51 research outputs found
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Filtered Multitensor Tractography
We describe a technique that uses tractography to drive the local fiber model estimation. Existing techniques use independent estimation at each voxel so there is no running knowledge of confidence in the estimated model fit. We formulate fiber tracking as recursive estimation: at each step of tracing the fiber, the current estimate is guided by those previous. To do this we perform tractography within a filter framework and use a discrete mixture of Gaussian tensors to model the signal. Starting from a seed point, each fiber is traced to its termination using an unscented Kalman filter to simultaneously fit the local model to the signal and propagate in the most consistent direction. Despite the presence of noise and uncertainty, this provides a causal estimate of the local structure at each point along the fiber. Using two- and three-fiber models we demonstrate in synthetic experiments that this approach significantly improves the angular resolution at crossings and branchings. In vivo experiments confirm the ability to trace through regions known to contain such crossing and branching while providing inherent path regularization
Doctor of Philosophy
dissertationRecent developments in magnetic resonance imaging (MRI) provide an in vivo and noninvasive tool for studying the human brain. In particular, the detection of anisotropic diffusion in biological tissues provides the foundation for diffusion-weighted imaging (DWI), an MRI modality. This modality opens new opportunities for discoveries of the brain's structural connections. Clinically, DWI is often used to analyze white matter tracts to understand neuropsychiatric disorders and the connectivity of the central nervous system. However, due to imaging time required, DWI used in clinical studies has a low angular resolution. In this dissertation, we aim to accurately track and segment the white matter tracts and estimate more representative models from low angular DWI. We first present a novel geodesic approach to segmentation of white matter tracts from diffusion tensor imaging (DTI), estimated from DWI. Geodesic approaches treat the geometry of brain white matter as a manifold, often using the inverse tensor field as a Riemannian metric. The white matter pathways are then inferred from the resulting geodesics. A serious drawback of current geodesic methods is that geodesics tend to deviate from the major eigenvectors in high-curvature areas in order to achieve the shortest path. We propose a method for learning an adaptive Riemannian metric from the DTI data, where the resulting geodesics more closely follow the principal eigenvector of the diffusion tensors even in high-curvature regions. Using the computed geodesics, we develop an automatic way to compute binary segmentations of the white matter tracts. We demonstrate that our method is robust to noise and results in improved geodesics and segmentations. Then, based on binary segmentations, we present a novel Bayesian approach for fractional segmentation of white matter tracts and simultaneous estimation of a multitensor diffusion model. By incorporating a prior that assumes the tensor fields inside each tract are spatially correlated, we are able to reliably estimate multiple tensor compartments in fiber crossing regions, even with low angular diffusion-weighted imaging. This reduces the effects of partial voluming and achieves a more reliable analysis of diffusion measurements
TractCloud: Registration-free tractography parcellation with a novel local-global streamline point cloud representation
Diffusion MRI tractography parcellation classifies streamlines into
anatomical fiber tracts to enable quantification and visualization for clinical
and scientific applications. Current tractography parcellation methods rely
heavily on registration, but registration inaccuracies can affect parcellation
and the computational cost of registration is high for large-scale datasets.
Recently, deep-learning-based methods have been proposed for tractography
parcellation using various types of representations for streamlines. However,
these methods only focus on the information from a single streamline, ignoring
geometric relationships between the streamlines in the brain. We propose
TractCloud, a registration-free framework that performs whole-brain
tractography parcellation directly in individual subject space. We propose a
novel, learnable, local-global streamline representation that leverages
information from neighboring and whole-brain streamlines to describe the local
anatomy and global pose of the brain. We train our framework on a large-scale
labeled tractography dataset, which we augment by applying synthetic transforms
including rotation, scaling, and translations. We test our framework on five
independently acquired datasets across populations and health conditions.
TractCloud significantly outperforms several state-of-the-art methods on all
testing datasets. TractCloud achieves efficient and consistent whole-brain
white matter parcellation across the lifespan (from neonates to elderly
subjects, including brain tumor patients) without the need for registration.
