Fuzzy Fibers: Uncertainty in dMRI Tractography

Fiber tracking based on diffusion weighted Magnetic Resonance Imaging (dMRI) allows for noninvasive reconstruction of fiber bundles in the human brain. In this chapter, we discuss sources of error and uncertainty in this technique, and review strategies that afford a more reliable interpretation of the results. This includes methods for computing and rendering probabilistic tractograms, which estimate precision in the face of measurement noise and artifacts. However, we also address aspects that have received less attention so far, such as model selection, partial voluming, and the impact of parameters, both in preprocessing and in fiber tracking itself. We conclude by giving impulses for future research

Improving Fiber Alignment in HARDI by Combining Contextual PDE Flow with Constrained Spherical Deconvolution

We propose two strategies to improve the quality of tractography results computed from diffusion weighted magnetic resonance imaging (DW-MRI) data. Both methods are based on the same PDE framework, defined in the coupled space of positions and orientations, associated with a stochastic process describing the enhancement of elongated structures while preserving crossing structures. In the first method we use the enhancement PDE for contextual regularization of a fiber orientation distribution (FOD) that is obtained on individual voxels from high angular resolution diffusion imaging (HARDI) data via constrained spherical deconvolution (CSD). Thereby we improve the FOD as input for subsequent tractography. Secondly, we introduce the fiber to bundle coherence (FBC), a measure for quantification of fiber alignment. The FBC is computed from a tractography result using the same PDE framework and provides a criterion for removing the spurious fibers. We validate the proposed combination of CSD and enhancement on phantom data and on human data, acquired with different scanning protocols. On the phantom data we find that PDE enhancements improve both local metrics and global metrics of tractography results, compared to CSD without enhancements. On the human data we show that the enhancements allow for a better reconstruction of crossing fiber bundles and they reduce the variability of the tractography output with respect to the acquisition parameters. Finally, we show that both the enhancement of the FODs and the use of the FBC measure on the tractography improve the stability with respect to different stochastic realizations of probabilistic tractography. This is shown in a clinical application: the reconstruction of the optic radiation for epilepsy surgery planning

Evaluating the Reliability of Human Brain White Matter Tractometry

Published Nov 17, 2021The validity of research results depends on the reliability of analysis methods. In recent years, there have been concerns about the validity of research that uses diffusion-weighted MRI (dMRI) to understand human brain white matter connections in vivo, in part based on the reliability of analysis methods used in this field. We defined and assessed three dimensions of reliability in dMRI-based tractometry, an analysis technique that assesses the physical properties of white matter pathways: (1) reproducibility, (2) test-retest reliability, and (3) robustness. To facilitate reproducibility, we provide software that automates tractometry (https://yeatmanlab.github.io/pyAFQ). In measurements from the Human Connectome Project, as well as clinical-grade measurements, we find that tractometry has high test-retest reliability that is comparable to most standardized clinical assessment tools. We find that tractometry is also robust: showing high reliability with different choices of analysis algorithms. Taken together, our results suggest that tractometry is a reliable approach to analysis of white matter connections. The overall approach taken here both demonstrates the specific trustworthiness of tractometry analysis and outlines what researchers can do to establish the reliability of computational analysis pipelines in neuroimaging.This work was supported through grant 1RF1MH121868- 01 from the National Institute of Mental Health/the BRAIN Initiative, through grant 5R01EB027585-02 to Eleftherios Garyfallidis (Indiana University) from the National Institute of Biomedical Imaging and Bioengineering, through Azure Cloud Computing Credits for Research & Teaching provided through the University of Washington’s Research Computing unit and the University of Washington eScience Institute, and NICHD R21HD092771 to Jason D. Yeatma

Reconstruction par tractographie des fibres de la matière blanche chez l'adulte sain ou souffrant de lésions neurologiques

