Diffusion-Weighted MRI Based System for the Early Detection of Prostate Cancer

Abstract

Prostate cancer is the second most diagnosed cancer in men. In this paper, we propose a diffusionweighted MRI based computer-aided detection system for the early detection of prostate cancer. The proposed system calculates seven apparent diffusion coefficients (ADC) for each subject based on the b values at which the scans are acquired. The 3D maps are then represented in a lower dimensional space using a data-driven approach. The reduced maps are fed into seven independent artificial neural networks, each corresponding to the b value at which the ADC maps are calculated. The final decision of malignancy is obtained by aggregating the outputs of all learners in a score-fusion scheme. Essentially, this pipeline is expected to reveal discriminative 3D patterns relevant to subject malignancy. Preliminary results show that the proposed system yields an accuracy of 100% in a leave-onepatient-out cross validation scheme, competing well with state of the art methods. 