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A comparison of RIGR and SVD dynamic imaging methods
Author(s) -
Hanson Jill M.,
Liang ZhiPei,
Magin Richard L.,
Duerk Jeff L.,
Lauterbur Paul C.
Publication year - 1997
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.1910380122
Subject(s) - singular value decomposition , computer science , encoding (memory) , a priori and a posteriori , artificial intelligence , iterative reconstruction , dynamic imaging , pattern recognition (psychology) , image (mathematics) , dynamic contrast enhanced mri , singular value , computer vision , algorithm , image processing , magnetic resonance imaging , epistemology , quantum mechanics , digital image processing , medicine , radiology , philosophy , eigenvalues and eigenvectors , physics
Several constrained imaging methods have recently been proposed for dynamic imaging applications. This paper compares two of these methods: the Reduced‐encoding Imaging by Generalized‐series Reconstruction (RIGR) and Singular Value Decomposition (SVD) methods. RIGR utilizes a priori data for optimal image reconstruction whereas the SVD method seeks to optimize data acquisition. However, this study shows that the existing SVD encoding method tends to bias the data acquisition scheme toward reproducing the known features in the reference image. This characteristic of the SVD encoding method reduces its capability to capture new image features and makes it less suitable than RIGR for dynamic imaging applications.