JOURNAL ARTICLE

Rapid unpaired CBCT‐based synthetic CT for CBCT‐guided adaptive radiotherapy

Abstract

Abstract In this work, we demonstrate a method for rapid synthesis of high‐quality CT images from unpaired, low‐quality CBCT images, permitting CBCT‐based adaptive radiotherapy. We adapt contrastive unpaired translation (CUT) to be used with medical images and evaluate the results on an institutional pelvic CT dataset. We compare the method against cycleGAN using mean absolute error, structural similarity index, root mean squared error, and Frèchet Inception Distance and show that CUT significantly outperforms cycleGAN while requiring less time and fewer resources. The investigated method improves the feasibility of online adaptive radiotherapy over the present state‐of‐the‐art.

Keywords:
Computer science Similarity (geometry) Radiation therapy Mean squared error Translation (biology) Similarity measure Artificial intelligence Medicine Image (mathematics) Radiology Mathematics Statistics

Metrics

7
Cited By
1.27
FWCI (Field Weighted Citation Impact)
30
Refs
0.76
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Advanced Image Processing Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Generative Adversarial Networks and Image Synthesis
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Medical Image Segmentation Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
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