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Abstract #5633

Higher-order subspace denoising for improved multi-contrast imaging and parameter mapping

Sagar Mandava1, Mahesh B Keerthivasan1, Diego R Martin2, Maria I Altbach2, and Ali Bilgin1,2

1Electrical and Computer Engineering, University of Arizona, Tucson, AZ, United States, 2Department of Medical Imaging, University of Arizona, Tucson, AZ, United States

Multi-contrast image acquisitions are valuable for diagnostics but the scan time scales with the number of contrast images. Accelerated acquisitions are necessary for practical scan times and require the use of constrained reconstructions. Subspace-constraints, which constrain the multi-contrast data to lie in a low-dimensional subspace, are popularly used to reconstruct these datasets. Despite yielding good quality images at most imaging contrasts, these constraints create poor image quality at certain contrasts. We demonstrate that this is due to poor recovery of higher order subspace coefficients and present a model to enable high quality recovery of these coefficients and consequently the echo-images.

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