Synthesizing CT Image from Single-echo UTE-MRI using Multi-Task Framework

This abstract proposed a cross-modality conversion method from UTE MRI to CT images. Using the TABS and ResidualAttentionU-net model in a processing framework combining image segmentation and prediction, the skull structure can be extracted from UTE MRI with high similarity of CT based skull. Five UTE-CT image pairs of mouse brains were used in the study. And a 3D-patch based training strategy was adopted, which took the advantage of structural continuity between slices in very limited datasets. The results show that the proposed combined image segmentation and prediction framework can achieve higher accuracy in medical image synthesizing for cross-modality conversion.