Reducing the Complexity of Model-Based MRI Reconstructions via Sparsification.

Model-based reconstruction methods have emerged as a powerful alternative to classical Fourier-based MRI techniques, largely because of their ability to explicitly model (and therefore, potentially overcome) moderate field inhomogeneities, streamline reconstruction from non-Cartesian sampling, and even allow for the use of custom designed non-Fourier encoding methods. Their application in such scenarios, however, often comes with a substantial increase in computational cost, owing to the fact that the corresponding forward model in such settings no longer possesses a direct Fourier Transform based implementation. This paper introduces an algorithmic framework designed to reduce the computational burden associated with model-based MRI reconstruction tasks. The key innovation is the strategic sparsification of the corresponding forward operators for these models, giving rise to approximations of the forward models (and their adjoints) that admit low computational complexity application. This enables overall a reduced computational complexity application of popular iterative first-order reconstruction methods for these reconstruction tasks. Computational results obtained on both synthetic and experimental data illustrate the viability and efficiency of the approach.

Accelerated imaging with segmented 2D pulses using parallel imaging and virtual coils.

Large magnetic field inhomogeneity can be a significant cause of spatial flip-angle variation when using ordinary, limited-bandwidth RF pulses. Multidimensional RF pulses are particularly sensitive to inhomogeneity due to their extended pulse length, which decreases their bandwidth. Previously, it was shown that, by breaking a 2D pulse into multiple undersampled k-space segments, the excitation bandwidth can be increased at the expense of increased imaging time. The present study shows how this increased imaging time can be offset by undersampling acquisition k-space in a phase-encoded dimension that is in the direction of excitation segmentation. Data from each segment are viewed as originating from "virtual receive coils" rather than multiple physical coils. The undersampled data are reconstructed using parallel imaging techniques (e.g. as in GRAPPA). The method was tested in vivo with brain imaging at both 3 T and 4 T, and used in conjunction with a 32-channel head coil and conventional GRAPPA on the 3 T data. Relationships with existing techniques and future applications are discussed.

A Compressed Sensing Based Decomposition of Electrodermal Activity Signals.

The measurement and analysis of electrodermal activity (EDA) offers applications in diverse areas ranging from market research to seizure detection and to human stress analysis. Unfortunately, the analysis of EDA signals is made difficult by the superposition of numerous components that can obscure the signal information related to a user's response to a stimulus. We show how simple preprocessing followed by a novel compressed sensing based decomposition can mitigate the effects of the undesired noise components and help reveal the underlying physiological signal. The proposed framework allows for decomposition of EDA signals with provable bounds on the recovery of user responses. We test our procedure on both synthetic and real-world EDA signals from wearable sensors and demonstrate that our approach allows for more accurate recovery of user responses as compared with the existing techniques.

Anomaly-sensitive dictionary learning for structural diagnostics from ultrasonic wavefields.

This paper proposes a strategy for the detection and triangulation of localized anomalies, such as defects, inclusions, or damage zones, in solid and structural media. The method revolves around the construction of sparse representations of the structure's ultrasonic wavefield response, which are obtained by learning instructive dictionaries that form a suitable basis for the response data. The resulting sparse coding problem is cast as a modified dictionary learning task with additional spatial sparsity constraints enforced on the atoms of the learned dictionaries, which provide them with the ability to unveil anomalous regions in the physical domain. The proposed methodology is model-agnostic, i.e., it forsakes the need for a physical model and requires virtually no a priori knowledge of the material properties. This characteristic makes the approach especially powerful for anomaly identification in systems with unknown or highly heterogeneous property distribution, for which a material model is unsuitable or unreliable. The method is tested against synthetically generated data as well as experimental data acquired using a scanning laser Doppler vibrometer.

Automated defect localization via low rank plus outlier modeling of propagating wavefield data.

This work proposes an agnostic inference strategy for material diagnostics, conceived within the context of laser-based nondestructive evaluation methods which extract information about structural anomalies from the analysis of acoustic wavefields measured on the structure's surface by means of a scanning laser interferometer. The proposed approach couples spatiotemporal windowing with low rank plus outlier modeling, to identify a priori unknown deviations in the propagating wavefields caused by material inhomogeneities or defects, using virtually no knowledge of the structural and material properties of the medium. This characteristic makes the approach particularly suitable for diagnostics scenarios in which the mechanical and material models are complex, unknown, or unreliable. We demonstrate our approach in a simulated environment using benchmark point and line defect localization problems based on propagating flexural waves in a thin plate.