Task-driven CT image quality optimization for low-contrast lesion detectability with tunable neural networks

Matthew Tivnan; Tzu-Cheng Lee; Ruoqiao Zhang; Kirsten Boedeker; Liang Cai; Jeremias Sulam; J. Webster Stayman

doi:10.1117/12.2653936

7 April 2023 Task-driven CT image quality optimization for low-contrast lesion detectability with tunable neural networks

Matthew Tivnan, Tzu-Cheng Lee, Ruoqiao Zhang, Kirsten Boedeker, Liang Cai, Jeremias Sulam, J. Webster Stayman

Author Affiliations +

Proceedings Volume 12463, Medical Imaging 2023: Physics of Medical Imaging; 124631M (2023) https://doi.org/10.1117/12.2653936
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

Low-contrast lesions are difficult to detect in noisy low-dose CT images. Improving CT image quality for this detection task has the potential to improve diagnostic accuracy and patient outcomes. In this work, we use tunable neural networks for CT image restoration with a hyperparameter to control the variance/bias tradeoff. We use clinical images from a super-high-resolution normal-dose CT scan to synthesize low-contrast low-dose CT images for supervised training of deep learning CT reconstruction models. Those models are trained using with multiple noise realizations so that variance and bias can be penalized separately. We use a training loss function with one hyperparameter called the denoising level, which controls the variance/bias tradeoff. Finally, we evaluate the CT image quality to find the optimal denoising level for low-contrast lesion detectability. We evaluate performance using a shallow neural network model classification model to represent a suboptimal image observer. Our results indicate that the optimal networks for low-contrast lesion detectability are those that prioritize bias reduction rather than mean-squared error, which demonstrates the potential clinical benefit of our proposed tunable neural networks.

Conference Presentation

Citation Download Citation

Matthew Tivnan, Tzu-Cheng Lee, Ruoqiao Zhang, Kirsten Boedeker, Liang Cai, Jeremias Sulam, and J. Webster Stayman "Task-driven CT image quality optimization for low-contrast lesion detectability with tunable neural networks", Proc. SPIE 12463, Medical Imaging 2023: Physics of Medical Imaging, 124631M (7 April 2023); https://doi.org/10.1117/12.2653936

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

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.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available