the rapid encoding benefits of the EPTI technique. The technique enables accurate quantitative maps to be calculated from even severe motion data sets. While the performance of the motion correction depends on the type and severity of motion encountered, in all cases PEPTIDE significantly increases image quality in the presence of motion comparative to conventional EPTI. CONCLUSION: The newly developed PEPTIDE technique combines a high degree of motion tolerance into the EPTI framework, enabling highly rapid acquisition of distortion-free and blurring-free images at multiple TEs in the presence of motion.

Speech Map: A Statistical Multimodal Atlas of 4D Tongue Motion During Speech from Tagged and Cine MR Images.

Quantitative measurement of functional and anatomical traits of 4D tongue motion in the course of speech or other lingual behaviors remains a major challenge in scientific research and clinical applications. Here, we introduce a statistical multimodal atlas of 4D tongue motion using healthy subjects, which enables a combined quantitative characterization of tongue motion in a reference anatomical configuration. This atlas framework, termed Speech Map, combines cine- and tagged-MRI in order to provide both the anatomic reference and motion information during speech. Our approach involves a series of steps including (1) construction of a common reference anatomical configuration from cine-MRI, (2) motion estimation from tagged-MRI, (3) transformation of the motion estimations to the reference anatomical configuration, and (4) computation of motion quantities such as Lagrangian strain. Using this framework, the anatomic configuration of the tongue appears motionless, while the motion fields and associated strain measurements change over the time course of speech. In addition, to form a succinct representation of the high-dimensional and complex motion fields, principal component analysis is carried out to characterize the central tendencies and variations of motion fields of our speech tasks. Our proposed method provides a platform to quantitatively and objectively explain the differences and variability of tongue motion by illuminating internal motion and strain that have so far been intractable. The findings are used to understand how tongue function for speech is limited by abnormal internal motion and strain in glossectomy patients.

Differentiating post-cancer from healthy tongue muscle coordination patterns during speech using deep learning.

The ability to differentiate post-cancer from healthy tongue muscle coordination patterns is necessary for the advancement of speech motor control theories and for the development of therapeutic and rehabilitative strategies. A deep learning approach is presented to classify two groups using muscle coordination patterns from magnetic resonance imaging (MRI). The proposed method uses tagged-MRI to track the tongue's internal tissue points and atlas-driven non-negative matrix factorization to reduce the dimensionality of the deformation fields. A convolutional neural network is applied to the classification task yielding an accuracy of 96.90%, offering the potential to the development of therapeutic or rehabilitative strategies in speech-related disorders.

Echo planar time-resolved imaging (EPTI).

PURPOSE: To develop an efficient distortion- and blurring-free multi-shot EPI technique for time-resolved multiple-contrast and/or quantitative imaging. METHODS: EPI is a commonly used sequence but suffers from geometric distortions and blurring. Here, we introduce a new multi-shot EPI technique termed echo planar time-resolved imaging (EPTI), which has the ability to rapidly acquire distortion- and blurring-free multi-contrast data set. The EPTI approach performs encoding in ky -t space and uses a new highly accelerated spatio-temporal CAIPI sampling trajectory to take advantage of signal correlation along these dimensions. Through this acquisition and a B0 -informed parallel imaging reconstruction, hundreds of "time-resolved" distortion- and blurring-free images at different TEs across the EPI readout window can be created at sub-millisecond temporal increments using a small number of EPTI shots. Moreover, a method for self-estimation and correction of shot-to-shot B0 variations was developed. Simultaneous multi-slice acquisition was also incorporated to further improve the acquisition efficiency. RESULTS: We evaluated EPTI under varying simulated acceleration factors, B0 -inhomogeneity, and shot-to-shot B0 variations to demonstrate its ability to provide distortion- and blurring-free images at multiple TEs. Two variants of EPTI were demonstrated in vivo at 3T: (1) a combined gradient- and spin-echo EPTI for quantitative mapping of T2 , T2* , proton density, and susceptibility at 1.1 × 1.1 × 3 mm3 whole-brain in 28 s (0.8 s/slice), and (2) a gradient-echo EPTI, for multi-echo and quantitative T2* fMRI at 2 × 2 × 3 mm3 whole-brain at a 3.3 s temporal resolution. CONCLUSION: EPTI is a new approach for multi-contrast and/or quantitative imaging that can provide fast acquisition of distortion- and blurring-free images at multiple TEs.

Tilted-CAIPI for highly accelerated distortion-free EPI with point spread function (PSF) encoding.

PURPOSE: To develop a method for fast distortion- and blurring-free imaging. THEORY: EPI with point-spread-function (PSF) mapping can achieve distortion- and blurring-free imaging at a cost of long acquisition time. In this study, an acquisition/reconstruction technique, termed "tilted-CAIPI," is proposed to achieve >20× acceleration for PSF-EPI. The proposed method systematically optimized the k-space sampling trajectory with B0 -inhomogeneity-informed reconstruction, to exploit the inherent signal correlation in PSF-EPI and take full advantage of coil sensitivity. Susceptibility-induced phase accumulation is regarded as an additional encoding that is estimated by calibration data and integrated into reconstruction. Self-navigated phase correction was developed to correct shot-to-shot phase variation in diffusion imaging. METHODS: Tilted-CAIPI was implemented at 3T, with incorporation of partial Fourier and simultaneous multislice to achieve further accelerations. T2 -weighted, T2* -weighted, and diffusion-weighted imaging experiments were conducted to evaluate the proposed method. RESULTS: The ability of tilted-CAIPI to provide highly accelerated imaging without distortion and blurring was demonstrated through in vivo brain experiments, where only 8 shots per simultaneous slice group were required to provide high-quality, high-SNR imaging at 0.8-1 mm resolution. CONCLUSION: Tilted-CAIPI achieved fast distortion- and blurring-free imaging with high SNR. Whole-brain T2 -weighted, T2* -weighted, and diffusion imaging can be obtained in just 15-60 s.

A Sparse Non-Negative Matrix Factorization Framework for Identifying Functional Units of Tongue Behavior From MRI.

Muscle coordination patterns of lingual behaviors are synergies generated by deforming local muscle groups in a variety of ways. Functional units are functional muscle groups of local structural elements within the tongue that compress, expand, and move in a cohesive and consistent manner. Identifying the functional units using tagged-magnetic resonance imaging (MRI) sheds light on the mechanisms of normal and pathological muscle coordination patterns, yielding improvement in surgical planning, treatment, or rehabilitation procedures. In this paper, to mine this information, we propose a matrix factorization and probabilistic graphical model framework to produce building blocks and their associated weighting map using motion quantities extracted from tagged-MRI. Our tagged-MRI imaging and accurate voxel-level tracking provide previously unavailable internal tongue motion patterns, thus revealing the inner workings of the tongue during speech or other lingual behaviors. We then employ spectral clustering on the weighting map to identify the cohesive regions defined by the tongue motion that may involve multiple or undocumented regions. To evaluate our method, we perform a series of experiments. We first use two-dimensional images and synthetic data to demonstrate the accuracy of our method. We then use three-dimensional synthetic and in vivo tongue motion data using protrusion and simple speech tasks to identify subject-specific and data-driven functional units of the tongue in localized regions.