KEYWORDS: Tissues, Body composition, Prostate, Physics, Magnetic resonance imaging, Deep learning, In vivo imaging, Monte Carlo methods, Model-based design, Education and training
Physics model-based approaches using Magnetic Resonance Imaging (MRI) have been developed to provide a non-invasive tool to estimate tissue composition by fitting the notoriously ill-conditioned sum-of-decaying-exponentials functions. However, even modest levels of noise can negatively impact the accuracy of such models. This study introduces a new paradigm that integrates the strengths of physics model-based and deep learning-based methods, while overcoming their respective weaknesses. We reformulated the MRI-based tissue composition estimation using a physics-informed autoencoder (PIA), transforming the problem from a least-squares fit into a robust deep learning framework. The PIA model consists of a trainable multi-head neural network encoder and a fixed, physics-informed decoder. Without a need for further fine-tuning for each new patient, the encoder’s latent activations are utilized to derive prostate tissue composition estimates, such as volume fractions for each compartment along with their diffusivity and T2. PIA’s estimates of tissue parameters are juxtaposed with the least-squares solution through both Monte Carlo simulations and the quantitative histology of in-vivo prostate scans. In-vivo evaluations, performed on 19 pathology-proven prostate cancer patients, have been validated with quantitative histology-based true tissue compositions. Both Monte Carlo and in-vivo evaluations reveal that PIA significantly outperforms the conventional least-squares solution on Pearson correlation and mean absolute error metrics. Notably, PIA was able to predict the diffusivity and T2 of each tissue compartment more accurately, whereas the least-squares method tends to pin these values to the extremities.
Standard treatment of prostate cancer has traditionally been invasive and radical, with the whole gland being treated, even in those instances of localized prostate cancer. The ongoing development of computational tools and improvements to MRI techniques has led to an increase in research exploring alternative treatment solutions. The goal of this study was to investigate the application of open-source computational tools to MR guided Prostate Cancer Focal Laser Ablation (PCaFLA). To this end, we explore the use of image registration tools within 3D Slicer to assess target coverage with the treated ablation zone. Three different registration tools were investigated for registration between a planning MRI (day of procedure MRI examination prior to ablation) and a post-treatment MRI (day of procedure MRI examination immediately after ablation). Prostate whole gland, focal tumor volume ROI, and ablation zone were segmented manually for each case. Overlap between the ablation zone and tumor ROI was quantified using volume overlap measures and directed Hausdorff distance (HD) metrics and experimental results were reported. Experimental results demonstrate the ability to apply open-source computational tools to investigate outcomes of focal laser ablation of the prostate from imaging. This study provides a preliminary evaluation of the utility of open-source computational tools in intra-procedural feedback and post-treatment assessment for focal therapy.
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