Diffusion-weighted magnetic resonance imaging (DW-MRI) is used to characterize brain tissue microstructure employing tissue-specific biophysical models. A current limitation, however, is that most of the proposed models are based on the assumption of negligible water exchange between the intra- and extracellular compartments, which might not be valid in various brain tissues, including unmyelinated axons, gray matter, and tumors. The purpose of this work is to quantify the effect of membrane permeability on the estimates of two popular models neglecting exchange, and compare their performance with a model including exchange. To this aim, DW-MRI experiments were performed in controlled environments with Monte-Carlo simulations. The DW-MRI signals were generated in numerical substrates mimicking biological tissue made of spherical cells with permeable membranes like cancerous tissue or the brain gray matter. From these signals, the substrates properties were estimated using SANDI and VERDICT, the two compartment-based models neglecting exchange, and CEXI, a new model which includes exchange. Our results show that, in cellular permeable tissue, the model with exchange outperformed models without exchange in the estimation of the tissue properties by providing more stable estimates of cell size, intracellular volume fraction and extracellular diffusion coefficient. Moreover, the model with exchange estimated accurately the exchange time in the range of permeability reported for cellular tissue. Finally, the simulations performed in this work showed that the exchange between the intracellular and the extracellular space cannot be neglected in permeable tissue with a conventional PGSE sequence, to obtain accurate estimates. Consequently, existing compartmentalized models of impermeable tissue cannot be used for microstructure estimation of cellular permeable tissue.
Diffusion MRI (dMRI) of the developing brain can provide valuable insights into the white matter development. However, slice thickness in fetal dMRI is typically high (i.e., 3-5 mm) to freeze the in-plane motion, which reduces the sensitivity of the dMRI signal to the underlying anatomy. In this study, we aim at overcoming this problem by using autoencoders to learn unsupervised efficient representations of brain slices in a latent space, using raw dMRI signals and their spherical harmonics (SH) representation. We first learn and quantitatively validate the autoencoders on the developing Human Connectome Project pre-term newborn data, and further test the method on fetal data. Our results show that the autoencoder in the signal domain better synthesized the raw signal. Interestingly, the fractional anisotropy and, to a lesser extent, the mean diffusivity, are best recovered in missing slices by using the autoencoder trained with SH coefficients. A comparison was performed with the same maps reconstructed using an autoencoder trained with raw signals, as well as conventional interpolation methods of raw signals and SH coefficients. From these results, we conclude that the recovery of missing/corrupted slices should be performed in the signal domain if the raw signal is aimed to be recovered, and in the SH domain if diffusion tensor properties (i.e., fractional anisotropy) are targeted. Notably, the trained autoencoders were able to generalize to fetal dMRI data acquired using a much smaller number of diffusion gradients and a lower b-value, where we qualitatively show the consistency of the estimated diffusion tensor maps.
Anatomic tracing data provides detailed information on brain circuitry essential for addressing some of the common errors in diffusion MRI tractography. However, automated detection of fiber bundles on tracing data is challenging due to sectioning distortions, presence of noise and artifacts and intensity/contrast variations. In this work, we propose a deep learning method with a self-supervised loss function that takes anatomy-based constraints into account for accurate segmentation of fiber bundles on the tracer sections from macaque brains. Also, given the limited availability of manual labels, we use a semi-supervised training technique for efficiently using unlabeled data to improve the performance, and location constraints for further reduction of false positives. Evaluation of our method on unseen sections from a different macaque yields promising results with a true positive rate of ~0.90. The code for our method is available at https://github.com/v-sundaresan/fiberbundle_seg_tracing.
This article introduces a new methodology for reconstructing the white matter fiber pathways of brain in diffusion MRI. Usually, the signal intensity values will be lesser in the direction of higher diffusivity. The proposed approach picks the diffusion sensitivity gradient directions (dSGD), where the signal intensities are diminutive. Considering these as the directions of maximum diffusivity, we generate directions uniformly distributed around the picked dSGD. These newly computed uniformly spaced directions are considered gradient directions used in the reconstruction process. The state-of-art schemes like uniform gradient direction (UGD) have redundancy in the gradient direction, and adaptive gradient direction (AGD) has a constraint of solving linear system twice per voxel. These two limitations are turned down in this study simultaneously. Estimating gradient directions with the proposed scheme is employed in the multi-compartmental mixture models for calculating the fiber orientations. Simulation and experiments on the real data evaluate the feasibility of the proposed method.
