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This work develops and evaluates a self‐navigated variable density spiral (VDS)‐based manifold regularization scheme to prospectively improve dynamic speech magnetic resonance imaging (MRI) at 3 T. Short readout duration spirals (1.3‐ms long) were used to minimize sensitivity to off‐resonance. A custom 16‐channel speech coil was used for improved parallel imaging of vocal tract structures. The manifold model leveraged similarities between frames sharing similar vocal tract postures without explicit motion binning. The self‐navigating capability of VDS was leveraged to learn the Laplacian structure of the manifold. Reconstruction was posed as a sensitivity‐encoding–based nonlocal soft‐weighted temporal regularization scheme. Our approach was compared with view‐sharing, low‐rank, temporal finite difference, extra dimension‐based sparsity reconstruction constraints. Undersampling experiments were conducted on five volunteers performing repetitive and arbitrary speaking tasks at different speaking rates. Quantitative evaluation in terms of mean square error over moving edges was performed in a retrospective undersampling experiment on one volunteer. For prospective undersampling, blinded image quality evaluation in the categories of alias artifacts, spatial blurring, and temporal blurring was performed by three experts in voice research. Region of interest analysis at articulator boundaries was performed in both experiments to assess articulatory motion. Improved performance with manifold reconstruction constraints was observed over existing constraints. With prospective undersampling, a spatial resolution of 2.4 × 2.4 mm2/pixel and a temporal resolution of 17.4 ms/frame for single‐slice imaging, and 52.2 ms/frame for concurrent three‐slice imaging, were achieved. We demonstrated implicit motion binning by analyzing the mechanics of the Laplacian matrix. Manifold regularization demonstrated superior image quality scores in reducing spatial and temporal blurring compared with all other reconstruction constraints. While it exhibited faint (nonsignificant) alias artifacts that were similar to temporal finite difference, it provided statistically significant improvements compared with the other constraints. In conclusion, the self‐navigated manifold regularized scheme enabled robust high spatiotemporal resolution dynamic speech MRI at 3 T. [ABSTRACT FROM AUTHOR]

Purpose: To develop a custom coil and evaluate its utility for accelerated upper and infraglottic airway MRI at 3 T. Methods: A 16‐channel flexible and anatomy‐conforming coil was developed to provide localized sensitivity over upper and infraglottic airway regions of interest. Parallel‐imaging capabilities were compared against existing head and head–neck coils. SENSE geometry factor losses were quantified for retrospectively accelerating 3D MRI. Blinded image‐quality ratings from two experts were performed. Spiral GRAPPA reconstructions were evaluated for a speaking task at a time resolution of 40 ms. Contrast‐to‐noise ratios between air and tissue at key landmarks along the vocal tract were compared. SENSE imaging with the custom coil in the lateral recumbent posture was evaluated. Multislice imaging was performed to image swallowing at 17 ms/frame via constrained reconstruction. Results: The custom coil showed improved SENSE imaging up to 3‐fold acceleration when accelerated along either the anterior–posterior or the superior–inferior direction and a net 4‐fold acceleration when accelerated along both directions. Spiral GRAPPA reconstructions with the custom coil showed higher contrast‐to‐noise ratio when compared with existing coils. In the lateral posture, robust SENSE imaging was achieved at up to 2‐fold and 3‐fold acceleration levels in the superior–inferior and anterior–posterior directions, respectively. Key events of swallowing in the multislice dynamic images were identified by an otolaryngologist. Conclusion: The coil provided improved parallel imaging of upper and infraglottic airway in both supine and lateral recumbent postures. It enabled efficient accelerated dynamic imaging of speaking and swallowing. [ABSTRACT FROM AUTHOR]

