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Related Concept Videos

Brain Imaging01:14

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).
Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
Assessment of Diffusion and Perfusion01:17

Assessment of Diffusion and Perfusion

Understanding and evaluating diffusion and perfusion is critical in assessing a patient's respiratory and circulatory health. These processes play key roles in maintaining the body's internal environment, ensuring that tissues receive adequate oxygen while waste products are efficiently removed.
The Role of Diffusion in Respiration
Diffusion is the process by which molecules move from an area of higher concentration to an area of lower concentration. In the respiratory system, this principle...

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Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging
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Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Published on: September 12, 2011

Quantitative evaluation of brain development using anatomical MRI and diffusion tensor imaging.

Kenichi Oishi1, Andreia V Faria, Shoko Yoshida

  • 1The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.

International Journal of Developmental Neuroscience : the Official Journal of the International Society for Developmental Neuroscience
|June 26, 2013
PubMed
Summary
This summary is machine-generated.

Brain development varies by structure and age. This study introduces an automated method for quantitative magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) analysis to create growth charts for pediatric brain development.

Keywords:
Brain atlasDiffusion tensor imagingMagnetic resonance imagingNeonateNormalizationPediatricQuantification

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Published on: November 8, 2012

Area of Science:

  • Neuroscience
  • Medical Imaging
  • Developmental Biology

Background:

  • Brain development is structure-specific, with varying growth rates by age.
  • Clinical magnetic resonance imaging (MRI) is primarily qualitative, lacking quantitative data for monitoring development.
  • Quantitative imaging is crucial for detecting subtle abnormalities linked to developmental disorders.

Purpose of the Study:

  • To develop an automated structure parcellation method for precise quantitative MRI and DTI analysis in neonates and children.
  • To create MRI- and DTI-based growth percentile charts for pediatric brain development.
  • To apply these charts to investigate developmental abnormalities in conditions like cerebral palsy, Williams syndrome, and Rett syndrome.

Main Methods:

  • Development of an automated structure parcellation technique tailored for neonatal and pediatric populations.
  • Quantification of multiple MRI modalities within a unified analytical framework.
  • Creation of growth percentile charts using standardized anatomical boundaries.

Main Results:

  • An automated method was developed to overcome challenges in standardizing anatomical boundaries for quantitative MRI and DTI.
  • The study demonstrates the creation of MRI- and DTI-based growth percentile charts.
  • These charts were applied to analyze developmental abnormalities in specific pediatric neurological conditions.

Conclusions:

  • Automated parcellation enhances precision in quantitative brain imaging by reducing inter-reader variability.
  • Growth percentile charts derived from MRI and DTI are valuable tools for assessing pediatric brain development.
  • Future work will focus on multimodal analysis and personalized clinical applications.