Diffusion MRI tractography is an advanced imaging technique that enables in vivo mapping of the brain's white matter connections. White matter parcellation classifies tractography streamlines into clusters or anatomically meaningful tracts. It enables quantification and visualization of whole-brain tractography. Currently, most parcellation methods focus on the deep white matter (DWM), whereas fewer methods address the superficial white matter (SWM) due to its complexity. We propose a novel two-stage deep-learning-based framework, Superficial White Matter Analysis (SupWMA), that performs an efficient and consistent parcellation of 198 SWM clusters from whole-brain tractography. A point-cloud-based network is adapted to our SWM parcellation task, and supervised contrastive learning enables more discriminative representations between plausible streamlines and outliers for SWM. We train our model on a large-scale tractography dataset including streamline samples from labeled long- and medium-range (over 40 mm) SWM clusters and anatomically implausible streamline samples, and we perform testing on six independently acquired datasets of different ages and health conditions (including neonates and patients with space-occupying brain tumors). Compared to several state-of-the-art methods, SupWMA obtains highly consistent and accurate SWM parcellation results on all datasets, showing good generalization across the lifespan in health and disease. In addition, the computational speed of SupWMA is much faster than other methods.
Diffusion MRI imaging and tractography algorithms have enabled the mapping of the macro-scale connectome of the entire brain. At the functional level, probably the simplest way to study the dynamics of macro-scale brain activity is to compute the "activation cascade" that follows the artificial stimulation of a source region. Such cascades can be computed using the Linear Threshold model on a weighted graph representation of the connectome. The question we focus on is: if we are given such activation cascades for two groups, say A and B (e.g. Controls versus a mental disorder), what is the smallest set of brain connectivity (graph edge weight) changes that are sufficient to explain the observed differences in the activation cascades between the two groups? We have developed and computationally validated an efficient algorithm, TRACED, to solve the previous problem. We argue that this approach to compare the connectomes of two groups, based on activation cascades, is more insightful than simply identifying "static" network differences (such as edges with large weight or centrality differences). We have also applied the proposed method in the comparison between a Major Depressive Disorder (MDD) group versus healthy controls and briefly report the resulting set of connections that cause most of the observed cascade differences.
Diffusion MRI tractography is an advanced imaging technique for quantitative mapping of the brain's structural connectivity. Whole brain tractography (WBT) data contains over hundreds of thousands of individual fiber streamlines (estimated brain connections), and this data is usually parcellated to create compact representations for data analysis applications such as disease classification. In this paper, we propose a novel parcellation-free WBT analysis framework, TractoFormer, that leverages tractography information at the level of individual fiber streamlines and provides a natural mechanism for interpretation of results using the attention mechanism of transformers. TractoFormer includes two main contributions. First, we propose a novel and simple 2D image representation of WBT, TractoEmbedding, to encode 3D fiber spatial relationships and any feature of interest that can be computed from individual fibers (such as FA or MD). Second, we design a network based on vision transformers (ViTs) that includes: 1) data augmentation to overcome model overfitting on small datasets, 2) identification of discriminative fibers for interpretation of results, and 3) ensemble learning to leverage fiber information from different brain regions. In a synthetic data experiment, TractoFormer successfully identifies discriminative fibers with simulated group differences. In a disease classification experiment comparing several methods, TractoFormer achieves the highest accuracy in classifying schizophrenia vs control. Discriminative fibers are identified in left hemispheric frontal and parietal superficial white matter regions, which have previously been shown to be affected in schizophrenia patients.
White matter tract microstructure has been shown to influence neuropsychological scores of cognitive performance. However, prediction of these scores from white matter tract data has not been attempted. In this paper, we propose a deep-learning-based framework for neuropsychological score prediction using microstructure measurements estimated from diffusion magnetic resonance imaging (dMRI) tractography, focusing on predicting performance on a receptive vocabulary assessment task based on a critical fiber tract for language, the arcuate fasciculus (AF). We directly utilize information from all points in a fiber tract, without the need to average data along the fiber as is traditionally required by diffusion MRI tractometry methods. Specifically, we represent the AF as a point cloud with microstructure measurements at each point, enabling adoption of point-based neural networks. We improve prediction performance with the proposed Paired-Siamese Loss that utilizes information about differences between continuous neuropsychological scores. Finally, we propose a Critical Region Localization (CRL) algorithm to localize informative anatomical regions containing points with strong contributions to the prediction results. Our method is evaluated on data from 806 subjects from the Human Connectome Project dataset. Results demonstrate superior neuropsychological score prediction performance compared to baseline methods. We discover that critical regions in the AF are strikingly consistent across subjects, with the highest number of strongly contributing points located in frontal cortical regions (i.e., the rostral middle frontal, pars opercularis, and pars triangularis), which are strongly implicated as critical areas for language processes.
