Enhancing Cross-Modality Synthesis: Subvolume Merging for MRI-to-CT Conversion
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Abstract (arXiv)
Providing more precise tissue attenuation information, synthetic computed tomography (sCT) generated from magnetic resonance imaging (MRI) contributes to improved radiation therapy treatment planning. In our study, we employ the advanced SwinUNETR framework for synthesizing CT from MRI images. Additionally, we introduce a three-dimensional subvolume merging technique in the prediction process. By selecting an optimal overlap percentage for adjacent subvolumes, stitching artifacts are effectively mitigated, leading to a decrease in the mean absolute error (MAE) between sCT and the labels from 52.65 HU to 47.75 HU. Furthermore, implementing a weight function with a gamma value of 0.9 results in the lowest MAE within the same overlap area. By setting the overlap percentage between 50% and 70%, we achieve a balance between image quality and computational efficiency.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 4 of its 14 references that are in the map. First posted on arXiv 9 Sep 2024.
Builds on
4 of 14 references.
References · 4
- 2022Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Imagescited by 830
- 2022MONAI: An open-source framework for deep learning in healthcarecited by 448
- 2023SynthRAD2023 Grand Challenge dataset: generating synthetic CT for radiotherapycited by 53
- 2019Generation of abdominal synthetic CTs from 0.35T MR images using generative adversarial networks for MR-only liver radiotherapycited by 2
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Citing papers per year: 2026: 1.
1 paper.