  Image segmentation, real-value prediction, and cross-modal translation are
critical challenges in medical imaging. In this study, we propose a versatile
multi-task neural network framework, based on an enhanced Transformer U-Net
architecture, capable of simultaneously, selectively, and adaptively addressing
these medical image tasks. Validation is performed on a public repository of
human brain MR and CT images. We decompose the traditional problem of
synthesizing CT images into distinct subtasks, which include skull
segmentation, Hounsfield unit (HU) value prediction, and image sequential
reconstruction. To enhance the framework's versatility in handling multi-modal
data, we expand the model with multiple image channels. Comparisons between
synthesized CT images derived from T1-weighted and T2-Flair images were
conducted, evaluating the model's capability to integrate multi-modal
information from both morphological and pixel value perspectives.
