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Related Concept Videos

Brain Imaging01:14

Brain Imaging

238
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...
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Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

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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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Related Experiment Video

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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
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The R package for DICOM to brain imaging data structure conversion.

Niklas Wulms1, Sven Eppe2, Mahboobeh Dehghan-Nayyeri3,4

  • 1Institute of Epidemiology and Social Medicine, University of Muenster, Muenster, Germany. wulms@uni-muenster.de.

Scientific Data
|October 4, 2023
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Summary

BIDSconvertR is a new R package that organizes magnetic resonance imaging (MRI) data using the Brain Imaging Data Structure (BIDS) standard. This tool simplifies data conversion, validation, and management for improved neuroimaging research reproducibility.

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Area of Science:

  • Neuroimaging
  • Data Science
  • Bioinformatics

Background:

  • Magnetic Resonance Imaging (MRI) research generates large, complex datasets.
  • Standardizing neuroimaging data is crucial for reproducibility and data sharing.
  • The Brain Imaging Data Structure (BIDS) is a growing standard for organizing such data.

Purpose of the Study:

  • To introduce BIDSconvertR, an R package for organizing MRI data.
  • To facilitate the conversion of DICOM and NIfTI files to the BIDS format.
  • To enhance MRI data management and validation within the BIDS framework.

Main Methods:

  • Development of an R-based package, BIDSconvertR.
  • Implementation of DICOM to NIfTI and NIfTI to BIDS conversion functionalities.
  • Integration of BIDS Validator and an MRI data viewer with a graphical user interface.

Main Results:

  • BIDSconvertR provides an interactive and user-friendly interface for MRI data organization.
  • The package supports efficient data conversion, validation, and management.
  • Features like color-coded BIDS sequence-IDs and regular expression support simplify data cleaning and validation.

Conclusions:

  • BIDSconvertR is the first R-based tool to support MRI data organization according to the BIDS specification.
  • The package promotes data standardization, transparency, and reproducibility in neuroimaging research.
  • It empowers researchers to efficiently manage and share their MRI datasets.