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SAM-guided structural consistency constraints for unsupervised MR-to-CT synthesis.

Jinlong Zhang1, Yiwen Zhang1, Xinqi Zhang1

  • 1School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou, 510515, China.

Applied Radiation and Isotopes : Including Data, Instrumentation and Methods for Use in Agriculture, Industry and Medicine
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PubMed
Summary
This summary is machine-generated.

This study introduces a new method for synthesizing CT images from MRI scans, improving anatomical accuracy for MRI-only radiotherapy. The Segment Anything Model (SAM) integration ensures better alignment, crucial for precise radiation treatment.

Keywords:
Prompt augmentationRadiotherapySAM-Guided structural consistencyUnsupervised MR-to-CT synthesis

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Area of Science:

  • Medical Imaging
  • Radiotherapy Physics
  • Artificial Intelligence in Medicine

Background:

  • MRI-only radiotherapy requires accurate CT synthesis for treatment planning.
  • Unsupervised MR-to-CT synthesis methods often lack structural consistency, causing anatomical misalignments.
  • Existing methods struggle with precise anatomical localization, impacting radiotherapy efficacy.

Purpose of the Study:

  • To develop an unsupervised MR-to-CT synthesis method with enhanced structural consistency.
  • To leverage the Segment Anything Model (SAM) for improved anatomical alignment between MR and synthesized CT images.
  • To validate the proposed method's performance in synthesizing CT images for nasopharyngeal carcinoma radiotherapy.

Main Methods:

  • Integration of the Segment Anything Model (SAM) into unsupervised MR-to-CT synthesis.
  • Implementation of SAM-guided structural consistency constraints for anatomical alignment.
  • Utilizing prompt augmentation during training to improve generalization across anatomical structures.

Main Results:

  • The proposed SAM-guided method achieved superior performance on a nasopharyngeal carcinoma dataset.
  • Quantitative metrics included Mean Absolute Error (MAE) of 108.12 HU, Peak Signal-to-Noise Ratio (PSNR) of 24.24 dB, and Structural Similarity Index (SSIM) of 0.753.
  • Exceptional anatomical consistency in synthesized CT images was confirmed through quantitative analysis and dose distribution assessments.

Conclusions:

  • The SAM-guided approach significantly improves anatomical consistency in synthesized CT images.
  • This method holds potential for enhancing anatomical localization accuracy in MRI-only radiotherapy.
  • The findings support the advancement of MRI-only radiotherapy workflows through improved image synthesis techniques.