This paper demonstrates spherical convolutional neural networks (S-CNN) offer distinct advantages over conventional fully-connected networks (FCN) at estimating scalar parameters of tissue microstructure from diffusion MRI (dMRI). Such microstructure parameters are valuable for identifying pathology and quantifying its extent. However, current clinical practice commonly acquires dMRI data consisting of only 6 diffusion weighted images (DWIs), limiting the accuracy and precision of estimated microstructure indices. Machine learning (ML) has been proposed to address this challenge. However, existing ML-based methods are not robust to differing dMRI gradient sampling schemes, nor are they rotation equivariant. Lack of robustness to sampling schemes requires a new network to be trained for each scheme, complicating the analysis of data from multiple sources. A possible consequence of the lack of rotational equivariance is that the training dataset must contain a diverse range of microstucture orientations. Here, we show spherical CNNs represent a compelling alternative that is robust to new sampling schemes as well as offering rotational equivariance. We show the latter can be leveraged to decrease the number of training datapoints required.
Diffusion MRI is a modern neuroimaging modality with a unique ability to acquire microstructural information by measuring water self-diffusion at the voxel level. However, it generates huge amounts of data, resulting from a large number of repeated 3D scans. Each volume samples a location in q-space, indicating the direction and strength of a diffusion sensitizing gradient during the measurement. This captures detailed information about the self-diffusion, and the tissue microstructure that restricts it. Lossless compression with GZIP is widely used to reduce the memory requirements. We introduce a novel lossless codec for diffusion MRI data. It reduces file sizes by more than 30% compared to GZIP, and also beats lossless codecs from the JPEG family. Our codec builds on recent work on lossless PDE-based compression of 3D medical images, but additionally exploits smoothness in q-space. We demonstrate that, compared to using only image space PDEs, q-space PDEs further improve compression rates. Moreover, implementing them with Finite Element Methods and a custom acceleration significantly reduces computational expense. Finally, we show that our codec clearly benefits from integrating subject motion correction, and slightly from optimizing the order in which the 3D volumes are coded.
Diffusion magnetic resonance imaging (dMRI) is an important tool in characterizing tissue microstructure based on biophysical models, which are complex and highly non-linear. Resolving microstructures with optimization techniques is prone to estimation errors and requires dense sampling in the q-space. Deep learning based approaches have been proposed to overcome these limitations. Motivated by the superior performance of the Transformer, in this work, we present a learning-based framework based on Transformer, namely, a Microstructure Estimation Transformer with Sparse Coding (METSC) for dMRI-based microstructure estimation with downsampled q-space data. To take advantage of the Transformer while addressing its limitation in large training data requirements, we explicitly introduce an inductive bias - model bias into the Transformer using a sparse coding technique to facilitate the training process. Thus, the METSC is composed with three stages, an embedding stage, a sparse representation stage, and a mapping stage. The embedding stage is a Transformer-based structure that encodes the signal to ensure the voxel is represented effectively. In the sparse representation stage, a dictionary is constructed by solving a sparse reconstruction problem that unfolds the Iterative Hard Thresholding (IHT) process. The mapping stage is essentially a decoder that computes the microstructural parameters from the output of the second stage, based on the weighted sum of normalized dictionary coefficients where the weights are also learned. We tested our framework on two dMRI models with downsampled q-space data, including the intravoxel incoherent motion (IVIM) model and the neurite orientation dispersion and density imaging (NODDI) model. The proposed method achieved up to 11.25 folds of acceleration in scan time and outperformed the other state-of-the-art learning-based methods.
