arXiv subjects

Erick Jorge Canales-Rodríguez

Publications and source records attributed to Erick Jorge Canales-Rodríguez.

4 recordsLinked to original sources

arXiv2025

Spatially Regularized Super-Resolved Constrained Spherical Deconvolution (SR$^2$-CSD) of Diffusion MRI Data

Constrained Spherical Deconvolution (CSD) is widely used to estimate the white matter fiber orientation distribution (FOD) from diffusion MRI data. Its angular resolution depends on the maximum spherical harmonic order ($l_{max}$): low $l_{max}$ yields smooth but poorly resolved FODs, while high $l_{max}$, as in Super-CSD, enables resolving fiber crossings with small inter-fiber angles but increases sensitivity to noise. In this proof-of-concept study, we introduce Spatially Regularized Super-Resolved CSD (SR$^2$-CSD), a novel method that regularizes Super-CSD using a spatial FOD prior estimated via a self-calibrated total variation denoiser. We evaluated SR$^2$-CSD against CSD and Super-CSD across four datasets: (i) the HARDI-2013 challenge numerical phantom, assessing angular and peak number errors across multiple signal-to-noise ratio (SNR) levels and CSD variants (single-/multi-shell, single-/multi-tissue); (ii) the Sherbrooke in vivo dataset, evaluating spatial coherence of FODs; (iii) a six-subject test-retest dataset acquired with both full (96 gradient directions) and subsampled (45 directions) protocols, assessing reproducibility; and (iv) the DiSCo phantom, evaluating tractography accuracy under varying SNR levels and multiple noise repetitions. Across all evaluations, SR$^2$-CSD consistently reduced angular and peak number errors, improved spatial coherence, enhanced test-retest reproducibility, and yielded connectivity matrices more strongly correlated with ground-truth. Most improvements were statistically significant under multiple-comparison correction. These results demonstrate that incorporating spatial priors into CSD is feasible, mitigates estimation instability, and improves FOD reconstruction accuracy.

physics.med-ph↗

arXiv2022

Validation and Generalizability of Self-Supervised Image Reconstruction Methods for Undersampled MRI

Deep learning methods have become the state of the art for undersampled MR reconstruction. Particularly for cases where it is infeasible or impossible for ground truth, fully sampled data to be acquired, self-supervised machine learning methods for reconstruction are becoming increasingly used. However potential issues in the validation of such methods, as well as their generalizability, remain underexplored. In this paper, we investigate important aspects of the validation of self-supervised algorithms for reconstruction of undersampled MR images: quantitative evaluation of prospective reconstructions, potential differences between prospective and retrospective reconstructions, suitability of commonly used quantitative metrics, and generalizability. Two self-supervised algorithms based on self-supervised denoising and the deep image prior were investigated. These methods are compared to a least squares fitting and a compressed sensing reconstruction using in-vivo and phantom data. Their generalizability was tested with prospectively under-sampled data from experimental conditions different to the training. We show that prospective reconstructions can exhibit significant distortion relative to retrospective reconstructions/ground truth. Furthermore, pixel-wise quantitative metrics may not capture differences in perceptual quality accurately, in contrast to a perceptual metric. In addition, all methods showed potential for generalization; however, generalizability is more affected by changes in anatomy/contrast than other changes. We further showed that no-reference image metrics correspond well with human rating of image quality for studying generalizability. Finally, we showed that a well-tuned compressed sensing reconstruction and learned denoising perform similarly on all data.

eess.IV↗

arXiv2022

Axial and radial axonal diffusivities from single encoding strongly diffusion-weighted MRI

We enable the estimation of the per-axon axial diffusivity from single encoding, strongly diffusion-weighted, pulsed gradient spin echo data. Additionally, we improve the estimation of the per-axon radial diffusivity compared to estimates based on spherical averaging. The use of strong diffusion weightings in magnetic resonance imaging (MRI) allows to approximate the signal in white matter as the sum of the contributions from axons. At the same time, spherical averaging leads to a major simplification of the modeling by removing the need to explicitly account for the unknown orientation distribution of axons. However, the spherically averaged signal acquired at strong diffusion weightings is not sensitive to the axial diffusivity, which cannot therefore be estimated. After revising existing theory, we introduce a new general method for the estimation of both axonal diffusivities at strong diffusion weightings based on zonal harmonics modeling. We additionally show how this could lead to estimates that are free from partial volume bias with, for instance, gray matter. We test the method on publicly available data from the MGH Adult Diffusion Human Connectome project dataset. We report reference values of axonal diffusivities based on 34 subjects, and derive estimates of axonal radii. We address the estimation problem also from the angle of the required data preprocessing, the presence of biases related to modeling assumptions, current limitations, and future possibilities.

physics.med-ph↗

arXiv2019

Robust Monte-Carlo Simulations in Diffusion-MRI: Effect of the substrate complexity and parameter choice on the reproducibility of results

Monte-Carlo Diffusion Simulations (MCDS) have been used extensively as a ground truth tool for the validation of microstructure models for Diffusion-Weighted MRI. However, methodological pitfalls in the design of the biomimicking geometrical configurations and the simulation parameters can lead to approximation biases. Such pitfalls affect the reliability of the estimated signal, as well as its validity and reproducibility as ground truth data. In this work, we first present a set of experiments in order to study three critical pitfalls encountered in the design of MCDS in the literature, namely, the number of simulated particles and time steps, simplifications in the intra-axonal substrate representation, and the impact of the substrate's size on the signal stemming from the extra-axonal space. The results obtained show important changes in the simulated signals and the recovered microstructure features when changes in those parameters are introduced. Thereupon, driven by our findings from the first studies, we outline a general framework able to generate complex substrates. We show the framework's capability to overcome the aforementioned simplifications by generating a complex crossing substrate, which preserves the volume in the crossing area and achieves a high packing density. The results presented in this work,along with the simulator developed, pave the way towards more realistic and reproducible Monte-Carlo simulations for Diffusion-Weighted MRI.

physics.med-ph↗