PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
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.
Alexandra G. O'Neill,Sajan Goud Lingala, andAngel R. Pineda
"Predicting human detection performance in magnetic resonance imaging (MRI) with total variation and wavelet sparsity regularizers", Proc. SPIE 12035, Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment, 1203511 (4 April 2022); https://doi.org/10.1117/12.2608986
ACCESS THE FULL ARTICLE
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
The alert did not successfully save. Please try again later.
Alexandra G. O'Neill, Sajan Goud Lingala, Angel R. Pineda, "Predicting human detection performance in magnetic resonance imaging (MRI) with total variation and wavelet sparsity regularizers," Proc. SPIE 12035, Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment, 1203511 (4 April 2022); https://doi.org/10.1117/12.2608986