An imageless magnetic resonance framework for fast and cost-effective decision-making

TL;DR

This paper introduces IMRD, a novel MRI framework that bypasses traditional imaging by analyzing raw signals, enabling rapid, low-cost diagnosis suitable for resource-limited settings.

Contribution

IMRD is a new framework that eliminates the need for image reconstruction in MRI, using raw signals and pattern recognition for fast, cost-effective diagnosis.

Findings

Achieved 0.95 AUC in lesion detection

Demonstrated 3-second protocols with and without spatial info

Proved robustness under noise and signal variability

Abstract

Magnetic Resonance Imaging (MRI) is the gold standard in countless diagnostic procedures, yet hardware complexity, long scans, and cost preclude rapid screening and point-of-care use. We introduce Imageless Magnetic Resonance Diagnosis (IMRD), a framework that bypasses k-space sampling and image reconstruction by analyzing raw one-dimensional MR signals. We identify potentially impactful embodiments where IMRD requires only optimized pulse sequences for time-domain contrast, minimal low-field hardware, and pattern recognition algorithms to answer clinical closed queries and quantify lesion burden. As a proof of concept, we simulate multiple sclerosis lesions in silico within brain phantoms and deploy two extremely fast protocols (approximately 3 s), with and without spatial information. A 1D convolutional neural network achieves AUC close to 0.95 for lesion detection and R2 close to 0.99 for volume estimation. We also perform robustness tests under reduced signal-to-noise ratio, partial signal omission, and relaxation-time variability. By reframing MR signals as direct diagnostic metrics, IMRD paves the way for fast, low-cost MR screening and monitoring in resource-limited environments.