Purpose The goal of this work is to propose a motion robust reconstruction method for diffusion‐weighted MRI that resolves shot‐to‐shot phase mismatches without using phase estimation. Methods Assuming that shot‐to‐shot phase variations are slowly varying, spatial‐shot matrices can be formed using a local group of pixels to form columns, in which each column is from a different shot (excitation). A convex model with a locally low‐rank constraint on the spatial‐shot matrices is proposed. In vivo brain and breast experiments were performed to evaluate the performance of the proposed method. Results The proposed method shows significant benefits when the motion is severe, such as for breast imaging. Furthermore, the resulting images can be used for reliable phase estimation in the context of phase‐estimation‐based methods to achieve even higher image quality. Conclusion We introduced the shot–locally low‐rank method, a reconstruction technique for multishot diffusion‐weighted MRI without explicit phase estimation. In addition, its motion robustness can be beneficial to neuroimaging and body imaging.

Purpose To enable robust high spatio-temporal-resolution 3D Cartesian MRI using a scheme incorporating a novel variable density random k-space sampling trajectory allowing flexible and retrospective selection of the temporal footprint with compressed sensing (CS). Methods A “complementary Poisson-disc” k-space sampling trajectory was designed to allow view sharing and varying combinations of reduced view sharing with CS from the same prospective acquisition. These schemes were used for two-point-Dixon-based dynamic contrast-enhanced MRI (DCE-MRI) of the breast and abdomen. Results were validated in vivo with a novel approach using variable-flip-angle data, which was retrospectively accelerated using the same methods but offered a ground truth. Results In breast DCE-MRI, the temporal footprint could be reduced 2.3-fold retrospectively without introducing noticeable artifacts, improving depiction of rapidly enhancing lesions. Further, experiments with variable-flip-angle data showed that reducing view sharing improved accuracy in reconstruction and T1 mapping. In abdominal MRI, 2.3-fold and 3.6-fold reductions in temporal footprint allowed reduced motion artifacts. Conclusion The complementary-Poisson-disc k-space sampling trajectory allowed a retrospective spatiotemporal resolution tradeoff using CS and view sharing, imparting robustness to motion and contrast enhancement. The technique was also validated using a novel approach of fully acquired variable-flip-angle acquisition.

We designed and trained a modified time-delay neural network (TDNN) to perform both automatic lipreading ("speech reading") in conjunction with acoustic speech recognition in order to improve recognition both in silent environments as well as in the presence of acoustic noise. The speech reader subsystem has a speaker-independent recognition accuracy of 51% (in the absence of acoustic information); the combined acoustic-visual system has a recognition accuracy of 9 1 %, all on a ten-utterance speakerindependent task. Most importantly, with no free parameters, our system is far more robust to acoustic noise and verbal distractors than is a system not incorporaQng visual information. Specifically, in the presence of high amplitude pink noise the low recognition rate in our acoustic only system (43%) is raised dramatically to 75% by the incorporation of visual information, Additionally, our system responds to (artificial) conflicting cross-modal patterns in a way closely analogous to the McGurk effect in humans.We thus demonstrate the power of neural techniques in several crucial and difficult domains: 1) pattern recognition, 2) sensory integration, and 3) distributed approaches toward "rule-based" (linguistic-phonological) processing. Our results suggest that speech reading systems may find use in a vast array of real-world situations, for instance high noise environments such as factory and shop floors, cockpits, large office environments, outdoor public spaces, and so on. AbstractWe designed and trained a modified time-delay neural network (TDNN) to perform both automatic lipreading ("speech reading") and acoustic speech recognition in order to improve recognition both in silent environments as well as in the presence of acoustic noise. The speech reader subsystem has a speaker-independent recognition accuracy of 51% (in the absence of acoustic information); the combined acoustic-visual system has a recogntion accuracy of 9 1 %, all on a ten-utterance speaker-independent task. Most importantly, with no free parameters, our system is far more robust to acoustic noise and verbal distractors than is a system not incorporating visual information. Specifically, in the presence of high amplitude pink noise the low recognition rate in an acoustic only system (43%) is raised dramatically to 75% by the incorporation of visual information. Our system responds to (artificial) conflicting cross-modal patterns in a way closely analogous to the McGurk effect in humans.We thus demonstrate the power of neural techniques in several crucial and difficult domains: 1) pattern recognition, 2) sensory integration, and 3) distributed approaches toward "rule-based" (linguistic-phonological) processing. Our results suggest that speech reading systems may find use in a vast array of real-world situations, for instance high noise environments such as factory and shop floors, cockpits, large office environments, outdoor public spaces, and so on. 11-292T 11-294 T

A bilateral coil-array setup can image both knees simultaneously in similar scan times as conventional unilateral knee scans, with comparable image quality and quantitative accuracy. This has the potential to improve the value of MRI knee evaluations. Magn Reson Med 80:529-537, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Abstract-In high-dimensional magnetic resonance imaging applications, time-consuming, sequential acquisition of data samples in the spatial frequency domain (k-space) can often be accelerated by accounting for dependencies along imaging dimensions other than space in linear reconstruction, at the cost of noise amplification that depends on the sampling pattern. Examples are support-constrained, parallel, and dynamic MRI, and k-space sampling strategies are primarily driven by imagedomain metrics that are expensive to compute for arbitrary sampling patterns. It remains challenging to provide systematic and computationally efficient automatic designs of arbitrary multidimensional Cartesian sampling patterns that mitigate noise amplification, given the subspace to which the object is confined. To address this problem, this work introduces a theoretical framework that describes local geometric properties of the sampling pattern and relates these properties to a measure of the spread in the eigenvalues of the information matrix described by its first two spectral moments. This new criterion is then used for very efficient optimization of complex multidimensional sampling patterns that does not require reconstructing images or explicitly mapping noise amplification. Experiments with in vivo data show strong agreement between this criterion and traditional, comprehensive image-domain-and k-space-based metrics, indicating the potential of the approach for computationally efficient (on-thefly), automatic, and adaptive design of sampling patterns.