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Related Concept Videos

Computed Tomography01:10

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...

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Related Experiment Video

Updated: May 14, 2026

Measuring Connectivity in the Primary Visual Pathway in Human Albinism Using Diffusion Tensor Imaging and Tractography
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Measuring Connectivity in the Primary Visual Pathway in Human Albinism Using Diffusion Tensor Imaging and Tractography

Published on: August 11, 2016

Simultaneous ODF estimation and tractography in HARDI.

H Ertan Cetingul1, H Ertan Cetingül, Mariappan Nadar

  • 1Siemens Corporation, Corporate Research and Technology, Princeton, NJ 08540, USA. hasan.cetingul@siemens.com

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|February 1, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces an advanced unscented Kalman filter (UKF) for tracking white matter fibers in high angular resolution diffusion imaging (HARDI) data. The novel method improves fiber tracking accuracy by utilizing orientation distribution functions (ODFs), outperforming previous techniques.

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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
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Last Updated: May 14, 2026

Measuring Connectivity in the Primary Visual Pathway in Human Albinism Using Diffusion Tensor Imaging and Tractography
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Measuring Connectivity in the Primary Visual Pathway in Human Albinism Using Diffusion Tensor Imaging and Tractography

Published on: August 11, 2016

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
17:06

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

Area of Science:

  • Neuroimaging
  • Biomedical Engineering
  • Computational Neuroscience

Background:

  • Tracking white matter fibers is crucial for understanding brain connectivity.
  • Existing methods using unscented Kalman filters (UKF) rely on restrictive parametric models for diffusion.
  • High angular resolution diffusion imaging (HARDI) provides rich information about local fiber orientations.

Purpose of the Study:

  • To extend the unscented Kalman filter (UKF) for white matter tractography using orientation distribution functions (ODFs).
  • To overcome the limitations of parametric mixture models in HARDI-based fiber tracking.
  • To develop a more generic and accurate method for estimating local fiber orientation profiles.

Main Methods:

  • Utilizing the spherical harmonic representation of HARDI signals as the state in the UKF.
  • Enforcing non-negativity constraints on the orientation distribution functions (ODFs).
  • Performing tractography by identifying peak directions of the ODFs.

Main Results:

  • The proposed UKF-ODF method demonstrated superior performance compared to filtered two-tensor tractography in simulations.
  • Achieved a significant reduction in mean Chamfer error (0.05 to 0.27 voxels) across varying noise levels.
  • In vivo fiber tracking results showed consistency with known neuroanatomical pathways.

Conclusions:

  • The UKF extended with ODFs offers a more robust and accurate approach to HARDI tractography.
  • This method provides a more generalized framework for modeling water diffusion in the brain.
  • The findings support the potential of this technique for clinical neuroimaging and research.