Deconvolution of dynamic contrast‐enhanced MRI data by linear inversion: Choice of the regularization parameter

Abstract

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.