Valindria, V;
Chiou, E;
Palombo, M;
Singh, S;
Punwani, S;
Panagiotaki, E;
(2021)
Synthetic Q-Space Learning with Deep Regression Networks for Prostate Cancer Characterisation with VERDICT.
In:
2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI).
IEEE
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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).
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