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Image quality assessment using deep learning in high b-value diffusion-weighted breast MRI.

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Deep learning effectively identifies magnetic resonance imaging (MRI) artifacts in breast diffusion-weighted imaging (DWI) maximum intensity projections (MIPs). This automated approach can enhance quality assurance for breast MRI examinations.

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Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Healthcare
  • Biomedical Engineering

Background:

  • Diffusion-weighted imaging (DWI) is crucial for breast MRI, but artifacts can compromise image quality and diagnostic accuracy.
  • Maximum intensity projections (MIPs) derived from DWI are commonly used for evaluating breast lesions.
  • Identifying and mitigating MRI artifacts is essential for reliable breast cancer detection and monitoring.

Purpose of the Study:

  • To develop and evaluate a deep learning algorithm for automated detection of MRI artifacts on breast DWI-derived MIPs.
  • To assess the performance of the deep learning model in identifying artifacts in a large, real-world dataset.
  • To explore the potential of AI in improving quality assurance for breast MRI protocols.

Main Methods:

  • A retrospective study analyzed 1309 breast MRI examinations with DWI sequences (b-value = 1500 s/mm²).
  • Two-dimensional MIP images were generated, and breast regions of interest (ROIs) were cropped.
  • A DenseNet deep learning model was trained using fivefold cross-validation and tested on an independent dataset (n=350).

Main Results:

  • Artifacts were present in 37% of the analyzed breast MRI images.
  • The deep learning model achieved an area under the precision-recall curve of 0.921 on the test dataset.
  • The model demonstrated a high positive predictive value of 0.981 for artifact detection.

Conclusions:

  • Deep learning algorithms can accurately identify MRI artifacts in breast DWI-derived MIPs.
  • This automated method shows promise for enhancing quality assurance in breast MRI examinations.
  • AI-driven artifact detection could lead to more reliable DWI interpretations and improved patient care.