ssVERDICT: Self‐supervised VERDICT‐MRI for enhanced prostate tumor characterization

Sen, Snigdha, Singh, Saurabh, Pye, Hayley, Moore, Caroline M., Whitaker, Hayley C., Punwani, Shonit, Atkinson, David, Panagiotaki, Eleftheria and Slator, Paddy J. ORCID: https://orcid.org/0000-0001-6967-989X 2024. ssVERDICT: Self‐supervised VERDICT‐MRI for enhanced prostate tumor characterization. Magnetic Resonance in Medicine 92 (5) , pp. 2181-2192. 10.1002/mrm.30186

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Abstract

Purpose: Demonstrating and assessing self‐supervised machine‐learning fitting of the VERDICT (vascular, extracellular and restricted diffusion for cytometry in tumors) model for prostate cancer. Methods: We derive a self‐supervised neural network for fitting VERDICT (ssVERDICT) that estimates parameter maps without training data. We compare the performance of ssVERDICT to two established baseline methods for fitting diffusion MRI models: conventional nonlinear least squares and supervised deep learning. We do this quantitatively on simulated data by comparing the Pearson's correlation coefficient, mean‐squared error, bias, and variance with respect to the simulated ground truth. We also calculate in vivo parameter maps on a cohort of 20 prostate cancer patients and compare the methods' performance in discriminating benign from cancerous tissue via Wilcoxon's signed‐rank test. Results: In simulations, ssVERDICT outperforms the baseline methods (nonlinear least squares and supervised deep learning) in estimating all the parameters from the VERDICT prostate model in terms of Pearson's correlation coefficient, bias, and mean‐squared error. In vivo, ssVERDICT shows stronger lesion conspicuity across all parameter maps, and improves discrimination between benign and cancerous tissue over the baseline methods. Conclusion: ssVERDICT significantly outperforms state‐of‐the‐art methods for VERDICT model fitting and shows, for the first time, fitting of a detailed multicompartment biophysical diffusion MRI model with machine learning without the requirement of explicit training labels.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Psychology Computer Science & Informatics Cardiff University Brain Research Imaging Centre (CUBRIC)
Additional Information:	License information from Publisher: LICENSE 1: URL: http://creativecommons.org/licenses/by/4.0/
Publisher:	Wiley
ISSN:	0740-3194
Funders:	Prostate Cancer UK, Engineering and Physical Sciences Research Council, University College London Hospitals
Date of First Compliant Deposit:	10 June 2024
Date of Acceptance:	17 May 2024
Last Modified:	02 Sep 2024 13:46
URI:	https://orca.cardiff.ac.uk/id/eprint/169645

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