62 research outputs found
Estimating axon radius using diffusion-relaxation MRI: calibrating a surface-based relaxation model with histology
Axon radius is a potential biomarker for brain diseases and a crucial tissue microstructure parameter that determines the speed of action potentials. Diffusion MRI (dMRI) allows non-invasive estimation of axon radius, but accurately estimating the radius of axons in the human brain is challenging. Most axons in the brain have a radius below one micrometer, which falls below the sensitivity limit of dMRI signals even when using the most advanced human MRI scanners. Therefore, new MRI methods that are sensitive to small axon radii are needed. In this proof-of-concept investigation, we examine whether a surface-based axonal relaxation process could mediate a relationship between intra-axonal T2 and T1 times and inner axon radius, as measured using postmortem histology. A unique in vivo human diffusion-T1-T2 relaxation dataset was acquired on a 3T MRI scanner with ultra-strong diffusion gradients, using a strong diffusion-weighting (i.e., b = 6,000 s/mm2) and multiple inversion and echo times. A second reduced diffusion-T2 dataset was collected at various echo times to evaluate the model further. The intra-axonal relaxation times were estimated by fitting a diffusion-relaxation model to the orientation-averaged spherical mean signals. Our analysis revealed that the proposed surface-based relaxation model effectively explains the relationship between the estimated relaxation times and the histological axon radius measured in various corpus callosum regions. Using these histological values, we developed a novel calibration approach to predict axon radius in other areas of the corpus callosum. Notably, the predicted radii and those determined from histological measurements were in close agreement
Hierarchical Tractography Optimisation
International audienc
Magnetic resonance imaging of T2 - and diffusion snisotropy using a tiltable receive coil
The anisotropic microstructure of white matter is reflected in various MRI contrasts. Transverse relaxation rates can be probed as a function of fibre-orientation with respect to the main magnetic field, while diffusion properties are probed as a function of fibre-orientation with respect to an encoding gradient. While the latter is easy to obtain by varying the orientation of the gradient, as the magnetic field is fixed, obtaining the former requires re-orienting the head. In this work we deployed a tiltable RF-coil to study T2 - and diffusional anisotropy of the brain white matter simultaneously in diffusion- T2 correlation experiments
Learn to Ignore: Domain Adaptation for Multi-Site MRI Analysis
The limited availability of large image datasets, mainly due to data privacy
and differences in acquisition protocols or hardware, is a significant issue in
the development of accurate and generalizable machine learning methods in
medicine. This is especially the case for Magnetic Resonance (MR) images, where
different MR scanners introduce a bias that limits the performance of a machine
learning model. We present a novel method that learns to ignore the
scanner-related features present in MR images, by introducing specific
additional constraints on the latent space. We focus on a real-world
classification scenario, where only a small dataset provides images of all
classes. Our method \textit{Learn to Ignore (L2I)} outperforms state-of-the-art
domain adaptation methods on a multi-site MR dataset for a classification task
between multiple sclerosis patients and healthy controls
GAMER-MRIL identifies Disability-Related Brain Changes in Multiple Sclerosis
Objective: Identifying disability-related brain changes is important for
multiple sclerosis (MS) patients. Currently, there is no clear understanding
about which pathological features drive disability in single MS patients. In
this work, we propose a novel comprehensive approach, GAMER-MRIL, leveraging
whole-brain quantitative MRI (qMRI), convolutional neural network (CNN), and an
interpretability method from classifying MS patients with severe disability to
investigating relevant pathological brain changes. Methods:
One-hundred-sixty-six MS patients underwent 3T MRI acquisitions. qMRI
informative of microstructural brain properties was reconstructed, including
quantitative T1 (qT1), myelin water fraction (MWF), and neurite density index
(NDI). To fully utilize the qMRI, GAMER-MRIL extended a gated-attention-based
CNN (GAMER-MRI), which was developed to select patch-based qMRI important for a
given task/question, to the whole-brain image. To find out disability-related
brain regions, GAMER-MRIL modified a structure-aware interpretability method,
Layer-wise Relevance Propagation (LRP), to incorporate qMRI. Results: The test
performance was AUC=0.885. qT1 was the most sensitive measure related to
disability, followed by NDI. The proposed LRP approach obtained more
specifically relevant regions than other interpretability methods, including
the saliency map, the integrated gradients, and the original LRP. The relevant
regions included the corticospinal tract, where average qT1 and NDI
significantly correlated with patients' disability scores (=-0.37 and
0.44). Conclusion: These results demonstrated that GAMER-MRIL can classify
patients with severe disability using qMRI and subsequently identify brain
regions potentially important to the integrity of the mobile function.