Dynamic magnetic resonance imaging has emerged as a powerful modality for investigating upper-airway function during speech production. Analyzing the changes in the vocal tract airspace, including the position of soft-tissue articulators (e.g., the tongue and velum), enhances our understanding of speech production. The advent of various fast speech MRI protocols based on sparse sampling and constrained reconstruction has led to the creation of dynamic speech MRI datasets on the order of 80–100 image frames/second. In this paper, we propose a stacked transfer learning U-NET model to segment the deforming vocal tract in 2D mid-sagittal slices of dynamic speech MRI. Our approach leverages (a) low- and mid-level features and (b) high-level features. The low- and mid-level features are derived from models pre-trained on labeled open-source brain tumor MR and lung CT datasets, and an in-house airway labeled dataset. The high-level features are derived from labeled protocol-specific MR images. The applicability of our approach to segmenting dynamic datasets is demonstrated in data acquired from three fast speech MRI protocols: Protocol 1: 3 T-based radial acquisition scheme coupled with a non-linear temporal regularizer, where speakers were producing French speech tokens; Protocol 2: 1.5 T-based uniform density spiral acquisition scheme coupled with a temporal finite difference (FD) sparsity regularization, where speakers were producing fluent speech tokens in English, and Protocol 3: 3 T-based variable density spiral acquisition scheme coupled with manifold regularization, where speakers were producing various speech tokens from the International Phonetic Alphabetic (IPA). Segments from our approach were compared to those from an expert human user (a vocologist), and the conventional U-NET model without transfer learning. Segmentations from a second expert human user (a radiologist) were used as ground truth. Evaluations were performed using the quantitative DICE similarity metric, the Hausdorff distance metric, and segmentation count metric. This approach was successfully adapted to different speech MRI protocols with only a handful of protocol-specific images (e.g., of the order of 20 images), and provided accurate segmentations similar to those of an expert human. [ABSTRACT FROM AUTHOR]

Constrained reconstruction in magnetic resonance imaging (MRI) allows the use of prior information through constraints to improve reconstructed images. These constraints often take the form of regularization terms in the objective function used for reconstruction. Constrained reconstruction leads to images which appear to have fewer artifacts than reconstructions without constraints but because the methods are typically nonlinear, the reconstructed images have artifacts whose structure is hard to predict. In this work, we compared different methods of optimizing the regularization parameter using a total variation (TV) constraint in the spatial domain and sparsity in the wavelet domain for one-dimensional (2.56×) undersampling using variable density undersampling. We compared the mean squared error (MSE), structural similarity (SSIM), L-curve and the area under the receiver operating characteristic (AUC) using a linear discriminant for detecting a small and a large signal. We used a signal-known-exactly task with varying backgrounds in a simulation where the anatomical variation was the major source of clutter for the detection task. Our results show that the AUC dependence on regularization parameters varies with the imaging task (i.e. the signal being detected). The choice of regularization parameters for MSE, SSIM, L-curve and AUC were similar. We also found that a model-based reconstruction including TV and wavelet sparsity did slightly better in terms of AUC than just enforcing data consistency but using these constraints resulted in much better MSE and SSIM. These results suggest that the increased performance in MSE and SSIM over-estimate the improvement in detection performance for the tasks in this paper. The MSE and SSIM metrics show a big difference in performance where the difference in AUC is small. To our knowledge, this is the first time that signal detection with varying backgrounds has been used to optimize constrained reconstruction in MRI. [ABSTRACT FROM AUTHOR]

Real-time magnetic resonance imaging (RT-MRI) of human speech production is enabling significant advances in speech science, linguistics, bio-inspired speech technology development, and clinical applications. Easy access to RT-MRI is however limited, and comprehensive datasets with broad access are needed to catalyze research across numerous domains. The imaging of the rapidly moving articulators and dynamic airway shaping during speech demands high spatio-temporal resolution and robust reconstruction methods. Further, while reconstructed images have been published, to-date there is no open dataset providing raw multi-coil RT-MRI data from an optimized speech production experimental setup. Such datasets could enable new and improved methods for dynamic image reconstruction, artifact correction, feature extraction, and direct extraction of linguistically-relevant biomarkers. The present dataset offers a unique corpus of 2D sagittal-view RT-MRI videos along with synchronized audio for 75 participants performing linguistically motivated speech tasks, alongside the corresponding public domain raw RT-MRI data. The dataset also includes 3D volumetric vocal tract MRI during sustained speech sounds and high-resolution static anatomical T2-weighted upper airway MRI for each participant. Measurement(s) Speech • vocal tract Technology Type(s) magnetic resonance imaging Factor Type(s) age • sex • native/non-native speaker Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.14892921 [ABSTRACT FROM AUTHOR]

