Fan F, Qiu J, Huang Y, Maier A (2024)
Publication Language: English
Publication Type: Conference contribution
Publication year: 2024
Pages Range: 94-97
Event location: Bamberg, Germany
URI: https://arxiv.org/pdf/2409.05982
DOI: 10.48550/arXiv.2409.05982
Open Access Link: https://arxiv.org/pdf/2409.05982
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, \modified{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.
APA:
Fan, F., Qiu, J., Huang, Y., & Maier, A. (2024). Enhancing Cross-Modality Synthesis: Subvolume Merging for MRI-to-CT Conversion. In Proceedings of the The 8th International Conference on Image Formation in X-Ray Computed Tomography (pp. 94-97). Bamberg, Germany.
MLA:
Fan, Fuxin, et al. "Enhancing Cross-Modality Synthesis: Subvolume Merging for MRI-to-CT Conversion." Proceedings of the The 8th International Conference on Image Formation in X-Ray Computed Tomography, Bamberg, Germany 2024. 94-97.
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