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Magnetic Resonance Imaging01:24

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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...
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DeepDicomSort: An Automatic Sorting Algorithm for Brain Magnetic Resonance Imaging Data.

Sebastian R van der Voort1, Marion Smits2, Stefan Klein3

  • 1Biomedical Imaging Group Rotterdam, Departments of Radiology and Nuclear Medicine and Medical Informatics, Erasmus MC - University Medical Centre Rotterdam, Rotterdam, The Netherlands. s.vandervoort@erasmusmc.nl.

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|July 7, 2020
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Summary
This summary is machine-generated.

Automated data curation for brain MRI datasets is now possible. A convolutional neural network (CNN) accurately identifies eight brain MRI scan types, improving data organization and reusability.

Keywords:
BIDSBrain imagingDICOMData curationMachine learningMagnetic resonance imaging

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Area of Science:

  • Medical imaging
  • Artificial intelligence
  • Data science

Background:

  • Increasing dataset sizes in medical imaging research necessitate automated data curation.
  • Structured organization of datasets is crucial for data integrity and reusability.
  • Manual data organization is time-consuming and prone to errors.

Purpose of the Study:

  • To investigate the automation of brain MRI dataset organization.
  • To develop a method for automatically recognizing and sorting different brain MRI scan types.
  • To improve the efficiency and accuracy of medical imaging data curation.

Main Methods:

  • Designed a convolutional neural network (CNN) to recognize eight brain MRI scan types based on visual appearance.
  • Trained and tested the CNN on large datasets of brain MRI scans from subjects with brain tumors and Alzheimer's disease.
  • The method is independent of scan metadata, relying solely on image data.

Main Results:

  • The CNN achieved an overall accuracy of 98.7% in recognizing brain MRI scan types in the first experiment (brain tumor subjects).
  • The CNN achieved an overall accuracy of 98.5% in the second experiment (Alzheimer's subjects).
  • The method demonstrated high accuracy in classifying various scan types, including pre-contrast T1-weighted (T1w), post-contrast T1-weighted (T1wC), T2-weighted (T2w), and proton density-weighted (PDw) scans.

Conclusions:

  • The developed CNN method can accurately and automatically predict brain MRI scan types.
  • This automation significantly enhances the process of organizing large brain MRI datasets.
  • The method improves data shareability and integrity by reducing the need for manual verification.