Dynamic magnetic resonance imaging (MRI) can be used to scan a wide range of dynamic processes within the body, including the motion of internal organs, tissue-level nuclear magnetic resonance (NMR) relaxation, and dynamic contrast enhancement (DCE) of dye agents. The ability of MRI to safely provide unique soft-tissue contrast and comprehensive functional information has made dynamic MRI central to a number of imaging exams for cardiac, interventional, vocal tract, cancer, and gastrointestinal applications, among others. Unfortunately, MRI is a notoriously slow imaging modality due to fundamental physical and physiological limitations. These limitations result in tradeoffs between spatial and temporal resolutions, spatial coverage, and the signal-to-noise ratio and have made dynamic MRI a challenging technical goal. [ABSTRACT FROM AUTHOR]

Purpose: To apply tracer kinetic models as temporal constraints during reconstruction of under‐sampled brain tumor dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI). Methods: A library of concentration vs time profiles is simulated for a range of physiological kinetic parameters. The library is reduced to a dictionary of temporal bases, where each profile is approximated by a sparse linear combination of the bases. Image reconstruction is formulated as estimation of concentration profiles and sparse model coefficients with a fixed sparsity level. Simulations are performed to evaluate modeling error, and error statistics in kinetic parameter estimation in presence of noise. Retrospective under‐sampling experiments are performed on a brain tumor DCE digital reference object (DRO), and 12 brain tumor in‐vivo 3T datasets. The performances of the proposed under‐sampled reconstruction scheme and an existing compressed sensing‐based temporal finite‐difference (tFD) under‐sampled reconstruction were compared against the fully sampled inverse Fourier Transform‐based reconstruction. Results: Simulations demonstrate that sparsity levels of 2 and 3 model the library profiles from the Patlak and extended Tofts‐Kety (ETK) models, respectively. Noise sensitivity analysis showed equivalent kinetic parameter estimation error statistics from noisy concentration profiles, and model approximated profiles. DRO‐based experiments showed good fidelity in recovery of kinetic maps from 20‐fold under‐sampled data. In‐vivo experiments demonstrated reduced bias and uncertainty in kinetic mapping with the proposed approach compared to tFD at under‐sampled reduction factors >= 20. Conclusions: Tracer kinetic models can be applied as temporal constraints during brain tumor DCE‐MRI reconstruction. The proposed under‐sampled scheme resulted in model parameter estimates less biased with respect to conventional fully sampled DCE MRI reconstructions and parameter estimation. The approach is flexible, can use nonlinear kinetic models, and does not require tuning of regularization parameters. [ABSTRACT FROM AUTHOR]

Purpose: To develop and evaluate a technique for 3D dynamic MRI of the full vocal tract at high temporal resolution during natural speech. Methods: We demonstrate 2.4 × 2.4 × 5.8 mm3 spatial resolution, 61‐ms temporal resolution, and a 200 × 200 × 70 mm3 FOV. The proposed method uses 3D gradient‐echo imaging with a custom upper‐airway coil, a minimum‐phase slab excitation, stack‐of‐spirals readout, pseudo golden‐angle view order in kx‐ky, linear Cartesian order along kz, and spatiotemporal finite difference constrained reconstruction, with 13‐fold acceleration. This technique is evaluated using in vivo vocal tract airway data from 2 healthy subjects acquired at 1.5T scanner, 1 with synchronized audio, with 2 tasks during production of natural speech, and via comparison with interleaved multislice 2D dynamic MRI. Results: This technique captured known dynamics of vocal tract articulators during natural speech tasks including tongue gestures during the production of consonants "s" and "l" and of consonant–vowel syllables, and was additionally consistent with 2D dynamic MRI. Coordination of lingual (tongue) movements for consonants is demonstrated via volume‐of‐interest analysis. Vocal tract area function dynamics revealed critical lingual constriction events along the length of the vocal tract for consonants and vowels. Conclusion: We demonstrate feasibility of 3D dynamic MRI of the full vocal tract, with spatiotemporal resolution adequate to visualize lingual movements for consonants and vocal tact shaping during natural productions of consonant–vowel syllables, without requiring multiple repetitions. [ABSTRACT FROM AUTHOR]

