Laguerre-Gauss and sparse difference-of-Gaussians observer models for signal detection using constrained reconstruction in magnetic resonance imaging

Angel R. Pineda

doi:10.1117/12.2512813

4 March 2019 Laguerre-Gauss and sparse difference-of-Gaussians observer models for signal detection using constrained reconstruction in magnetic resonance imaging

Angel R. Pineda

Author Affiliations +

Proceedings Volume 10952, Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment; 109520A (2019) https://doi.org/10.1117/12.2512813
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

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.

Conference Presentation

Citation Download Citation

Angel R. Pineda "Laguerre-Gauss and sparse difference-of-Gaussians observer models for signal detection using constrained reconstruction in magnetic resonance imaging", Proc. SPIE 10952, Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment, 109520A (4 March 2019); https://doi.org/10.1117/12.2512813

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