The robustness and high inference speed of TractCloud make it suitable for
large-scale tractography data analysis. Our project page is available at
https://tractcloud.github.io/.Comment: MICCAI 202
Implications of Inconsistencies between fMRI and dMRI on Multimodal Connectivity Estimation
International audienceThere is a recent trend towards integrating resting state functional magnetic resonance imaging (RS-fMRI) and diffusion MRI (dMRI) for brain connectivity estimation, as motivated by how estimates from these modalities are presumably two views reflecting the same underlying brain circuitry. In this paper, we show on a cohort of 60 subjects that conventional functional connectivity (FC) estimates based on Pearson's correlation and anatomical connectivity (AC) estimates based on fiber counts are actually not that highly correlated for typical RS-fMRI (~7 min) and dMRI (~32 gradient directions) data. The FC-AC correlation can be significantly increased by considering sparse partial correlation and modeling fiber endpoint uncertainty, but the resulting FC-AC correlation is still rather low in absolute terms. We further exemplify the inconsistencies between FC and AC estimates by integrating them as priors into activation detection and demonstrating significant differences in their detection sensitivity. Importantly, we illustrate that these inconsistencies can be useful in fMRI-dMRI integration for improving brain connectivity estimation
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Two-Tensor Tractography Using a Constrained Filter
We describe a technique to simultaneously estimate a weighted, positive-definite multi-tensor fiber model and perform tractography. Existing techniques estimate the local fiber orientation at each voxel independently so there is no running knowledge of confidence in the estimated fiber model. We formulate fiber tracking as recursive estimation: at each step of tracing the fiber, the current estimate is guided by the previous. To do this we model the signal as a weighted mixture of Gaussian tensors and perform tractography within a filter framework. Starting from a seed point, each fiber is traced to its termination using an unscented Kalman filter to simultaneously fit the local model and propagate in the most consistent direction. Further, we modify the Kalman filter to enforce model constraints, i.e. positive eigenvalues and convex weights. Despite the presence of noise and uncertainty, this provides a causal estimate of the local structure at each point along the fiber. Synthetic experiments demonstrate that this approach significantly improves the angular resolution at crossings and branchings while consistently estimating the mixture weights. In vivo experiments confirm the ability to trace out fibers in areas known to contain such crossing and branching while providing inherent path regularization
Reconstructing the somatotopic organization of the corticospinal tract remains a challenge for modern tractography methods
The corticospinal tract (CST) is a critically important white matter fiber
tract in the human brain that enables control of voluntary movements of the
body. Diffusion MRI tractography is the only method that enables the study of
the anatomy and variability of the CST pathway in human health. In this work,
we explored the performance of six widely used tractography methods for
reconstructing the CST and its somatotopic organization. We perform experiments
using diffusion MRI data from the Human Connectome Project. Four quantitative
measurements including reconstruction rate, the WM-GM interface coverage,
anatomical distribution of streamlines, and correlation with cortical volumes
to assess the advantages and limitations of each method. Overall, we conclude
that while current tractography methods have made progress toward the
well-known challenge of improving the reconstruction of the lateral projections
of the CST, the overall problem of performing a comprehensive CST
reconstruction, including clinically important projections in the lateral (hand
and face area) and medial portions (leg area), remains an important challenge
for diffusion MRI tractography.Comment: 41 pages, 19 figure
Variability and anatomical specificity of the orbitofrontothalamic fibers of passage in the ventral capsule/ventral striatum (VC/VS): precision care for patient-specific tractography-guided targeting of deep brain stimulation (DBS) in obsessive compulsive disorder (OCD)
Deep Brain Stimulation (DBS) is a neurosurgical procedure that can reduce symptoms in medically intractable obsessive-compulsive disorder (OCD). Conceptually, DBS of the ventral capsule/ventral striatum (VC/VS) region targets reciprocal excitatory connections between the orbitofrontal cortex (OFC) and thalamus, decreasing abnormal reverberant activity within the OFC-caudate-pallidal-thalamic circuit. In this study, we investigated these connections using diffusion magnetic resonance imaging (dMRI) on human connectome datasets of twenty-nine healthy young-adult volunteers with two-tensor unscented Kalman filter based tractography. We studied the morphology of the lateral and medial orbitofrontothalamic connections and estimated their topographic variability within the VC/VS region. Our results showed that the morphology of the individual orbitofrontothalamic fibers of passage in the VC/VS region is complex and inter-individual variability in their topography is high. We applied this method to an example OCD patient case who underwent DBS surgery, formulating an initial proof of concept for a tractography-guided patient-specific approach in DBS for medically intractable OCD. This may improve on current surgical practice, which involves implanting all patients at identical stereotactic coordinates within the VC/VS region
Superficial White Matter Analysis: An Efficient Point-cloud-based Deep Learning Framework with Supervised Contrastive Learning for Consistent Tractography Parcellation across Populations and dMRI Acquisitions
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 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.Comment: 12 pages, 7 figures. Extension of our ISBI 2022 paper
(arXiv:2201.12528) (Best Paper Award Finalist
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Neural Tractography Using an Unscented Kalman Filter
We describe a technique to simultaneously estimate a local neural fiber model and trace out its path. Existing techniques estimate the local fiber orientation at each voxel independently so there is no running knowledge of confidence in the estimated fiber model. We formulate fiber tracking as recursive estimation: at each step of tracing the fiber, the current estimate is guided by the previous. To do this we model the signal as a mixture of Gaussian tensors and perform tractography within a filter framework. Starting from a seed point, each fiber is traced to its termination using an unscented Kalman filter to simultaneously fit the local model and propagate in the most consistent direction. Despite the presence of noise and uncertainty, this provides a causal estimate of the local structure at each point along the fiber. Synthetic experiments demonstrate that this approach reduces signal reconstruction error and significantly improves the angular resolution at crossings and branchings. In vivo experiments confirm the ability to trace out fibers in areas known to contain such crossing and branching while providing inherent path regularization
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