Purpose: To improve the depiction and tracking of vocal tract articulators in spiral real‐time MRI (RT‐MRI) of speech production by estimating and correcting for dynamic changes in off‐resonance. Methods: The proposed method computes a dynamic field map from the phase of single‐TE dynamic images after a coil phase compensation where complex coil sensitivity maps are estimated from the single‐TE dynamic scan itself. This method is tested using simulations and in vivo data. The depiction of air–tissue boundaries is evaluated quantitatively using a sharpness metric and visual inspection. Results: Simulations demonstrate that the proposed method provides robust off‐resonance correction for spiral readout durations up to 5 ms at 1.5T. In ‐vivo experiments during human speech production demonstrate that image sharpness is improved in a majority of data sets at air–tissue boundaries including the upper lip, hard palate, soft palate, and tongue boundaries, whereas the lower lip shows little improvement in the edge sharpness after correction. Conclusion: Dynamic off‐resonance correction is feasible from single‐TE spiral RT‐MRI data, and provides a practical performance improvement in articulator sharpness when applied to speech production imaging. [ABSTRACT FROM AUTHOR]

Purpose: To develop and evaluate a model‐based reconstruction framework for joint arterial input function (AIF) and kinetic parameter estimation from undersampled brain tumor dynamic contrast‐enhanced MRI (DCE‐MRI) data. Methods: The proposed method poses the tracer‐kinetic (TK) model as a model consistency constraint, enabling the flexible inclusion of different TK models and TK solvers, and the joint estimation of the AIF. The proposed method is evaluated using an anatomic realistic digital reference object (DRO), and nine retrospectively down‐sampled brain tumor DCE‐MRI datasets. We also demonstrate application to 30‐fold prospectively undersampled brain tumor DCE‐MRI. Results: In DRO studies with up to 60‐fold undersampling, the proposed method provided TK maps with low error that were comparable to fully sampled data and were demonstrated to be compatible with a third‐party TK solver. In retrospective undersampling studies, this method provided patient‐specific AIF with normalized root mean‐squared‐error (normalized by the 90th percentile value) less than 8% at up to 100‐fold undersampling. In the 30‐fold undersampled prospective study, the proposed method provided high‐resolution whole‐brain TK maps and patient‐specific AIF. Conclusion: The proposed model‐based DCE‐MRI reconstruction enables the use of different TK solvers with a model consistency constraint and enables joint estimation of patient‐specific AIF. TK maps and patient‐specific AIF with high fidelity can be reconstructed at up to 100‐fold undersampling in k,t‐space. Magn Reson Med 79:2804–2815, 2018. © 2017 International Society for Magnetic Resonance in Medicine. [ABSTRACT FROM AUTHOR]

Purpose To evaluate the feasibility of through-time spiral generalized autocalibrating partial parallel acquisition (GRAPPA) for low-latency accelerated real-time MRI of speech. Methods Through-time spiral GRAPPA (spiral GRAPPA), a fast linear reconstruction method, is applied to spiral (k-t) data acquired from an eight-channel custom upper-airway coil. Fully sampled data were retrospectively down-sampled to evaluate spiral GRAPPA at undersampling factors R = 2 to 6. Pseudo-golden-angle spiral acquisitions were used for prospective studies. Three subjects were imaged while performing a range of speech tasks that involved rapid articulator movements, including fluent speech and beat-boxing. Spiral GRAPPA was compared with view sharing, and a parallel imaging and compressed sensing (PI-CS) method. Results Spiral GRAPPA captured spatiotemporal dynamics of vocal tract articulators at undersampling factors ≤4. Spiral GRAPPA at 18 ms/frame and 2.4 mm2/pixel outperformed view sharing in depicting rapidly moving articulators. Spiral GRAPPA and PI-CS provided equivalent temporal fidelity. Reconstruction latency per frame was 14 ms for view sharing and 116 ms for spiral GRAPPA, using a single processor. Spiral GRAPPA kept up with the MRI data rate of 18ms/frame with eight processors. PI-CS required 17 minutes to reconstruct 5 seconds of dynamic data. Conclusion Spiral GRAPPA enabled 4-fold accelerated real-time MRI of speech with a low reconstruction latency. This approach is applicable to wide range of speech RT-MRI experiments that benefit from real-time feedback while visualizing rapid articulator movement. Magn Reson Med 78:2275-2282, 2017. © 2017 International Society for Magnetic Resonance in Medicine. [ABSTRACT FROM AUTHOR]

