arXiv subjects

Rafael Neto Henriques

Publications and source records attributed to Rafael Neto Henriques.

10 recordsLinked to original sources

arXiv2026

Axonal microstructure and compartmentalization impact the orientation and time dependence of mesoscopic transverse relaxation

In biological tissue, MR transverse relaxation stems from mechanisms spanning multiple scales, from molecular dipole-dipole interactions to mesoscopic field variations driven by tissue microstructure. While mesoscopic relaxation reflects cellular organization, its dynamics in white matter, specifically its dependence on axonal orientation and echo time, remain less investigated. This study employs theoretical frameworks and Monte-Carlo simulations using 3D EM-based white matter (WM) geometries to investigate how compartmentalization and realistic morphology drive these effects. Specifically, we simulate intra-axonal relaxation driven by magnetic fields induced by realistic axonal myelin sheaths and intra-axonal spheres as a model of iron-containing mitochondria. Our results confirm that orientation dependence of mesoscopic relaxation in WM is detectable and agrees with experimental observations. The time-dependence aligns with 1-dimensional short-range structural disorder, but at clinical echo times, this signature may be masked by dominant molecular relaxation. This work moves beyond idealized models to aid the development of more specific biophysical models of mesoscopic relaxation to achieve better biomarkers for neurodegenerative disease.

physics.med-ph↗

arXiv2023

Incorporating the effect of white matter microstructure in the estimation of magnetic susceptibility in ex-vivo mouse brain

Accurate estimation of microscopic magnetic field variations induced in biological tissue can be valuable for mapping tissue composition in health and disease. Here, we present an extension to Quantitative susceptibility mapping (QSM) to account for local white matter (WM) magnetic microstructure by using our previously presented model for solid cylinders with arbitrary orientations to describe axons in terms of concentric cylinders. Through computer simulations, we find that our model improves susceptibility estimation compared to QSM, and Susceptibility Tensor Imaging (STI) are substantially biased by the unaccounted-for structural anisotropy due to the mesoscopic frequency contribution, indicating the observed STI tensor might not represent susceptibility anisotropy as expected. Experiments in mouse brains acquired at ultrahigh field shows the mesoscopic contribution due to WM microstructure to be substantial. This in turn changed estimated susceptibility values up to 25% root-mean-squared-difference in WM compared to standard QSM. Our work underscores how microstructural field effects impact susceptibility estimates, and should not be neglected when imaging anisotropic tissue such as brain WM.

physics.med-ph↗

arXiv2022

Improved reproducibility of diffusion kurtosis imaging using regularized non-linear optimization informed by artificial neural networks

Diffusion kurtosis imaging is an extension of diffusion tensor imaging that provides scientifically and clinically valuable information about brain tissue microstructure but suffers from poor robustness to noise, especially in voxels containing tightly packed aligned axons. We present a new algorithm for estimating diffusion and kurtosis tensors using regularized non-linear optimization and make it publicly available in an easy-to-use open-source Python software package. Our approach uses fully-connected feed-forward neural networks to predict kurtosis values in voxels where the standard non-linear least squares fit fails. The predicted values are then used in the objective function to avoid implausible kurtosis values. We show that our algorithm is more robust than standard non-linear least squares and a previously proposed regularized non-linear optimization method. The algorithm was then applied on a multi-site scan-rescan dataset acquired using a clinical scan protocol to assess the reproducibility of diffusion kurtosis parameter estimation in human white matter using the proposed algorithm. Our results show that the reproducibility of diffusion kurtosis parameters is similar to diffusion tensor parameters.

physics.med-ph↗

arXiv2021

Evidence for microscopic kurtosis in neural tissue revealed by Correlation Tensor MRI

Purpose: The impact of microscopic diffusional kurtosis ($μK$) - arising from restricted diffusion and/or structural disorder - remains a controversial issue in contemporary diffusion MRI (dMRI). Recently, Correlation Tensor MRI (CTI) was introduced to disentangle the sources contributing to diffusional kurtosis, without relying on a-priori assumptions. Here, we aimed to investigate $μK$ in in vivo rat brains and assess its impact on state-of-the-art methods ignoring $μK$. Methods: CTI harnesses double diffusion encoding (DDE) experiments, which were here improved for speed and minimal bias using four different sets of acquisition parameters. The robustness of CTI estimates from the improved protocol is assessed in simulations. The in vivo CTI acquisitions were performed in healthy rat brains using a 9.4T pre-clinical scanner equipped with a cryogenic coil, and targeted the estimation of $μK$, anisotropic kurtosis, and isotropic kurtosis. Results: The improved CTI acquisition scheme substantially reduces scan time and importantly, also minimizes higher-order-term biases, thus enabling robust $μK$ estimation, alongside Kaniso and Kiso metrics. Our CTI experiments revealed positive $μK$ both in white and grey matter of the rat brain in vivo; $μK$ is the dominant kurtosis source in healthy grey matter tissue. The non-negligible $μK$ substantially biases prior state-of-the-art analyses of Kiso and Kaniso. Conclusion: Correlation Tensor MRI offers a more accurate and robust characterization of kurtosis sources than its predecessors. $μK$ is non-negligible in vivo in healthy white and grey matter tissues and could be an important biomarker for future studies. Our findings thus have both theoretical and practical implications for future experiments.