Alterations to structural and functional brain networks have been reported across many neurological conditions. However, the relationship between structure and function -- their coupling -- is relatively unexplored, particularly in the context of an intervention. Epilepsy surgery alters the brain structure and networks to control the functional abnormality of seizures. Given that surgery is a structural modification aiming to alter the function, we hypothesized that stronger structure-function coupling preoperatively is associated with a greater chance of post-operative seizure control. We constructed structural and functional brain networks in 39 subjects with medication-resistant focal epilepsy using data from intracranial EEG (pre-surgery), structural MRI (pre-and post-surgery), and diffusion MRI (pre-surgery). We investigated pre-operative structure-function coupling at two spatial scales a) at the global iEEG network level and b) at the resolution of individual iEEG electrode contacts using virtual surgeries. At global network level, seizure-free individuals had stronger structure-function coupling pre-operatively than those that were not seizure-free regardless of the choice of interictal segment or frequency band. At the resolution of individual iEEG contacts, the virtual surgery approach provided complementary information to localize epileptogenic tissues. In predicting seizure outcomes, structure-function coupling measures were more important than clinical attributes, and together they predicted seizure outcomes with an accuracy of 85% and sensitivity of 87%. The underlying assumption that the structural changes induced by surgery translate to the functional level to control seizures is valid when the structure-functional coupling is strong. Mapping the regions that contribute to structure-functional coupling using virtual surgeries may help aid surgical planning.
High resolution diffusion MRI (dMRI) data is often constrained by limited scanning time in clinical settings, thus restricting the use of downstream analysis techniques that would otherwise be available. In this work we develop a 3D recurrent convolutional neural network (RCNN) capable of super-resolving dMRI volumes in the angular (q-space) domain. Our approach formulates the task of angular super-resolution as a patch-wise regression using a 3D autoencoder conditioned on target b-vectors. Within the network we use a convolutional long short term memory (ConvLSTM) cell to model the relationship between q-space samples. We compare model performance against a baseline spherical harmonic interpolation and a 1D variant of the model architecture. We show that the 3D model has the lowest error rates across different subsampling schemes and b-values. The relative performance of the 3D RCNN is greatest in the very low angular resolution domain. Code for this project is available at https://github.com/m-lyon/dMRI-RCNN.
The isocitrate dehydrogenase (IDH) gene mutation is an essential biomarker for the diagnosis and prognosis of glioma. It is promising to better predict glioma genotype by integrating focal tumor image and geometric features with brain network features derived from MRI. Convolutions neural networks show reasonable performance in predicting IDH mutation, which, however, cannot learn from non-Euclidean data, e.g., geometric and network data. In this study, we propose a multi-modal learning framework using three separate encoders to extract features of focal tumor image, tumor geometrics and global brain networks. To mitigate the limited availability of diffusion MRI, we develop a self-supervised approach to generate brain networks from anatomical multi-sequence MRI. Moreover, to extract tumor-related features from the brain network, we design a hierarchical attention module for the brain network encoder. Further, we design a bi-level multi-modal contrastive loss to align the multi-modal features and tackle the domain gap at the focal tumor and global brain. Finally, we propose a weighted population graph to integrate the multi-modal features for genotype prediction. Experimental results on the testing set show that the proposed model outperforms the baseline deep learning models. The ablation experiments validate the performance of different components of the framework. The visualized interpretation corresponds to clinical knowledge with further validation. In conclusion, the proposed learning framework provides a novel approach for predicting the genotype of glioma.
Alzheimer's disease (AD) is the most common age-related dementia. Mild cognitive impairment (MCI) is the early stage of cognitive decline before AD. It is crucial to predict the MCI-to-AD conversion for precise management, which remains challenging due to the diversity of patients. Previous evidence shows that the brain network generated from diffusion MRI promises to classify dementia using deep learning. However, the limited availability of diffusion MRI challenges the model training. In this study, we develop a self-supervised contrastive learning approach to generate structural brain networks from routine anatomical MRI under the guidance of diffusion MRI. The generated brain networks are applied to train a learning framework for predicting the MCI-to-AD conversion. Instead of directly modelling the AD brain networks, we train a graph encoder and a variational autoencoder to model the healthy ageing trajectories from brain networks of healthy controls. To predict the MCI-to-AD conversion, we further design a recurrent neural networks based approach to model the longitudinal deviation of patients' brain networks from the healthy ageing trajectory. Numerical results show that the proposed methods outperform the benchmarks in the prediction task. We also visualize the model interpretation to explain the prediction and identify abnormal changes of white matter tracts.