Purpose The purpose of this work was to develop and evaluate a T1-weighted dynamic contrast enhanced (DCE) MRI methodology where tracer-kinetic (TK) parameter maps are directly estimated from undersampled (k,t)-space data. Theory and Methods The proposed reconstruction involves solving a nonlinear least squares optimization problem that includes explicit use of a full forward model to convert parameter maps to (k,t)-space, utilizing the Patlak TK model. The proposed scheme is compared against an indirect method that creates intermediate images by parallel imaging and compressed sensing before to TK modeling. Thirteen fully sampled brain tumor DCE-MRI scans with 5-second temporal resolution are retrospectively undersampled at rates R = 20, 40, 60, 80, and 100 for each dynamic frame. TK maps are quantitatively compared based on root mean-squared-error (rMSE) and Bland-Altman analysis. The approach is also applied to four prospectively R = 30 undersampled whole-brain DCE-MRI data sets. Results In the retrospective study, the proposed method performed statistically better than indirect method at R ≥ 80 for all 13 cases. This approach provided restoration of TK parameter values with less errors in tumor regions of interest, an improvement compared to a state-of-the-art indirect method. Applied prospectively, the proposed method provided whole-brain, high-resolution TK maps with good image quality. Conclusion Model-based direct estimation of TK maps from k,t-space DCE-MRI data is feasible and is compatible up to 100-fold undersampling. Magn Reson Med 78:1566-1578, 2017. © 2016 International Society for Magnetic Resonance in Medicine. [ABSTRACT FROM AUTHOR]

Static anatomical and real-time dynamic magnetic resonance imaging (RT-MRI) of the upper airway is a valuable method for studying speech production in research and clinical settings. The test–retest repeatability of quantitative imaging biomarkers is an important parameter, since it limits the effect sizes and intragroup differences that can be studied. Therefore, this study aims to present a framework for determining the test–retest repeatability of quantitative speech biomarkers from static MRI and RT-MRI, and apply the framework to healthy volunteers. Subjects (n=8, 4 females, 4 males) are imaged in two scans on the same day, including static images and dynamic RT-MRI of speech tasks. The inter-study agreement is quantified using intraclass correlation coefficient (ICC) and mean within-subject standard deviation (σe). Inter-study agreement is strong to very strong for static measures (ICC: min/median/max 0.71/0.89/0.98, σe: 0.90/2.20/6.72 mm), poor to strong for dynamic RT-MRI measures of articulator motion range (ICC: 0.26/0.75/0.90, σe: 1.6/2.5/3.6 mm), and poor to very strong for velocities (ICC: 0.21/0.56/0.93, σe: 2.2/4.4/16.7 cm/s). In conclusion, this study characterizes repeatability of static and dynamic MRI-derived speech biomarkers using state-of-the-art imaging. The introduced framework can be used to guide future development of speech biomarkers. Test–retest MRI data are provided free for research use. [ABSTRACT FROM AUTHOR]

Purpose To introduce a fast algorithm for motion-compensated accelerated dynamic MRI. Methods An efficient patch smoothness regularization scheme, which implicitly compensates for inter-frame motion, is introduced to recover dynamic MRI data from highly undersampled measurements. The regularization prior is a sum of distances between each rectangular patch in the dataset with other patches in the dataset using a saturating distance metric. Unlike current motion estimation and motion compensation (ME-MC) methods, the proposed scheme does not require reference frames or complex motion models. The proposed algorithm, which alternates between inter-patch shrinkage step and conjugate gradient algorithm, is considerably more computationally efficient than ME-MC methods. The reconstructions obtained using the proposed algorithm is compared against state-of-the-art methods. Results The proposed method is observed to yield reconstructions with minimal spatiotemporal blurring and motion artifacts. In comparison to the existing state-of-the-art ME-MC methods, PRICE provides comparable or even better image quality with faster reconstruction times (approximately nine times faster). Conclusion The presented scheme enables computationally efficient and effective motion-compensated reconstruction in a variety of applications with large inter-frame motion and contrast changes. This algorithm could be seen as an alternative over the current state-of-the-art ME-MC schemes that are computationally expensive. Magn Reson Med 77:1238-1248, 2017. © 2016 International Society for Magnetic Resonance in Medicine [ABSTRACT FROM AUTHOR]

