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- W3037442078 abstract "Recently, interest in MR-only treatment planning using synthetic CTs (synCTs) has grown rapidly in radiation therapy. However, developing class solutions for medical images that contain atypical anatomy remains a major limitation. In this paper, we propose a novel spatial attention-guided generative adversarial network (attention-GAN) model to generate accurate synCTs using T1-weighted MRI images as the input to address atypical anatomy. Experimental results on fifteen brain cancer patients show that attention-GAN outperformed existing synCT models and achieved an average MAE of 85.22$pm$12.08, 232.41$pm$60.86, 246.38$pm$42.67 Hounsfield units between synCT and CT-SIM across the entire head, bone and air regions, respectively. Qualitative analysis shows that attention-GAN has the ability to use spatially focused areas to better handle outliers, areas with complex anatomy or post-surgical regions, and thus offer strong potential for supporting near real-time MR-only treatment planning." @default.
- W3037442078 created "2020-07-02" @default.
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- W3037442078 date "2020-06-27" @default.
- W3037442078 modified "2023-09-27" @default.
- W3037442078 title "Attention-Guided Generative Adversarial Network to Address Atypical Anatomy in Modality Transfer." @default.
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