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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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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.
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Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
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Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
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Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...
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Positron Emission Tomography (PET) is a medical imaging technique that provides crucial insights into the body's physiological functions at a molecular level. It is an indispensable resource for diagnosing, staging, and monitoring various illnesses, notably cancer, neurological disorders, and cardiovascular conditions.
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Unified voxel- and tensor-based morphometry (UVTBM) using registration confidence.

Ali R Khan1, Lei Wang2, Mirza Faisal Beg3

  • 1Department of Medical Biophysics, Robarts Research Institute, Western University, London, Ontario, Canada.

Neurobiology of Aging
|September 30, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a new unified method for brain morphometry, combining Voxel-Based Morphometry (VBM) and Tensor-Based Morphometry (TBM). The approach improves detection of group differences in neuroimaging studies.

Keywords:
AtlasesBrain registrationDiffeomorphismsMRIMorphometryTensor-based morphometryVoxel-based morphometry

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Area of Science:

  • Neuroimaging
  • Computational Anatomy
  • Medical Image Analysis

Background:

  • Voxel-based morphometry (VBM) and tensor-based morphometry (TBM) are neuroimaging techniques used to analyze brain structure.
  • These methods have different registration accuracy requirements and performance variations across brain regions.
  • Current methods may introduce errors when combining VBM and TBM due to varying registration accuracy.

Purpose of the Study:

  • To develop a unified approach combining VBM and TBM.
  • To leverage the advantages of both morphometry methods by using local registration confidence estimates.
  • To improve the detection and description of group differences in brain structure.

Main Methods:

  • A novel unified morphometry approach weighting VBM and TBM components based on local registration confidence.
  • Application of the method to healthy and mild Alzheimer's disease subjects (N=150) for age and group difference analysis.
  • Differential diagnosis investigation using Alzheimer's disease (N=34), frontotemporal dementia (N=30), and control (N=14) cohorts.

Main Results:

  • The proposed unified approach detected more descriptive group differences compared to VBM, Jacobian-modulated VBM, and TBM alone.
  • Demonstrated effectiveness in analyzing age and group differences in a cohort of healthy and mild Alzheimer's subjects.
  • Showcased potential for differential diagnosis between neurodegenerative diseases and controls.

Conclusions:

  • The unified morphometry framework effectively integrates VBM and TBM, overcoming individual method limitations.
  • This approach enhances the sensitivity and descriptiveness of neuroimaging analyses for detecting brain structure alterations.
  • The method holds promise for improved diagnostic capabilities in neurological and psychiatric disorders.