A geometry-based particle filtering approach to white matter tractography.

Med Image Comput Comput Assist Interv

Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.

Published: November 2010

We introduce a fibre tractography framework based on a particle filter which estimates a local geometrical model of the underlying white matter tract, formulated as a 'streamline flow' using generalized helicoids. The method is not dependent on the diffusion model, and is applicable to diffusion tensor (DT) data as well as to high angular resolution reconstructions. The geometrical model allows for a robust inference of local tract geometry, which, in the context of the causal filter estimation, guides tractography through regions with partial volume effects. We validate the method on synthetic data and present results on two types in vivo data: diffusion tensors and a spherical harmonic reconstruction of the fibre orientation distribution function (fODF).

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3081616PMC
http://dx.doi.org/10.1007/978-3-642-15745-5_29DOI Listing

Publication Analysis

Top Keywords

white matter
8
geometrical model
8
geometry-based particle
4
particle filtering
4
filtering approach
4
approach white
4
matter tractography
4
tractography introduce
4
introduce fibre
4
fibre tractography
4

Similar Publications

Microstructural white matter injury contributes to cognitive decline: Besides amyloid and tau.

J Prev Alzheimers Dis

February 2025

Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, PR China. Electronic address:

Background: Cognitive decline and the progression to Alzheimer's disease (AD) are traditionally associated with amyloid-beta (Aβ) and tau pathologies. This study aims to evaluate the relationships between microstructural white matter injury, cognitive decline and AD core biomarkers.

Methods: We conducted a longitudinal study of 566 participants using peak width of skeletonized mean diffusivity (PSMD) to quantify microstructural white matter injury.

View Article and Find Full Text PDF

Background: The associations of early-onset coronary heart disease (CHD) and genetic susceptibility with incident dementia and brain white matter hyperintensity (WMH) remain unclear. Elucidation of this problem could promote understanding of the neurocognitive impact of early-onset CHD and provide suggestions for the prevention of dementia.

Objectives: This study aimed to investigate whether observed and genetically predicted early-onset CHD were related to subsequent dementia and WMH volume.

View Article and Find Full Text PDF

Background: Cardiovascular risk factors (CRFs) like hypertension, high cholesterol, and diabetes mellitus are increasingly linked to cognitive decline and dementia, especially in cerebral small vessel disease (cSVD). White matter hyperintensities (WMH) are closely associated with cognitive impairment, but the mechanisms behind their development remain unclear. Blood-brain barrier (BBB) dysfunction may be a key factor, particularly in cSVD.

View Article and Find Full Text PDF

Alzheimer's disease (AD) is the most common type of dementia and one of the leading causes of death in elderly patients. The number of patients with AD in the United States is projected to double by 2060. Thus, understanding modifiable risk factors for AD is an urgent public health priority.

View Article and Find Full Text PDF

Our objectives were to use a quantitative literature review to explore dietary and feed factors influencing apparent total-tract digestibility of dry matter (DMD), crude protein (CPD), neutral detergent fiber (NDFD), ether extract (EED), non-structural carbohydrates (NSCD), non-fiber carbohydrates (NFCD), and residual organic matter (rOMD) in equine diets, and to assess their contributions to digestible energy (DE) supplies. Data from 54 studies were modeled using linear mixed-effect regressions, with publication as a random effect to account for study variability. For each nutrient, five models were derived with explanatory variables including: dry matter intake (DMI; % BW/day) and DM (% as-fed), and dietary components (CP, organic matter, EE, NDF, acid detergent fiber, NSC, starch, and NFC as % of DM), and feed types (forage, non-forage fiber, legumes, cereal, and oil proportions).

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!