Le cerveau est l'un des organes les plus complexes et les plus méconnus du corps humain. Grâce à l'imagerie par résonance magnétique (IRM) et plus précisément l'imagerie de diffusion, il est maintenant possible de reconstruire la connectivité de la matière blanche. Avec le temps ou la maladie, le cerveau peut subir des altérations pouvant modifier la connectivité de la matière blanche. Il est important de prendre en compte ces altérations pour pouvoir effectuer des analyses précises des connexions cérébrales.\\ La majorité des algorithmes utilisés dans le domaine de l'imagerie cérébrale sont développés avec des images provenant de sujets jeunes et sains. Cependant, la réalité de la recherche appliquée et de la clinique est tout autre. Les outils utilisés doivent donc être modulaires, que ce soit pour le traitement d'un sujet sain, âgé ou souffrant d'une pathologie.\\ Premièrement, cette thèse présente une mise en contexte. Ensuite, cette thèse s'intéresse au développement de méthodes pour la tractographie en milieu pratique et d'outils automatisés de traitement de l'IRM de diffusion (IRMd). Le guide sur la tractographie en milieu pratique est un chapitre de livre qui a pour but de former et conseiller les chercheurs cliniciens pour l'obtention d'un tractogramme répondant à leurs besoins. Les outils développés dans cette thèse sont composés d'un algorithme de traitement de l'IRMd automatisé appelé TractoFlow, ainsi que d'un outil de segmentation robuste aux lésions de matière blanche liées au vieillissement appelé DORIS. TractoFlow permet d'obtenir un tractogramme à partir des images d'IRMd brute facilement, rapidement et de manière reproductible. Notre second algorithme, DORIS, permet d'obtenir une segmentation des tissus cérébraux en 10 classes à partir des mesures de l'IRMd tout en améliorant la qualité de la tractographie anatomiquement contrainte. En guise de discussion, cette thèse présente deux projets futurs: DORIS adapté aux lésions et la tractographie adaptative au tissu sous-jacent. DORIS adapté aux lésions à pour but d'ajouter une 11ème classe afin de segmenter les lésions liées à la sclérose en plaque. Ensuite la tractographie adaptive présente une nouvelle manière de reconstruire les fibres de matière blanche en adaptant les paramètre de reconstruction suivant le tissu traversé. Cette thèse vise donc à remplir 2 objectifs: le premier est de pouvoir traiter et analyser la connectivité cérébrale chez des sujets jeunes, des sujets âgés ou souffrant d'une pathologie, le second est de répondre aux besoins du milieu clinique et de la recherche appliquée en étant simple et modulaire. Finalement, cette thèse conclue en présentant l'impact des différents outils sur la communauté et en discutant de ma vision du futur de l'IRMd et de la tractographie.\

Zifazah: A Scientific Visualization Language for Tensor Field Visualizations

This thesis presents the design and prototype implementation of a scientific visualization language called Zifazah for composing and exploring 3D visualizations of diffusion tensor magnetic resonance imaging (DT-MRI or DTI) data. Unlike existing tools allowing flexible customization of data visualizations that are programmer-oriented, Zifazah focuses on domain scientists as end users in order to enable them to freely compose visualizations of their scientific data set. Verbal descriptions of end users about how they would build and explore DTI visualizations are analyzed to collect syntax, semantics, and control structures of the language. Zifazah makes use of the initial set of lexical terms and semantical patterns to provide a declarative language in the spirit of intuitive syntax and usage. Along with sample scripts representative of the main language design features, some new DTI visualizations created by end users using the novel language have also been presented

On connectivity in the central nervous systeem : a magnetic resonance imaging study

Brain function has long been the realm of philosophy, psychology and psychiatry and since the mid 1800s, of histopathology. Through the advent of magnetic imaging in the end of the last century, an in vivo visualization of the human brain became available. This thesis describes the development of two unique techniques, imaging of diffusion of water protons and manganese enhanced imaging, that both allow for the depiction of white matter tracts. The reported studies show, that these techniques can be used for a three-dimensional depiction of fiber bundles and that quantitative measures reflecting fiber integrity and neuronal function can be extracted from such data. In clinical applications, the potential use of the developed methods is illustrated in human gliomas, as measure for fiber infiltration, and in spinal cord injury, to monitor potential neuroprotective and __regenerative medication.UBL - phd migration 201