Synthetic Q-Space learning with deep regression networks for prostate cancer characterisation with VERDICT

Valindria, V., Palombo, M. ORCID: https://orcid.org/0000-0003-4892-7967, Chiou, E., Singh, S., Punwani, S. and Panagiotaki, E. 2021. Synthetic Q-Space learning with deep regression networks for prostate cancer characterisation with VERDICT. Presented at: 2021 IEEE 18th International Symposium on Biomedical Imaging, 13-16 April 2021. 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI). 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI). IEEE, 10.1109/ISBI48211.2021.9434096

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Abstract

Traditional quantitative MRI (qMRI) signal model fitting to diffusion-weighted MRI (DW-MRI) is slow and requires long computational time per patient. Recently, q-space learning utilises machine learning methods to overcome these issues and to infer diffusion metrics. However, most of q-space learning studies use simple multi layer perceptron (MLP) for model fitting, which might be sub-optimal when estimating more complex diffusion models with many free parameters. Previous works only investigate the application of q-space learning on diffusion models in the brain. In this work, we explore q-space learning for prostate cancer characterization. Our results show that while simple MLP is adequate to estimate parametric maps on simple models like classic VERDICT, deep residual regression networks are needed for more complex models such as VERDICT with compensated relaxation (R-VERDICT).

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Psychology
Publisher:	IEEE
ISBN:	978-1-6654-2947-4
ISSN:	1945-7928
Last Modified:	10 Nov 2022 10:43
URI:	https://orca.cardiff.ac.uk/id/eprint/147891

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