Significance: GAMER-MRIL holds promise for developing biomarkers and increasing
clinicians' trust in NN
Evaluation of tractography-based myelin-weighted connectivity across the lifespan
IntroductionRecent studies showed that the myelin of the brain changes in the life span, and demyelination contributes to the loss of brain plasticity during normal aging. Diffusion-weighted magnetic resonance imaging (dMRI) allows studying brain connectivity in vivo by mapping axons in white matter with tractography algorithms. However, dMRI does not provide insight into myelin; thus, combining tractography with myelin-sensitive maps is necessary to investigate myelin-weighted brain connectivity. Tractometry is designated for this purpose, but it suffers from some serious limitations. Our study assessed the effectiveness of the recently proposed Myelin Streamlines Decomposition (MySD) method in estimating myelin-weighted connectomes and its capacity to detect changes in myelin network architecture during the process of normal aging. This approach opens up new possibilities compared to traditional Tractometry.MethodsIn a group of 85 healthy controls aged between 18 and 68 years, we estimated myelin-weighted connectomes using Tractometry and MySD, and compared their modulation with age by means of three well-known global network metrics.ResultsFollowing the literature, our results show that myelin development continues until brain maturation (40 years old), after which degeneration begins. In particular, mean connectivity strength and efficiency show an increasing trend up to 40 years, after which the process reverses. Both Tractometry and MySD are sensitive to these changes, but MySD turned out to be more accurate.ConclusionAfter regressing the known predictors, MySD results in lower residual error, indicating that MySD provides more accurate estimates of myelin-weighted connectivity than Tractometry
Relax! Diffusion is not the only way to estimate axon radius in vivo
Axon radius is a potential biomarker for brain diseases and a crucial tissue
microstructure parameter that determines the speed of action potentials.
Diffusion MRI (dMRI) allows non-invasive estimation of axon radius, but
accurately estimating the radius of axons in the human brain is challenging.
Most axons in the brain have a radius below one micrometre, which falls below
the sensitivity limit of dMRI signals even when using the most advanced human
MRI scanners. Therefore, new MRI methods that are sensitive to small axon radii
are needed. In this proof-of-concept investigation, we examine whether a
surface-based axonal relaxation process could mediate a relationship between
intra-axonal T2 and T1 times and inner axon radius, as measured using
postmortem histology. A unique in vivo human diffusion-T1-T2 relaxation dataset
was acquired on a 3T MRI scanner with ultra-strong diffusion gradients, using a
strong diffusion-weighting (i.e., b=6000 s/mm2) and multiple inversion and echo
times. A second reduced diffusion-T2 dataset was collected at various echo
times to evaluate the model further. The intra-axonal relaxation times were
estimated by fitting a diffusion-relaxation model to the orientation-averaged
spherical mean signals. Our analysis revealed that the proposed surface-based
relaxation model effectively explains the relationship between the estimated
relaxation times and the histological axon radius measured in various corpus
callosum regions. Using these histological values, we developed a novel
calibration approach to predict axon radius in other areas of the corpus
callosum. Notably, the predicted radii and those determined from histological
measurements were in close agreement.Comment: 48 pages, 10 figure
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
A Multicenter Longitudinal MRI Study Assessing LeMan-PV Software Accuracy in the Detection of White Matter Lesions in Multiple Sclerosis Patients.
BACKGROUND
Detecting new and enlarged lesions in multiple sclerosis (MS) patients is needed to determine their disease activity. LeMan-PV is a software embedded in the scanner reconstruction system of one vendor, which automatically assesses new and enlarged white matter lesions (NELs) in the follow-up of MS patients; however, multicenter validation studies are lacking.
PURPOSE
To assess the accuracy of LeMan-PV for the longitudinal detection NEL white-matter MS lesions in a multicenter clinical setting.
STUDY TYPE
Retrospective, longitudinal.
SUBJECTS
A total of 206 patients with a definitive MS diagnosis and at least two follow-up MRI studies from five centers participating in the Swiss Multiple Sclerosis Cohort study. Mean age at first follow-up = 45.2 years (range: 36.9-52.8 years); 70 males.
FIELD STRENGTH/SEQUENCE
Fluid attenuated inversion recovery (FLAIR) and T1-weighted magnetization prepared rapid gradient echo (T1-MPRAGE) sequences at 1.5 T and 3 T.
ASSESSMENT
The study included 313 MRI pairs of datasets. Data were analyzed with LeMan-PV and compared with a manual "reference standard" provided by a neuroradiologist. A second rater (neurologist) performed the same analysis in a subset of MRI pairs to evaluate the rating-accuracy. The Sensitivity (Se), Specificity (Sp), Accuracy (Acc), F1-score, lesion-wise False-Positive-Rate (aFPR), and other measures were used to assess LeMan-PV performance for the detection of NEL at 1.5 T and 3 T. The performance was also evaluated in the subgroup of 123 MRI pairs at 3 T.
STATISTICAL TESTS
Intraclass correlation coefficient (ICC) and Cohen's kappa (CK) were used to evaluate the agreement between readers.
RESULTS
The interreader agreement was high for detecting new lesions (ICC = 0.97, Pvalue < 10-20 , CK = 0.82, P value = 0) and good (ICC = 0.75, P value < 10-12 , CK = 0.68, P value = 0) for detecting enlarged lesions. Across all centers, scanner field strengths (1.5 T, 3 T), and for NEL, LeMan-PV achieved: Acc = 61%, Se = 65%, Sp = 60%, F1-score = 0.44, aFPR = 1.31. When both follow-ups were acquired at 3 T, LeMan-PV accuracy was higher (Acc = 66%, Se = 66%, Sp = 66%, F1-score = 0.28, aFPR = 3.03).
DATA CONCLUSION
In this multicenter study using clinical data settings acquired at 1.5 T and 3 T, and variations in MRI protocols, LeMan-PV showed similar sensitivity in detecting NEL with respect to other recent 3 T multicentric studies based on neural networks. While LeMan-PV performance is not optimal, its main advantage is that it provides automated clinical decision support integrated into the radiological-routine flow.
EVIDENCE LEVEL
4 TECHNICAL EFFICACY: Stage 2
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