Diffusion MRI (dMRI) is the only non-invasive technique sensitive to tissue micro-architecture, which can, in turn, be used to reconstruct tissue microstructure and white matter pathways. The accuracy of such tasks is hampered by the low signal-to-noise ratio in dMRI. Today, the noise is characterized mainly by visual inspection of residual maps and estimated standard deviation. However, it is hard to estimate the impact of noise on downstream tasks based only on such qualitative assessments. To address this issue, we introduce a novel metric, Noise Uncertainty Quantification (NUQ), for quantitative image quality analysis in the absence of a ground truth reference image. NUQ uses a recent Bayesian formulation of dMRI models to estimate the uncertainty of microstructural measures. Specifically, NUQ uses the maximum mean discrepancy metric to compute a pooled quality score by comparing samples drawn from the posterior distribution of the microstructure measures. We show that NUQ allows a fine-grained analysis of noise, capturing details that are visually imperceptible. We perform qualitative and quantitative comparisons on real datasets, showing that NUQ generates consistent scores across different denoisers and acquisitions. Lastly, by using NUQ on a cohort of schizophrenics and controls, we quantify the substantial impact of denoising on group differences.
Biophysical modelling of the diffusion MRI signal provides estimates of specific microstructural tissue properties. Although nonlinear optimization such as non-linear least squares (NLLS) is the most widespread method for model estimation, it suffers from local minima and high computational cost. Deep Learning approaches are steadily replacing NL fitting, but come with the limitation that the model needs to be retrained for each acquisition protocol and noise level. The White Matter Tract Integrity (WMTI)-Watson model was proposed as an implementation of the Standard Model of diffusion in white matter that estimates model parameters from the diffusion and kurtosis tensors (DKI). Here we proposed a deep learning approach based on the encoder-decoder recurrent neural network (RNN) to increase the robustness and accelerate the parameter estimation of WMTI-Watson. We use an embedding approach to render the model insensitive to potential differences in distributions between training data and experimental data. This RNN-based solver thus has the advantage of being highly efficient in computation and more readily translatable to other datasets, irrespective of acquisition protocol and underlying parameter distributions as long as DKI was pre-computed from the data. In this study, we evaluated the performance of NLLS, the RNN-based method and a multilayer perceptron (MLP) on synthetic and in vivo datasets of rat and human brain. We showed that the proposed RNN-based fitting approach had the advantage of highly reduced computation time over NLLS (from hours to seconds), with similar accuracy and precision but improved robustness, and superior translatability to new datasets over MLP.
The human brain's white matter (WM) structure is of immense interest to the scientific community. Diffusion MRI gives a powerful tool to describe the brain WM structure noninvasively. To potentially enable monitoring of age-related changes and investigation of sex-related brain structure differences on the mapping between the brain connectome and healthy subjects' age and sex, we extract fiber-cluster-based diffusion features and predict sex and age with a novel ensembled neural network classifier. We conduct experiments on the Human Connectome Project (HCP) young adult dataset and show that our model achieves 94.82% accuracy in sex prediction and 2.51 years MAE in age prediction. We also show that the fractional anisotropy (FA) is the most predictive of sex, while the number of fibers is the most predictive of age and the combination of different features can improve the model performance.
High spatio-angular resolution diffusion MRI (dMRI) has been shown to provide accurate identification of complex neuronal fiber configurations, albeit, at the cost of long acquisition times. We propose a method to recover intra-voxel fiber configurations at high spatio-angular resolution relying on a 3D kq-space under-sampling scheme to enable accelerated acquisitions. Simulations and real data analysis suggest that accurate FOD mapping can be achieved from severe kq-space under-sampling regimes potentially enabling high spatio-angular resolution dMRI in the clinical setting.
It is highly desirable to know how uncertain a model's predictions are, especially for models that are complex and hard to understand as in deep learning. Although there has been a growing interest in using deep learning methods in diffusion-weighted MRI, prior works have not addressed the issue of model uncertainty. Here, we propose a deep learning method to estimate the diffusion tensor and compute the estimation uncertainty. Data-dependent uncertainty is computed directly by the network and learned via loss attenuation. Model uncertainty is computed using Monte Carlo dropout. We also propose a new method for evaluating the quality of predicted uncertainties. We compare the new method with the standard least-squares tensor estimation and bootstrap-based uncertainty computation techniques. Our experiments show that when the number of measurements is small the deep learning method is more accurate and its uncertainty predictions are better calibrated than the standard methods. We show that the estimation uncertainties computed by the new method can highlight the model's biases, detect domain shift, and reflect the strength of noise in the measurements. Our study shows the importance and practical value of modeling prediction uncertainties in deep learning-based diffusion MRI analysis.