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Related Concept Videos

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...

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Multi-plane vision transformer for hemorrhage classification using axial and sagittal MRI data.

Badhan Kumar Das1,2, Gengyan Zhao3, Boris Mailhe3

  • 1Digital Technology and Innovation, Siemens Healthineers, Erlangen, Germany. badhankumar.das@siemens-healthineers.com.

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|March 19, 2026
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Summary

A new 3D multi-plane vision transformer (MP-ViT) effectively identifies brain hemorrhages in MRI scans with varying orientations. This approach improves detection accuracy by integrating information across different image planes, outperforming existing methods.

Keywords:
Cross attentionHemorrhage classificationVision transformer

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neuroscience

Background:

  • Identifying brain hemorrhages from magnetic resonance imaging (MRI) is crucial for clinical diagnosis.
  • Variations in MRI acquisition contrasts and orientations pose challenges for automated detection using neural networks.
  • Traditional methods like resampling can lead to information loss when handling varied image orientations.

Purpose of the Study:

  • To propose a novel 3D multi-plane vision transformer (MP-ViT) model for robust brain hemorrhage classification.
  • To address the challenge of varying MRI acquisition orientations without information loss.
  • To improve the accuracy of hemorrhage detection in diverse clinical MRI datasets.

Main Methods:

  • Developed a 3D multi-plane vision transformer (MP-ViT) incorporating separate encoders for axial and sagittal contrasts.
  • Utilized cross-attention mechanisms to integrate information across different image orientations.
  • Introduced a modality indication vector to supply missing contrast information to the model.
  • Validated the model on a large-scale clinical dataset (10,084 training, 1,289 validation, 1,496 test subjects).

Main Results:

  • MP-ViT demonstrated substantial improvements in the area under the curve (AUC) for hemorrhage classification.
  • The proposed MP-ViT outperformed standard vision transformer (ViT) by 5.5% in AUC.
  • MP-ViT achieved a 1.8% higher AUC compared to convolutional neural network (CNN)-based architectures.
  • The model showed significant potential in enhancing hemorrhage detection performance with varied orientation contrasts.

Conclusions:

  • The 3D multi-plane vision transformer (MP-ViT) offers a promising solution for brain hemorrhage detection in diverse MRI orientations.
  • Integrating multi-plane information and contrast data improves classification accuracy.
  • MP-ViT represents a significant advancement in automated medical image analysis for neurological conditions.