Abstract #2787

Synthetic CT Generation using MRI with Deep Learning: How does the selection of input images affect the resulting synthetic CT?

Andrew Palmera Leynes^1,2 and Peder Eric Zufall Larson^1,2

¹Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States, ²UC Berkeley - UC San Francisco Joint Graduate Program in Bioengineering, Berkeley and San Francisco, CA, United States

Most recently, synthetic CT generation methods have been utilizing deep learning. One major open question with this approach is that it is not clear what MRI images would produce the best synthetic CT images. We investigated how the selection of MRI inputs affect the resulting output using a fixed network. We found that Dixon MRI may be sufficient for quantitatively accurate synthetic CT images and ZTE MRI may provide additional information to capture bowel air distributions.

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