Modeling human observer detection in undersampled magnetic resonance imaging reconstruction with total variation and wavelet sparsity regularization

Alexandra G. O’Neill; Emely L. Valdez; Sajan G. Lingala; Angel R. Pineda

doi:10.1117/1.JMI.10.1.015502

25 February 2023 Modeling human observer detection in undersampled magnetic resonance imaging reconstruction with total variation and wavelet sparsity regularization

Alexandra G. O’Neill, Emely L. Valdez, Sajan G. Lingala, Angel R. Pineda

Author Affiliations +

Journal of Medical Imaging, Vol. 10, Issue 1, 015502 (February 2023). https://doi.org/10.1117/1.JMI.10.1.015502

Abstract

Purpose

Task-based assessment of image quality in undersampled magnetic resonance imaging 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

Human observer studies used two-alternative forced choice (2-AFC) trials with a small signal known exactly task but with varying backgrounds for fluid-attenuated inversion recovery 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. The internal noise for the S-DOG was chosen to match the average percent correct (PC) in 2-AFC studies for four observers using no regularization. That S-DOG model was used to predict the PC of human observers for a range of regularization parameters.

Results

We observed a trend that the human observer detection performance remained fairly constant for a broad range of values in the regularization parameter before decreasing at large values. A similar result was found for the normalized ensemble root mean squared error. Without changing the internal noise, the model observer tracked the performance of the human observers as the regularization was increased but overestimated 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 TV and wavelet sparsity regularizers over a broad range of regularization parameters. We observed a trend that task performance remained fairly constant for a range of regularization parameters before decreasing for large amounts of regularization.

Citation Download Citation

Alexandra G. O’Neill, Emely L. Valdez, Sajan G. Lingala, and Angel R. Pineda "Modeling human observer detection in undersampled magnetic resonance imaging reconstruction with total variation and wavelet sparsity regularization," Journal of Medical Imaging 10(1), 015502 (25 February 2023). https://doi.org/10.1117/1.JMI.10.1.015502

Received: 14 July 2022; Accepted: 6 February 2023; Published: 25 February 2023

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KEYWORDS

Wavelets

Image restoration

Magnetic resonance imaging

Image quality

Signal detection

Modeling

Performance modeling

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