physics.bio-ph↗

arXiv2020

Free water DTI estimates from single b-value data might seem plausible but must be interpreted with care

Purpose: Free water elimination diffusion tensor imaging (FWE-DTI) has been widely used to distinguish increases of free water (FW) partial volume effects from tissue's diffusion in healthy ageing and degenerative diseases. Since the FWE-DTI fitting is only well posed for multi-shell acquisitions, a regularized gradient descent (RGD) method was proposed to enable application to single-shell data, more common in the clinic. However, the validity of the RGD method has been poorly assessed. This study aims to quantify the specificity of FWE-DTI procedures on single- and multi-shell data. Methods: Different FWE-DTI fitting procedures were tested on an open-source in vivo diffusion dataset and single- and multi-shell synthetic signals, including the RGD and standard non-linear least squares (NLS) methods. Single-voxel simulations were carried out to compare initialization approaches. A multi-voxel phantom simulation was performed to evaluate the impact of spatial regularization when comparing between methods. To test the algorithms' specificity, phantoms with two different types of lesions were simulated: with increased mean diffusivity (MD) or with increased FW. Result: Plausible parameter maps were obtained with RGD from single-shell in vivo data. The plausibility of these maps was shown to be determined by the initialization. Tests with simulated lesions inserted into the in vivo data revealed that the RGD approach cannot distinguish FW from tissue MD alterations, contrarily to the NLS algorithm. Conclusion: RGD FWE-DTI has limited specificity and, thus, its results from single-shell data should be carefully interpreted. When possible, multi-shell acquisitions and the NLS approach should be preferred instead.

physics.med-ph↗

arXiv2020

Correlation Tensor Magnetic Resonance Imaging

Diffusional Kurtosis Imaging (DKI) is a sensitive biomarker for microstructure in health and disease. However, DKI is not specific to any microstructural property since it may emerge from several different sources. Q-space trajectory encoding has been proposed for decoupling isotropic from anisotropic kurtosis. Still, this method assume that the system is comprised of multiple Gaussian diffusion components. Here, we develop a more general framework for resolving the underlying kurtosis sources. We introduce Correlation Tensor MRI (CTI) - an approach harnessing the versatility of double diffusion encoding (DDE) and capable of explicitly decoupling isotropic and anisotropic kurtosis components from intra-compartmental kurtosis effects arising from restricted diffusion. Additionally, CTI provides an index that is potentially sensitive to intra-compartmental kurtosis. The theoretical foundations of CTI, as well as the first proof-of-concept CTI ex vivo experiments in mouse brains at a field of 16.4T, are presented. We find that anisotropic and isotropic kurtosis can decouple microscopic anisotropy from substantial partial volume effects between tissue and free water. Our intra-compartmental kurtosis index exhibited positive values in both white and grey matter tissues. Simulations in different microenvironments show, however, that our current CTI protocol for estimating intra-compartmental kurtosis is limited by higher-order terms that were not considered in this study. CTI measurements were then extended to in vivo settings and used to map healthy rat brains at 9.4T. These in vivo CTI results were consistent with our ex vivo findings. Although future studies are still required to mitigate the higher-order effects on the intra-compartmental kurtosis index, results show that CTI's more general estimates of anisotropic and isotropic kurtosis contributions are already ripe for future studies.

physics.med-ph↗

arXiv2020

Fitting IVIM with Variable Projection and Simplicial Optimization

Fitting multi-exponential models to Diffusion MRI (dMRI) data has always been challenging due to various underlying complexities. In this work, we introduce a novel and robust fitting framework for the standard two-compartment IVIM microstructural model. This framework provides a significant improvement over the existing methods and helps estimate the associated diffusion and perfusion parameters of IVIM in an automatic manner. As a part of this work we provide capabilities to switch between more advanced global optimization methods such as simplicial homology (SH) and differential evolution (DE). Our experiments show that the results obtained from this simultaneous fitting procedure disentangle the model parameters in a reduced subspace. The proposed framework extends the seminal work originated in the MIX framework, with improved procedures for multi-stage fitting. This framework has been made available as an open-source Python implementation and disseminated to the community through the DIPY project.

