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Improved compressed sensing and super‐resolution of cardiac diffusion MRI with structure‐guided total variation
Author(s) -
Teh Irvin,
McClymont Darryl,
Carruth Eric,
Omens Jeffrey,
McCulloch Andrew,
Schneider Jürgen E.
Publication year - 2020
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.28245
Subject(s) - undersampling , compressed sensing , image resolution , computer science , context (archaeology) , diffusion mri , reconstruction algorithm , ground truth , iterative reconstruction , diffusion , skew , algorithm , artificial intelligence , physics , geology , telecommunications , magnetic resonance imaging , medicine , radiology , paleontology , thermodynamics
Purpose Structure‐guided total variation is a recently introduced prior that allows reconstruction of images using knowledge of the location and orientation of edges in a reference image. In this work, we demonstrate the advantages of a variant of structure‐guided total variation known as directional total variation (DTV), over traditional total variation (TV), in the context of compressed‐sensing reconstruction and super‐resolution. Methods We compared TV and DTV in retrospectively undersampled ex vivo diffusion tensor imaging and diffusion spectrum imaging data from healthy, sham, and hypertrophic rat hearts. Results In compressed sensing at an undersampling factor of 8, the RMS error of mean diffusivity and fractional anisotropy relative to the fully sampled ground truth were 44% and 20% lower in DTV compared with TV. In super‐resolution, these values were 29% and 14%, respectively. Similarly, we observed improvements in helix angle, transverse angle, sheetlet elevation, and sheetlet azimuth. The RMS error of the diffusion kurtosis in the undersampled data relative to the ground truth was uniformly lower (22% on average) with DTV compared to TV. Conclusion Acquiring one fully sampled non‐diffusion‐weighted image and 10 diffusion‐weighted images at 8× undersampling would result in an 80% net reduction in data needed. We demonstrate efficacy of the DTV algorithm over TV in reducing data sampling requirements, which can be translated into higher apparent resolution and potentially shorter scan times. This method would be equally applicable in diffusion MRI applications outside the heart.