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Abstract #1905

Improved Region-Optimized Virtual Coils for Cartesian Acquisition Geometries

Chin-Cheng Chan1, Christopher Nguyen2, and Justin P. Haldar1
1Signal and Image Processing Institute, Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, United States, 2Cardiovascular Innovation Research Center, Heart, Vascular, and Thoracic Institute, Cleveland Clinic, Cleveland, OH, United States

Synopsis

Keywords: Image Reconstruction, Image Reconstruction, accelerated acquisition, beamforming, reduced field-­of-­view imaging, signal suppression, region-­optimized virtual coils, coil compression

Motivation: ROVir (Region-Optimized Virtual coils) is a technique that constructs MRI virtual coils in a way that seeks to simultaneously maximize the amount of information captured by the smallest number of virtual coils (coil compression/dimensionality reduction) while also suppressing signal from undesired spatial regions (avoiding aliasing/leakage artifacts). Although ROVir generally performs well, its performance can sometimes be limited by coil geometry.

Goal(s): To improve the performance of ROVir.

Approach: We exploit the structure of Cartesian imaging, calculating distinct ROVir weights for each position along the fully-sampled readout.

Results: The proposed approach enables substantially better dimensionality reduction and signal suppression performance.

Impact: The proposed approach provides substantially better signal suppression and coil compression for Cartesian acquisitions, alleviating burdens on data acquisition (reducing the need for sequence-based signal suppression and enabling reduced-FOV imaging) and reducing the computational complexity of image reconstruction.

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Keywords