Introduction: The Need for Speed in Medical Imaging Analysis

In the high-stakes world of clinical research, time is not just money—it’s lives. Yet, the tools we rely on to extract critical insights from medical imaging data are often shackled by inefficiency. Take PyRadiomics , the de facto standard for radiomic feature extraction from CT and MRI scans. On paper, its 3-second processing time per scan seems negligible. But scale that up to thousands of scans in a large-study cohort, and you’re staring down hours—sometimes days—of compute time before analysis even begins. This isn’t just an inconvenience; it’s a bottleneck that delays research, inflates costs, and stifles the very advancements it aims to enable.

The root of PyRadiomics’ inefficiency lies in its CPU-only architecture . While CPUs excel at general-purpose tasks, they’re ill-equipped for the parallelized, matrix-heavy operations inherent in radiomic feature extraction.…