arXiv subjects

Juan A. Hernandez-Tamames

Publications and source records attributed to Juan A. Hernandez-Tamames.

5 recordsLinked to original sources

arXiv2026

Self-Supervised Weighted Image Guided Quantitative MRI Super-Resolution

Object: To present and evaluate Self-supervised Weighted Image Guided quantitative MRI Super-Resolution (SWIG qMRI SR), a physics-informed framework recovering high-resolution (HR) qMRI from a rapid low-resolution (LR) acquisition guided by routine weighted images (wMRI), without HR training targets. Materials and Methods: A CNN matches acquired wMRI to images synthesized from predicted maps through forward signal models, while anchoring those maps to the acquired LR qMRI. Training and ablation used synthetic data (n = 27); cross-sequence generalizability was tested in three volunteers scanned with silent 3D MuPa-ZTE at 1 and 5min, with clinical T1w/T2w guides. Results: In synthetic data, dual-guide SR reduced the T1 high-frequency error norm 45.4% below baseline; removing the LR qMRI anchor raised gray-matter T1 error from 63 to 110ms. In vivo, super-resolved 1-min maps synthesized T1w images (SSIM 0.93, PSNR 27.3dB) surpassing synthesis from 5-min maps (SSIM 0.83), and improved synthesis of T2-FLAIR, never used as a guide (SSIM 0.69 to 0.75). Discussion: HR detail was recovered without HR supervision, and the network transferred across a different qMRI sequence. Because the guides are already acquired, adding a 1-min qMRI scan to a standard exam offers a practical route to routine clinical qMRI integration.

cs.CV↗

arXiv2026

Evaluating and Calibrating Diffusion Model-derived Uncertainty for Quantitative MRI Mapping

Quantitative MRI (qMRI) provides standardised tissue parameter maps, but the reliability of deep learning-based qMRI mapping methods is often not explicitly characterised. In this work we systematically evaluate uncertainty maps for quantitative MRI derived from multiple inferences of a data-consistent diffusion model-based qMRI framework. Evaluation on synthetic test data assessed error-awareness, high-error detection, selective prediction, and Gaussian interval calibration. Diffusion model-derived uncertainty was positively associated with the mapping error, while risk-coverage analysis showed that excluding high-uncertainty voxels reduced the retained error. However, the raw uncertainty was poorly calibrated for quantitative interval interpretation. Calibration was substantially improved using a post-hoc procedure combining prediction-value-dependent bias correction with scalar uncertainty scaling. Qualitative evaluation on a healthy volunteer showed spatially meaningful uncertainty patterns. These results indicate that diffusion model-derived uncertainty is informative for reliability assessment and selective prediction, but requires calibration for quantitative interval interpretation.

cs.CV↗

arXiv2026

q3-MuPa: Quick, Quiet, Quantitative Multi-Parametric MRI using Physics-Informed Diffusion Models

The 3D fast silent multi-parametric mapping sequence with zero echo time (MuPa-ZTE) is a novel quantitative MRI (qMRI) acquisition that enables nearly silent scanning by using a 3D phyllotaxis sampling scheme. MuPa-ZTE improves patient comfort and motion robustness, and generates quantitative maps of T1, T2, and proton density using the acquired weighted image series. In this work, we propose a diffusion model-based qMRI mapping method that leverages both a deep generative model and physics-based data consistency to further improve the mapping performance. Furthermore, our method enables additional acquisition acceleration, allowing high-quality qMRI mapping from a fourfold-accelerated MuPa-ZTE scan (approximately 1 minute). Specifically, we trained a denoising diffusion probabilistic model (DDPM) to map MuPa-ZTE image series to qMRI maps, and we incorporated the MuPa-ZTE forward signal model as an explicit data consistency (DC) constraint during inference. We compared our mapping method against a baseline dictionary matching approach and a purely data-driven diffusion model. The diffusion models were trained entirely on synthetic data generated from digital brain phantoms, eliminating the need for large real-scan datasets. We evaluated on synthetic data, a NISM/ISMRM phantom, healthy volunteers, and a patient with brain metastases. The results demonstrated that our method produces 3D qMRI maps with high accuracy, reduced noise and better preservation of structural details. Notably, it generalised well to real scans despite training on synthetic data alone. The combination of the MuPa-ZTE acquisition and our physics-informed diffusion model is termed q3-MuPa, a quick, quiet, and quantitative multi-parametric mapping framework, and our findings highlight its strong clinical potential.

physics.med-ph↗

arXiv2024

qMRI Diffuser: Quantitative T1 Mapping of the Brain using a Denoising Diffusion Probabilistic Model

Quantitative MRI (qMRI) offers significant advantages over weighted images by providing objective parameters related to tissue properties. Deep learning-based methods have demonstrated effectiveness in estimating quantitative maps from series of weighted images. In this study, we present qMRI Diffuser, a novel approach to qMRI utilising deep generative models. Specifically, we implemented denoising diffusion probabilistic models (DDPM) for T1 quantification in the brain, framing the estimation of quantitative maps as a conditional generation task. The proposed method is compared with the residual neural network (ResNet) and the recurrent inference machine (RIM) on both phantom and in vivo data. The results indicate that our method achieves improved accuracy and precision in parameter estimation, along with superior visual performance. Moreover, our method inherently incorporates stochasticity, enabling straightforward quantification of uncertainty. Hence, the proposed method holds significant promise for quantitative MR mapping.

cs.CV↗

arXiv2023

Neuromelanin-MRI using 2D GRE and deep learning: considerations for improving the visualization of substantia nigra and locus coeruleus

An optimized clinically feasible neuromelanin-MRI imaging protocol for visualising the SN and LC simultaneously using deep learning reconstruction is presented. We optimize flip-angle for optimal combined SN and LC depiction. We also experimented with combinations of anisotropic and isotropic in-plane resolution, partial vs full echoes and the number of averages. Phantom and in-vivo experiments on three healthy volunteers illustrate that high-resolution imaging combined with deep-learning denoising shows good depiction of the SN and LC with a clinically feasible sequence of around 7 minutes.

physics.med-ph↗