23 research outputs found
Model-Informed Machine Learning for Multi-component T2 Relaxometry
Recovering the T2 distribution from multi-echo T2 magnetic resonance (MR)
signals is challenging but has high potential as it provides biomarkers
characterizing the tissue micro-structure, such as the myelin water fraction
(MWF). In this work, we propose to combine machine learning and aspects of
parametric (fitting from the MRI signal using biophysical models) and
non-parametric (model-free fitting of the T2 distribution from the signal)
approaches to T2 relaxometry in brain tissue by using a multi-layer perceptron
(MLP) for the distribution reconstruction. For training our network, we
construct an extensive synthetic dataset derived from biophysical models in
order to constrain the outputs with \textit{a priori} knowledge of \textit{in
vivo} distributions. The proposed approach, called Model-Informed Machine
Learning (MIML), takes as input the MR signal and directly outputs the
associated T2 distribution. We evaluate MIML in comparison to non-parametric
and parametric approaches on synthetic data, an ex vivo scan, and
high-resolution scans of healthy subjects and a subject with Multiple
Sclerosis. In synthetic data, MIML provides more accurate and noise-robust
distributions. In real data, MWF maps derived from MIML exhibit the greatest
conformity to anatomical scans, have the highest correlation to a histological
map of myelin volume, and the best unambiguous lesion visualization and
localization, with superior contrast between lesions and normal appearing
tissue. In whole-brain analysis, MIML is 22 to 4980 times faster than
non-parametric and parametric methods, respectively.Comment: Preprint submitted to Medical Image Analysis (July 14, 2020
Variability and reproducibility of multi-echo T2 relaxometry: Insights from multi-site, multi-session and multi-subject MRI acquisitions
Quantitative magnetic resonance imaging (qMRI) can increase the specificity and sensitivity of conventional weighted MRI to underlying pathology by comparing meaningful physical or chemical parameters, measured in physical units, with normative values acquired in a healthy population. This study focuses on multi-echo T2 relaxometry, a qMRI technique that probes the complex tissue microstructure by differentiating compartment-specific T2 relaxation times. However, estimation methods are still limited by their sensitivity to the underlying noise. Moreover, estimating the model's parameters is challenging because the resulting inverse problem is ill-posed, requiring advanced numerical regularization techniques. As a result, the estimates from distinct regularization strategies are different. In this work, we aimed to investigate the variability and reproducibility of different techniques for estimating the transverse relaxation time of the intra- and extra-cellular space (T2IE) in gray (GM) and white matter (WM) tissue in a clinical setting, using a multi-site, multi-session, and multi-run T2 relaxometry dataset. To this end, we evaluated three different techniques for estimating the T2 spectra (two regularized non-negative least squares methods and a machine learning approach). Two independent analyses were performed to study the effect of using raw and denoised data. For both the GM and WM regions, and the raw and denoised data, our results suggest that the principal source of variance is the inter-subject variability, showing a higher coefficient of variation (CoV) than those estimated for the inter-site, inter-session, and inter-run, respectively. For all reconstruction methods studied, the CoV ranged between 0.32 and 1.64%. Interestingly, the inter-session variability was close to the inter-scanner variability with no statistical differences, suggesting that T2IE is a robust parameter that could be employed in multi-site neuroimaging studies. Furthermore, the three tested methods showed consistent results and similar intra-class correlation (ICC), with values superior to 0.7 for most regions. Results from raw data were slightly more reproducible than those from denoised data. The regularized non-negative least squares method based on the L-curve technique produced the best results, with ICC values ranging from 0.72 to 0.92
Probing myelin content of the human brain with MRI: A review
Rapid and efficient transmission of electric signals among neurons of vertebrates is ensured by myelin-insulating sheaths surrounding axons. Human cognition, sensation, and motor functions rely on the integrity of these layers, and demyelinating diseases often entail serious cognitive and physical impairments. Magnetic resonance imaging radically transformed the way these disorders are monitored, offering an irreplaceable tool to noninvasively examine the brain structure. Several advanced techniques based on MRI have been developed to provide myelin-specific contrasts and a quantitative estimation of myelin density in vivo. Here, the vast offer of acquisition strategies developed to date for this task is reviewed. Advantages and pitfalls of the different approaches are compared and discussed
Normative volumes and relaxation times at 3T during brain development.
