  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.
