Conference proceeding
Predicting human detection performance in magnetic resonance imaging (MRI) with total variation and wavelet sparsity regularizers
Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment, Vol.12035, pp.1203511-1203511-6
04/04/2022
DOI: 10.1117/12.2608986
PMCID: PMC9581458
PMID: 36267385
Abstract
Two common regularization methods in reconstruction of magnetic resonance images are total variation (TV) which restricts the magnitude of the gradient in the reconstructed image and wavelet sparsity which assumes that the object being imaged is sparse in the wavelet domain. These regularization methods have resulted in images with fewer undersampling artifacts and less noise but introduce their own artifacts. In this work, we extend previous results on modeling of human observer performance for images using TV regularization to also predict human detection performance using wavelet regularization and a combination of wavelet and TV regularization. Small lesions were placed in the coil k-space data for fluid-attenuated inversion recovery (FLAIR) brain images from the fastMRI database. The data was undersampled using an acceleration factor of 3.48. The undersampled data was reconstructed using a range of regularization parameters for both the TV and wavelet regularization. The internal noise level for the sparse difference-of-Gaussians (S-DOG) model observer was chosen to match the average human percent correct in two-alternative forced choice (2-AFC) studies with a signal known exactly with variable backgrounds and no regularization. The S-DOG model largely tracked the human observer results except at large values of the regularization parameter where it outperformed the average human observer. We found that the regularization with either constraint or in combination did not improve human observer performance for this task.
Details
- Title: Subtitle
- Predicting human detection performance in magnetic resonance imaging (MRI) with total variation and wavelet sparsity regularizers
- Creators
- Alexandra G. O'Neill - Manhattan UniversitySajan Goud Lingala - University of IowaAngel R. Pineda - Manhattan University
- Contributors
- Claudia R. Mello-Thoms (Editor) - Univ. Iowa Carver College of Medicine (United States)Sian Taylor-Phillips (Editor) - University of Warwick
- Resource Type
- Conference proceeding
- Publication Details
- Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment, Vol.12035, pp.1203511-1203511-6
- DOI
- 10.1117/12.2608986
- PMID
- 36267385
- PMCID
- PMC9581458
- ISSN
- 1605-7422
- Publisher
- SPIE
- Language
- English
- Date published
- 04/04/2022
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology
- Record Identifier
- 9984259383902771
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