Purpose The aim of this work was to develop and evaluate an MRI-based system for study of dynamic vocal tract shaping during speech production, which provides high spatial and temporal resolution. Methods The proposed system utilizes (a) custom eight-channel upper airway coils that have high sensitivity to upper airway regions of interest, (b) two-dimensional golden angle spiral gradient echo acquisition, (c) on-the-fly view-sharing reconstruction, and (d) off-line temporal finite difference constrained reconstruction. The system also provides simultaneous noise-cancelled and temporally aligned audio. The system is evaluated in 3 healthy volunteers, and 1 tongue cancer patient, with a broad range of speech tasks. Results We report spatiotemporal resolutions of 2.4 × 2.4 mm2 every 12 ms for single-slice imaging, and 2.4 × 2.4 mm2 every 36 ms for three-slice imaging, which reflects roughly 7-fold acceleration over Nyquist sampling. This system demonstrates improved temporal fidelity in capturing rapid vocal tract shaping for tasks, such as producing consonant clusters in speech, and beat-boxing sounds. Novel acoustic-articulatory analysis was also demonstrated. Conclusion A synergistic combination of custom coils, spiral acquisitions, and constrained reconstruction enables visualization of rapid speech with high spatiotemporal resolution in multiple planes. Magn Reson Med 77:112-125, 2017. © 2016 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Purpose: To clinically evaluate a highly accelerated T1-weighted dynamic contrast-enhanced (DCE) MRI technique that provides high spatial resolution and whole-brain coverage via undersampling and constrained reconstruction with multiple sparsity constraints. Methods: Conventional (rate-2 SENSE) and experimental DCE-MRI (rate-30) scans were performed 20 minutes apart in 15 brain tumor patients. The conventional clinical DCE-MRI had voxel dimensions 0.9×1.3×7.0 mm3, FOV 22×22×4.2 cm3, and the experimental DCE-MRI had voxel dimensions 0.9×0.9×1.9 mm3, and broader coverage 22×22×19 cm3. Temporal resolution was 5 s for both protocols. Time-resolved images and blood-brain barrier permeability maps were qualitatively evaluated by two radiologists. Results: The experimental DCE-MRI scans showed no loss of qualitative information in any of the cases, while achieving substantially higher spatial resolution and whole-brain spatial coverage. Average qualitative scores (from 0 to 3) were 2.1 for the experimental scans and 1.1 for the conventional clinical scans. Conclusions: The proposed DCE-MRI approach provides clinically superior image quality with higher spatial resolution and coverage than currently available approaches. These advantages may allow comprehensive permeability mapping in the brain, which is especially valuable in the setting of large lesions or multiple lesions spread throughout the brain. [ABSTRACT FROM AUTHOR]

