Ganesh Adluru1, Yaniv Gur2, Liyong Chen3, David Feinberg3, Jeffrey Anderson1, Edward V R DiBella4. 1. UCAIR, Department of Radiology, University of Utah, Salt Lake City, Utah 84108. 2. IBM Almaden Research Center, San Jose, California 95120. 3. Advanced MRI Technologies, Sebastpool, California, 95472. 4. UCAIR, Department of Radiology, University of Utah, Salt Lake City, Utah 84108 and Department of Bioengineering, University of Utah, Salt Lake City, Utah 84112.
Abstract
PURPOSE: To improve rank constrained reconstructions for undersampled multi-image MRI acquisitions. METHODS: Motivated by the recent developments in low-rank matrix completion theory and its applicability to rapid dynamic MRI, a new reordering-based rank constrained reconstruction of undersampled multi-image data that uses prior image information is proposed. Instead of directly minimizing the nuclear norm of a matrix of estimated images, the nuclear norm of reordered matrix values is minimized. The reordering is based on the prior image estimates. The method is tested on brain diffusion imaging data and dynamic contrast enhanced myocardial perfusion data. RESULTS: Good quality images from data undersampled by a factor of three for diffusion imaging and by a factor of 3.5 for dynamic cardiac perfusion imaging with respiratory motion were obtained. Reordering gave visually improved image quality over standard nuclear norm minimization reconstructions. Root mean squared errors with respect to ground truth images were improved by ∼18% and ∼16% with reordering for diffusion and perfusion applications, respectively. CONCLUSIONS: The reordered low-rank constraint is a way to inject prior image information that offers improvements over a standard low-rank constraint for undersampled multi-image MRI reconstructions.
PURPOSE: To improve rank constrained reconstructions for undersampled multi-image MRI acquisitions. METHODS: Motivated by the recent developments in low-rank matrix completion theory and its applicability to rapid dynamic MRI, a new reordering-based rank constrained reconstruction of undersampled multi-image data that uses prior image information is proposed. Instead of directly minimizing the nuclear norm of a matrix of estimated images, the nuclear norm of reordered matrix values is minimized. The reordering is based on the prior image estimates. The method is tested on brain diffusion imaging data and dynamic contrast enhanced myocardial perfusion data. RESULTS: Good quality images from data undersampled by a factor of three for diffusion imaging and by a factor of 3.5 for dynamic cardiac perfusion imaging with respiratory motion were obtained. Reordering gave visually improved image quality over standard nuclear norm minimization reconstructions. Root mean squared errors with respect to ground truth images were improved by ∼18% and ∼16% with reordering for diffusion and perfusion applications, respectively. CONCLUSIONS: The reordered low-rank constraint is a way to inject prior image information that offers improvements over a standard low-rank constraint for undersampled multi-image MRI reconstructions.
Authors: Felix A Breuer; Martin Blaimer; Robin M Heidemann; Matthias F Mueller; Mark A Griswold; Peter M Jakob Journal: Magn Reson Med Date: 2005-03 Impact factor: 4.668
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Authors: P S Tofts; G Brix; D L Buckley; J L Evelhoch; E Henderson; M V Knopp; H B Larsson; T Y Lee; N A Mayr; G J Parker; R E Port; J Taylor; R M Weisskoff Journal: J Magn Reson Imaging Date: 1999-09 Impact factor: 4.813