arXiv subjects

Jesús Conejero

Publications and source records attributed to Jesús Conejero.

3 recordsLinked to original sources

arXiv2026

A wearable, stretchable radio-frequency coil for extremity imaging in low-field MRI systems

Purpose: To develop a wearable, stretchable radio-frequency (RF) coil that conforms to the extremities and improves the filling factor in low-field MRI. Methods: A stretchable solenoid RF coil was fabricated by stitching a sinusoidally arranged Litz-wire conductor onto an elastic textile. The coil was compared with four rigid coils, including two routinely used designs and two prototypes designed to isolate the effects of conductor material and stretchability. Performance was characterized through quality-factor, loading-factor, transmit-efficiency, and image-based signal-to-noise ratio (SNR) measurements. Phantom and in-vivo knee experiments for one volunteer were performed using two portable MRI systems operating at 3.53 and 3.04 MHz. Results: The electrical performance of the selected Litz wire decreased with increasing frequency, becoming a slight disadvantage at 3.53 MHz. Nevertheless, the stretchable coil provided the highest phantom SNR in both systems, exceeding that of the best-performing rigid coil by approximately 8 % at 3.53 MHz and 20 % at 3.04 MHz. In vivo, its global SNR was 5 % lower than that of the best rigid coil at 3.53 MHz and 19 % higher at 3.04 MHz. Compared with the larger rigid coil required when knee positioning is constrained, the corresponding SNR improvements were approximately 18 % and 80 %. Conclusion: Anatomical conformity can compensate for the frequency-dependent electrical limitations of Litz wire in stretchable low-field RF coils. The proposed design provided SNR comparable to or greater than the best rigid designs while facilitating coil placement in subjects for whom smaller rigid coils may be impractical.

physics.med-ph↗

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↗

arXiv2026

High-resolution ultra-low-field MRI with SNRAware denoising

Ultra-low-field (ULF, <0.1 T) magnetic resonance imaging (MRI) systems offer advantages in cost, portability, and accessibility, but their current utility is still limited by low signal-to-noise ratio (SNR). Deep learning (DL)-based denoising has emerged as a potential strategy to mitigate this limitation. In this work, we present a systematic evaluation of a high-performance DL denoising model trained using the SNRAware framework and applied to 88 mT and 72 mT data. Using a series of controlled experiments, we assessed model performance as a function of spatial resolution, coil impedance matching, readout bandwidth, input noise level, k-space undersampling, anatomy, image contrast, and scanner platform, and compared against analytical denoising algorithms. The model consistently increased the effective SNR of ULF acquisitions, enabling images with nominal spatial resolutions comparable to those commonly used in clinical 3 T protocols. Residual analyses indicated that the model predominantly removed stochastic noise while preserving underlying signal structure. At the same time, the results highlight some constraints: denoising performance remains dependent on the starting SNR of the acquisition, and training-domain mismatch influences behavior under certain artifact conditions. These findings suggest that DL-based denoising can significantly expand the practical capabilities of ULF MRI, while emphasizing potential benefits from hardware-software co-optimization and the need for rigorous clinical validation to determine the diagnostic value of denoised images.

physics.med-ph↗