Purpose To introduce a blind compressed sensing (BCS) framework to accelerate multi-parameter MR mapping, and demonstrate its feasibility in high-resolution, whole-brain and T2 mapping. Methods BCS models the evolution of magnetization at every pixel as a sparse linear combination of bases in a dictionary. Unlike compressed sensing, the dictionary and the sparse coefficients are jointly estimated from undersampled data. Large number of non-orthogonal bases in BCS accounts for more complex signals than low rank representations. The low degree of freedom of BCS, attributed to sparse coefficients, translates to fewer artifacts at high acceleration factors ( R). Results From 2D retrospective undersampling experiments, the mean square errors in and T2 maps were observed to be within 0.1% up to R = 10. BCS was observed to be more robust to patient-specific motion as compared to other compressed sensing schemes and resulted in minimal degradation of parameter maps in the presence of motion. Our results suggested that BCS can provide an acceleration factor of 8 in prospective 3D imaging with reasonable reconstructions. Conclusion BCS considerably reduces scan time for multiparameter mapping of the whole brain with minimal artifacts, and is more robust to motion-induced signal changes compared to current compressed sensing and principal component analysis-based techniques. Magn Reson Med 75:1175-1186, 2016. © 2015 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Real-time magnetic resonance imaging (RT-MRI) is being increasingly used for speech and vocal production research studies. Several imaging protocols have emerged based on advances in RT-MRI acquisition, reconstruction, and audio-processing methods. This review summarizes the state-of-the-art, discusses technical considerations, and provides specific guidance for new groups entering this field. We provide recommendations for performing RT-MRI of the upper airway. This is a consensus statement stemming from the ISMRM-endorsed Speech MRI summit held in Los Angeles, February 2014. A major unmet need identified at the summit was the need for consensus on protocols that can be easily adapted by researchers equipped with conventional MRI systems. To this end, we provide a discussion of tradeoffs in RT-MRI in terms of acquisition requirements, a priori assumptions, artifacts, computational load, and performance for different speech tasks. We provide four recommended protocols and identify appropriate acquisition and reconstruction tools. We list pointers to open-source software that facilitate implementation. We conclude by discussing current open challenges in the methodological aspects of RT-MRI of speech. [ABSTRACT FROM AUTHOR]

We propose a novel deformation corrected compressed sensing (DC-CS) framework to recover contrast enhanced dynamic magnetic resonance images from undersampled measurements. We introduce a formulation that is capable of handling a wide class of sparsity/compactness priors on the deformation corrected dynamic signal. In this work, we consider example compactness priors such as sparsity in temporal Fourier domain, sparsity in temporal finite difference domain, and nuclear norm penalty to exploit low rank structure. Using variable splitting, we decouple the complex optimization problem to simpler and well understood sub problems; the resulting algorithm alternates between simple steps of shrinkage-based denoising, deformable registration, and a quadratic optimization step. Additionally, we employ efficient continuation strategies to reduce the risk of convergence to local minima. The decoupling enabled by the proposed scheme enables us to apply this scheme to contrast enhanced MRI applications. Through experiments on numerical phantom and in vivo myocardial perfusion MRI datasets, we observe superior image quality of the proposed DC-CS scheme in comparison to the classical k-t FOCUSS with motion estimation/correction scheme, and demonstrate reduced motion artifacts over classical compressed sensing schemes that utilize the compact priors on the original deformation uncorrected signal. [ABSTRACT FROM AUTHOR]

The clinical utility of myocardial perfusion MR imaging (MPI) is often restricted by the inability of current acquisition schemes to simultaneously achieve high spatio-temporal resolution, good volume coverage, and high signal to noise ratio. Moreover, many subjects often find it difficult to hold their breath for sufficiently long durations making it difficult to obtain reliable MPI data. Accelerated acquisition of free breathing MPI data can overcome some of these challenges. Recently, an algorithm termed as k − t SLR has been proposed to accelerate dynamic MRI by exploiting sparsity and low rank properties of dynamic MRI data. The main focus of this paper is to further improve k − t SLR and demonstrate its utility in considerably accelerating free breathing MPI. We extend its previous implementation to account for multi-coil radial MPI acquisitions. We perform k − t sampling experiments to compare different radial trajectories and determine the best sampling pattern. We also introduce a novel augmented Lagrangian framework to considerably improve the algorithm’s convergence rate. The proposed algorithm is validated using free breathing rest and stress radial perfusion data sets from two normal subjects and one patient with ischemia. k − t SLR was observed to provide faithful reconstructions at high acceleration levels with minimal artifacts compared to existing MPI acceleration schemes such as spatio-temporal constrained reconstruction and k − t SPARSE/SENSE. [ABSTRACT FROM AUTHOR]

