Alba González-Cebrián

arXiv2026

Qualitative and quantitative hard-tissue MRI with portable Halbach scanners

Purpose: To demonstrate the feasibility of performing in-vivo imaging and quantitative relaxation mapping of soft and hard tissues using a low-cost, portable MRI scanner, and to establish the methodological foundations for zero echo time (ZTE) imaging in systems affected by strong field inhomogeneities. Methods: A complete framework for artifact-free ZTE imaging at low field was developed, including: (i) RF pulse pre/counteremphasis calibration to minimize ring-down and electronics switching time; (ii) an extension of a recent single-point double-shot (SPDS) protocol for simultaneous B0 and B1 mapping; and (iii) a model-based reconstruction incorporating these field maps into the encoding matrix. ZTE imaging and variable flip angle (VFA) T1 mapping were performed on phantoms and in-vivo human knees and ankles, and benchmarked against standard RARE and STIR acquisitions. Results: The optimized PETRA sequence produced 3D images of knees and ankles within clinically compatible times (< 15 min), revealing hard tissues such as ligaments, tendons, cartilage, and bone that are invisible in spin-echo sequences. The extended SPDS method enabled accurate field mapping, while the VFA approach provided the first in-vivo T1 measurements of hard tissues at B0 < 0.1 T. Conclusions: The proposed framework broadens the range of pulse sequences feasible in portable low-field MRI and demonstrates the potential of ZTE for quantitative and structural imaging of musculoskeletal tissues in affordable Halbach-based systems.

physics.med-ph↗

arXiv2025

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

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.

physics.med-ph↗