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Deconvolution of dynamic contrast‐enhanced MRI data by linear inversion: Choice of the regularization parameter
Author(s) -
Sourbron Steven,
Luypaert Rob,
Van Schuerbeek Peter,
Dujardin Martine,
Stadnik Tadeusz,
Osteaux Michel
Publication year - 2004
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.20113
Subject(s) - deconvolution , regularization (linguistics) , dynamic contrast enhanced mri , dynamic contrast , contrast (vision) , inversion (geology) , computer science , nuclear magnetic resonance , magnetic resonance imaging , algorithm , mathematics , artificial intelligence , radiology , physics , medicine , geology , paleontology , structural basin
Truncated singular value decomposition (TSVD) is an effective method for the deconvolution of dynamic contrast‐enhanced MRI. Two robust methods for the selection of the truncation threshold on a pixel‐by‐pixel basis—generalized cross validation (GCV) and the L‐curve criterion (LCC)—were optimized and compared to paradigms in the literature. The methods lead to improvements in the estimate of the residue function and of its maximum and converge properly with SNR. The oscillations typically observed in the solution vanish entirely and perfusion is more accurately estimated at small mean transit times. This results in improved image contrast and increased sensitivity to perfusion abnormalities, at the cost of 1–2 min in calculation time and isolated instabilities in the image. It is argued that the latter problem may be resolved by optimization. Simulated results for GCV and LCC are equivalent in terms of performance, but GCV is faster. Magn Reson Med 52:209–213, 2004. © 2004 Wiley‐Liss, Inc.