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MRI Cross-Modality Image-to-Image Translation.

Qianye Yang1, Nannan Li1,2, Zixu Zhao1

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This study introduces a deep learning framework for generating translated MR image modalities. This novel approach enhances medical image analysis tasks like registration and segmentation by effectively utilizing cross-modality information.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neuroscience

Background:

  • Magnetic Resonance Imaging (MRI) involves multiple modalities, each offering unique information.
  • Acquiring all desired MRI modalities for a patient can be time-consuming and costly.
  • Integrating information across different MRI modalities is crucial for comprehensive analysis.

Purpose of the Study:

  • To develop a deep learning framework for Image Modality Translation (IMT) in MRI.
  • To leverage cross-modality information for improved medical image analysis tasks.
  • To create an auxiliary tool with broad applications in medical fields.

Main Methods:

  • Utilized conditional generative adversarial networks (cGANs) for IMT.
  • Exploited both low-level (pixel-wise) and high-level (semantic) features across modalities.
  • Developed cross-modality registration by fusing deformation fields.
  • Introduced translated multichannel segmentation (TMS) using fully convolutional networks (FCNs).

Main Results:

  • The IMT framework successfully generated translated MRI modalities.
  • Cross-modality registration and MRI segmentation performance were significantly improved.
  • The proposed methods advanced the state-of-the-art on five brain MRI datasets.
  • No additional data was required to achieve performance gains.

Conclusions:

  • The proposed framework effectively translates MRI modalities, enabling cross-modality information fusion.
  • The developed methods for registration and segmentation demonstrate significant improvements.
  • This work offers a valuable auxiliary tool for various medical imaging applications.