Magnetic resonance imaging (MRI) data acquisition is sometimes accelerated by pseudo-random under-sampling of the frequency domain which is followed by constrained reconstruction. This approach to acceleration assumes a certain level of sparsity of the object being imaged. The sparsity is typically considered for the background anatomy but not explored in terms of a signal detection task. In this study we implement a 2.56x one dimensional acceleration in the acquisition using fully sampled low frequencies and randomly sampled high frequencies with a total variation reconstruction. A small and a large lesion were synthetically placed in a 3D MRI volume in non-overlapping regions. From 40 slices of this volume and 16 regions per slice, 640 sub-images with and without signals were generated to estimate the detection performance of lesions with anatomical variation. We compared the effect of this approach on signal detection using a channelized Hotelling observer approximating the ideal linear observer (with 10 Laguerre-Gauss channels) and one approximating a human observer (with sparse difference-of-Gaussians channels). The area under the receiver operating characteristic curve (AUC) was estimated using the Mann-Whitney statistic and the uncertainty of the estimate was assessed using a bootstrap distribution with 10,000 samples. We found that for these two tasks and model observers, total variation did not lead to a statistically significant improvement in detection performance and that the effect of regularization was larger for the Laguerre-Gauss model than for the sparse difference-of-Gaussians model.

Date Published: 4 March 2019
PDF: 6 pages
Proc. SPIE 10952, Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment, 109520A (4 March 2019); doi: 10.1117/12.2512813

Published in SPIE Proceedings Vol. 10952:
Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment
Robert M. Nishikawa; Frank W. Samuelson, Editor(s)