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Abstract #3921

Exploration of vision transformer models in medical images synthesis

Weijie Chen1, Seyed Iman Zare Estakhraji2, and Alan B McMillan3
1Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI, United States, 2Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States, 3Department of Biomedical Engineering, Department of Radiology and Medical Physics, Madison, WI, United States


Applications such as PET/MR and MR-only Radiotherapy Planning need the capability to derive a CT-like image from MRI inputs to enable accurate attenuation correction and dose estimation. More recently, transformer models have been proposed for computer vision applications. Models in the transformer family discard traditional convolution-based network structures and emphasize the importance of non-local information yielding potentially more realistic outputs. To evaluate the performance of SwinIR, TransUet, and Unet. After comparing results visually and quantitatively, the SwinIR models and TransUnet models appear to provide higher-quality synthetic CT scans compared to the conventional Unet.

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