While research has unveiled and quantified brain markers of abnormal neurodevelopment, clinicians still work with qualitative metrics for MRI brain investigation. The purpose of the current article is to bridge the knowledge gap between case-control cohort studies and individual patient care. Here, we provide a unique dataset of seventy-three 3-to-17 years-old healthy subjects acquired with a 6-minute MRI protocol encompassing T1 and T2 relaxation quantitative sequence that can be readily implemented in the clinical setting; MP2RAGE for T1 mapping and the prototype sequence GRAPPATINI for T2 mapping. White matter and grey matter volumes were automatically quantified. We further provide normative developmental curves based on these two imaging sequences; T1, T2 and volume normative ranges with respect to age were computed, for each ROI of a pediatric brain atlas. This open-source dataset provides normative values allowing to position individual patients acquired with the same protocol on the brain maturation curve and as such provides potentially useful quantitative biomarkers facilitating precise and personalized care
Quantitative brain relaxation atlases for personalized detection and characterization of brain pathology
Purpose To exploit the improved comparability and hardware independency of quantitative MRI, databases of MR physical parameters in healthy tissue are required, to which tissue properties of patients can be compared. In this work, normative values for longitudinal and transverse relaxation times in the brain were established and tested in single-subject comparisons for detection of abnormal relaxation times. Methods Relaxometry maps of the brain were acquired from 52 healthy volunteers. After spatially normalizing the volumes into a common space, T-1 and T-2 inter-subject variability within the healthy cohort was modeled voxel-wise. A method for a single-subject comparison against the atlases was developed by computing z-scores with respect to the established healthy norms. The comparison was applied to two multiple sclerosis and one clinically isolated syndrome cases for a proof of concept. Results The established atlases exhibit a low variation in white matter structures (median RMSE of models equal to 32 ms for T-1 and 4 ms for T-2), indicating that relaxation times are in a narrow range for normal tissues. The proposed method for single-subject comparison detected relaxation time deviations from healthy norms in the example patient data sets. Relaxation times were found to be increased in brain lesions (mean z-scores >5). Moreover, subtle and confluent differences (z-scores similar to 2-4) were observed in clinically plausible regions (between lesions, corpus callosum). Conclusions Brain T-1 and T-2 quantitative norms were derived voxel-wise with low variability in healthy tissue. Example patient deviation maps demonstrated good sensitivity of the atlases for detecting relaxation time alterations
Deriving Brain Myelin Water Fraction Maps from Relaxometry: a Data-Driven Approach
Currently, one of the gold-standard methods to obtain brain myelin water fraction (MWF) maps is the multi-echo spin-echo sequence. To overcome some of its limitations (e.g. long acquisition times), a data-driven approach for deriving MWF maps is proposed here. A general linear model (GLM) and a conditional generative adversarial network (cGAN) were trained to learn the reference MWF from T1 and T2 maps acquired in a healthy cohort. While GLM-derived maps exhibited MWF overestimation, especially in WM tissue, the cGAN yielded images in agreement with the reference. The proposed methods were preliminarily tested in patients and revealed myelin degradation in expected areas
Revisiting the T2 Spectrum Imaging Inverse Problem: Bayesian Regularized Non-Negative Least Squares
Multi-echo T2 magnetic resonance images contain information about the distribution of T2 relaxation times of compartmentalized water, from which we can estimate relevant brain tissue properties such as the myelin water fraction (MWF). Regularized non-negative least squares (NNLS) is the tool of choice for estimating non-parametric T2 spectra. However, the estimation is ill-conditioned, sensitive to noise, and highly affected by the employed regularization weight. The purpose of this study is threefold: first, we want to underline that the apparently innocuous use of two alternative parameterizations for solving the inverse problem, which we called the standard and alternative regularization forms, leads to different solutions; second, to assess the performance of both parameterizations; and third, to propose a new Bayesian regularized NNLS method (BayesReg). The performance of BayesReg was compared with that of two conventional approaches (L-curve and Chi-square (X2) fitting) using both regularization forms. We generated a large dataset of synthetic data, acquired in vivo human brain data in healthy participants for conducting a scan-rescan analysis, and correlated the myelin content derived from histology with the MWF estimated from ex vivo data. Results from synthetic data indicate that BayesReg provides accurate MWF estimates, comparable to those from L-curve and X2, and with better overall stability across a wider signal-to-noise range. Notably, we obtained superior results by using the alternative regularization form. The correlations reported in this study are higher than those reported in previous studies employing the same ex vivo and histological data. In human brain data, the estimated maps from L-curve and BayesReg were more reproducible. However, the T2 spectra produced by BayesReg were less affected by over-smoothing than those from L-curve. These findings suggest that BayesReg is a good alternative for estimating T2 distributions and MWF maps
Multi-compartment diffusion MRI, T2 relaxometry and myelin water imaging as neuroimaging descriptors for anomalous tissue detection
Multiple sclerosis (MS) is an inflammatory and neurodegenerative disease
characterized by diffuse and focal areas of tissue loss. Conventional MRI
techniques such as T1-weighted and T2-weighted scans are generally used in the
diagnosis and prognosis of the disease. Yet, these methods are limited by the
lack of specificity between lesions, their perilesional area and non-lesional
tissue. Alternative MRI techniques exhibit a higher level of sensitivity to
focal and diffuse MS pathology than conventional MRI acquisitions. However,
they still suffer from limited specificity when considered alone. In this work,
we have combined tissue microstructure information derived from
multicompartment diffusion MRI and T2 relaxometry models to explore the
voxel-based prediction power of a machine learning model in a cohort of MS
patients and healthy controls. Our results show that the combination of
multi-modal features, together with a boosting enhanced decision-tree based
classifier, which combines a set of weak classifiers to form a strong
classifier via a voting mechanism, is able to utilise the complementary
information for the classification of abnormal tissue.Comment: Accepted at ISBI202
Robust myelin water imaging from multi-echo T2 data using second-order Tikhonov regularization with control points
Myelin water imaging is an MRI technique used to quantify myelination in the brain. The state-of-the-art reconstruction method is based on non-negative least squares optimization with zero-order Tikhonov regularization. In this study, a second-order Tikhonov regularization approach with control points was examined. This penalty term is more efficient for promoting smooth solutions while minimizing the contamination between myelin and non-myelin components. The performance of the proposed algorithm was investigated on in-vivo and ex-vivo multi-echo T2 data. It exhibited a higher correlation with histology than the state-of-the-art method. Its stability was studied using scan-rescan data