eess.IV↗

arXiv2019

Validation and noise robustness assessment of microscopic anisotropy estimation with clinically feasible double diffusion encoding MRI

Purpose: Double diffusion encoding (DDE) MRI enables the estimation of microscopic diffusion anisotropy, yielding valuable information on tissue microstructure. A recent study proposed that the acquisition of rotationally invariant DDE metrics, typically obtained using a spherical "5-design", could be greatly simplified by assuming Gaussian diffusion, facilitating reduced acquisition times that are more compatible with clinical settings. Here, we aim to validate the new minimal acquisition scheme against the standard DDE 5-design, and to quantify the proposed method's noise robustness to facilitate future clinical use. Methods: DDE MRI experiments were performed on both ex vivo and in vivo rat brains at 9.4 T using the 5-design and the proposed minimal design and taking into account the difference in the number of acquisitions. The ensuing microscopic fractional anisotropy (μFA) maps were compared over a range of b-values up to 5000 s/mm2. Noise robustness was studied using analytical calculations and numerical simulations. Results: The minimal protocol quantified μFA at an accuracy comparable to the estimates obtained via the more theoretically robust DDE 5-design. μFA's sensitivity to noise was found to strongly depend on compartment anisotropy and tensor magnitude in a non-linear fashion. When μFA < 0.75 or when mean diffusivity is particularly low, very high signal to noise ratio (SNR) is required for precise quantification of μFA. Conclusion: Our work supports using DDE for quantifying microscopic diffusion anisotropy in clinical settings but raises hitherto overlooked precision issues when measuring μFA with DDE and typical clinical SNR.

physics.med-ph↗

arXiv2019

Microscopic anisotropy misestimation in spherical-mean single diffusion encoding MRI

Purpose: Microscopic fractional anisotropy (μFA) can disentangle microstructural information from orientation dispersion. While double diffusion encoding (DDE) MRI methods are widely used to extract accurate μFA, it has only recently been proposed that powder-averaged single diffusion encoding (SDE) signals, when coupled with the diffusion standard model (SM) and a set of constraints, could be used for μFA estimation. This study aims to evaluate μFA as derived from the spherical mean technique (SMT) set of constraints, as well as more generally for powder-averaged SM signals. Methods: SDE experiments were performed at 16.4 T on an ex vivo mouse brain (Δ/δ = 12/1.5 ms). The μFA maps obtained from powder-averaged SDE signals were then compared to maps obtained from DDE-MRI experiments (Δ/τ/δ = 12/12/1.5 ms), which allow a model-free estimation of μFA. Theory and simulations that consider different types of heterogeneity are presented for corroborating the experimental findings. Results: μFA, as well as other estimates derived from powder-averaged SDE signals produced large deviations from the ground truth in both gray and white matter. Simulations revealed that these misestimations are likely a consequence of factors not considered by the underlying microstructural models (such as intercomponent and intracompartmental kurtosis). Conclusion: Powder-averaged SMT and (2-component) SM are unable to accurately report μFA and other microstructural parameters in ex vivo tissues. Improper model assumptions and constraints can significantly compromise parameter specificity. Further developments and validations are required prior to implementation of these models in clinical or preclinical research.

physics.med-ph↗

arXiv2017

Sustainable computational science: the ReScience initiative

Computer science offers a large set of tools for prototyping, writing, running, testing, validating, sharing and reproducing results, however computational science lags behind. In the best case, authors may provide their source code as a compressed archive and they may feel confident their research is reproducible. But this is not exactly true. James Buckheit and David Donoho proposed more than two decades ago that an article about computational results is advertising, not scholarship. The actual scholarship is the full software environment, code, and data that produced the result. This implies new workflows, in particular in peer-reviews. Existing journals have been slow to adapt: source codes are rarely requested, hardly ever actually executed to check that they produce the results advertised in the article. ReScience is a peer-reviewed journal that targets computational research and encourages the explicit replication of already published research, promoting new and open-source implementations in order to ensure that the original research can be replicated from its description. To achieve this goal, the whole publishing chain is radically different from other traditional scientific journals. ReScience resides on GitHub where each new implementation of a computational study is made available together with comments, explanations, and software tests.

cs.DL↗