We propose a novel blind compressive sensing (BCS) frame work to recover dynamic magnetic resonance images from undersampled measurements. This scheme models the dynamic signal as a sparse linear combination of temporal basis functions, chosen from a large dictionary. In contrast to classical compressed sensing, the BCS scheme simultaneously estimates the dictionary and the sparse coefficients from the undersampled measurements. Apart from the sparsity of the coefficients, the key difference of the BCS scheme with current low rank methods is the nonorthogonal nature of the dictionary basis functions. Since the number of degrees-of-freedom of the BCS model is smaller than that of the low-rank methods, it provides improved reconstructions at high acceleration rates. We formulate the reconstruction as a constrained optimization problem; the objective function is the linear combination of a data consistency term and sparsity promoting \ell1 prior of the coefficients. The Frobenius norm dictionary constraint is used to avoid scale ambiguity. We introduce a simple and efficient majorize–minimize algorithm, which decouples the original criterion into three simpler subproblems. An alternating minimization strategy is used, where we cycle through the minimization of three simpler problems. This algorithm is seen to be considerably faster than approaches that alternates between sparse coding and dictionary estimation, as well as the extension of K-SVD dictionary learning scheme. The use of the \ell1 penalty and Frobenius norm dictionary constraint enables the attenuation of insignificant basis functions compared to the \ell0 norm and column norm constraint assumed in most dictionary learning algorithms; this is especially important since the number of basis functions that can be reliably estimated is restricted by the available measurements. We also observe that the proposed scheme is more robust to local minima compared to K-SVD method, which relies on greedy sparse coding. Our phase transition experiments demonstrate that the BCS scheme provides much better recovery rates than classical Fourier-based CS schemes, while being only marginally worse than the dictionary aware setting. Since the overhead in additionally estimating the dictionary is low, this method can be very useful in dynamic magnetic resonance imaging applications, where the signal is not sparse in known dictionaries. We demonstrate the utility of the BCS scheme in accelerating contrast enhanced dynamic data. We observe superior reconstruction performance with the BCS scheme in comparison to existing low rank and compressed sensing schemes. [ABSTRACT FROM AUTHOR]

We introduce a novel algorithm to recover sparse and low-rank matrices from noisy and undersampled measurements. We pose the reconstruction as an optimization problem, where we minimize a linear combination of data consistency error, nonconvex spectral penalty, and nonconvex sparsity penalty. We majorize the nondifferentiable spectral and sparsity penalties in the criterion by quadratic expressions to realize an iterative three-step alternating minimization scheme. Since each of these steps can be evaluated either analytically or using fast schemes, we obtain a computationally efficient algorithm. We demonstrate the utility of the algorithm in the context of dynamic magnetic resonance imaging (MRI) reconstruction from sub-Nyquist sampled measurements. The results show a significant improvement in signal-to-noise ratio and image quality compared with classical dynamic imaging algorithms. We expect the proposed scheme to be useful in a range of applications including video restoration and multidimensional MRI. [ABSTRACT FROM PUBLISHER]

We introduce a novel algorithm to reconstruct dynamic magnetic resonance imaging (MRI) data from under-sampled k-t space data. In contrast to classical model based cine MRI schemes that rely on the sparsity or banded structure in Fourier space, we use the compact representation of the data in the Karhunen Louve transform (KLT) domain to exploit the correlations in the dataset. The use of the data-dependent KL transform makes our approach ideally suited to a range of dynamic imaging problems, even when the motion is not periodic. In comparison to current KLT-based methods that rely on a two-step approach to first estimate the basis functions and then use it for reconstruction, we pose the problem as a spectrally regularized matrix recovery problem. By simultaneously determining the temporal basis functions and its spatial weights from the entire measured data, the proposed scheme is capable of providing high quality reconstructions at a range of accelerations. In addition to using the compact representation in the KLT domain, we also exploit the sparsity of the data to further improve the recovery rate. Validations using numerical phantoms and in vivo cardiac perfusion MRI data demonstrate the significant improvement in performance offered by the proposed scheme over existing methods. [ABSTRACT FROM AUTHOR]

An abstract of the conference paper "Accelerated imaging of rest and stress myocardial perfusion MRI using multi-coil k-t SLR: A feasibility study," by Sajan Goud Lingala and colleagues is presented.

An abstract of the conference paper "Motion compensated reconstruction for myocardial perfusion MRI," by Sajan Goud Lingala and colleagues is presented.