A Transparent and Lightweight Tumor-Aware MRI Super-Resolution Framework to Enhance Prostate Cancer Detection

Magnetic Resonance Imaging (MRI) is one of the primary steps for screening for prostate cancer (PCa), one of the most frequent types of cancer in men. Although it provides significant clinical information, MRI is acquired at low-resolution images, thus making its readout more challenging for radiologists or AI-based tools for PCa detection. Generative Artificial Intelligence (GenAI) and Super Resolution (SR) have achieved impressive results in improving image quality. This work proposes SR-MR-Hop, an SR method to enhance the MRI input, meant to improve the PCa detection performance of AI-based detectors. SR-MR-Hop method provides a novel, lightweight and transparent feature extraction methodology for super-resolving the input MRI sequences. Moreover, a novel tumor-aware loss function is introduced to boost learning from the tumor regions. Also, a fusion model is proposed to enhance the MRI sequence, based on the cross-modal features from the different sequences of MRI, leveraging their complementary properties. The experiments on the publicly available PI-CAI dataset, show that SR-MR-Hop can provide superior MRI enhancement results. Notably, by combining the SR-MR-Hop as a preprocessing method with AI-based PCa detection models, the Area Under The Curve (AUC) increases from 0.811 to 0.834.