Preprint
Modeling human observer detection in undersampled magnetic resonance imaging (MRI) reconstruction with total variation and wavelet sparsity regularization
ArXiv.org
10/21/2022
Abstract
Purpose: Task-based assessment of image quality in undersampled magnetic
resonance imaging (MRI) provides a way of evaluating the impact of
regularization on task performance. In this work, we evaluated the effect of
total variation (TV) and wavelet regularization on human detection of signals
with a varying background and validated a model observer in predicting human
performance.
Approach: For the human observer studies we used two-alternative forced
choice (2-AFC) studies with a small signal known exactly (SKE) task but with
varying backgrounds for fluid-attenuated inversion recovery (FLAIR) images
reconstructed from undersampled multi-coil data. We used a 3.48 undersampling
factor with TV and a wavelet sparsity constraints. The sparse
difference-of-Gaussians (S-DOG) observer with internal noise was used to model
human observer detection. That S-DOG model was used to predict the percent
correct of human observers for a range of regularization parameters.
Results: We found that the human observer detection performance remained
fairly constant for a broad range of values in the regularization parameter
before decreasing at large values. Using the TV, wavelet sparsity, or a
combination of the constraints did not meaningfully improve task performance.
The model observer tracked the performance of the human observers as the
regularization was increased but over-estimated the PC for large amounts of
regularization for TV and wavelet sparsity, as well as the combination of both
parameters.
Conclusions: For the task we studied, the S-DOG observer was able to
reasonably predict human performance with both total variation and wavelet
sparsity regularizers over a broad range of regularization parameters. We also
did not find a large improvement in task performance due to regularization.
Details
- Title: Subtitle
- Modeling human observer detection in undersampled magnetic resonance imaging (MRI) reconstruction with total variation and wavelet sparsity regularization
- Creators
- Alexandra G O'Neill - The Bronx DefendersEmely L Valdez - The Bronx DefendersSajan Goud Lingala - University of IowaAngel R Pineda - The Bronx Defenders
- Resource Type
- Preprint
- Publication Details
- ArXiv.org
- ISSN
- 2331-8422
- Language
- English
- Date posted
- 10/21/2022
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology
- Record Identifier
- 9984306742702771
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