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Brain MRI artefact detection and correction using convolutional neural networks.

Ilkay Oksuz1

  • 1Computer Engineering Department, Istanbul Technical University, Istanbul, Turkey; School of Biomedical Engineering & Imaging Sciences, King's College London, U.K.

Computer Methods and Programs in Biomedicine
|December 29, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning method using dense convolutional neural networks and residual U-net to detect and correct motion artifacts in brain MRI scans, improving image and stroke segmentation quality.

Keywords:
Artefact detectionBrain MRIConvolutional neural networksStroke segmentation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neuroscience

Background:

  • Brain MRI is crucial for diagnosing neurodegenerative diseases.
  • Image artifacts significantly degrade diagnostic quality and hinder analysis.
  • Low image quality impacts the reliability of automated segmentation tasks.

Purpose of the Study:

  • To develop and evaluate a deep learning pipeline for detecting and correcting motion artifacts in brain MRI.
  • To enhance diagnostic image quality and improve the accuracy of stroke segmentation.

Main Methods:

  • Utilized dense convolutional neural networks (CNNs) for artifact detection.
  • Employed a residual U-net architecture for artifact correction.
  • Generated synthetic motion artifacts using MR physics-based methods for training.

Main Results:

  • The proposed pipeline achieved high accuracy (97.8%) in detecting brain MRI artifacts.
  • Demonstrated significant improvements in both image quality and stroke segmentation accuracy.
  • Validated on a dataset of 28 brain MRI stroke segmentation cases.

Conclusions:

  • Jointly ensuring high image and segmentation quality enhances automated analysis pipelines.
  • The developed method effectively reduces the impact of low image quality on prognosis.
  • Highlights the performance of deep learning for brain MRI stroke segmentation.