Keywords: Machine Learning/Artificial Intelligence, Machine Learning/Artificial Intelligence, 4D-MRI\Enhancement\Temporal-compensationIn this study, we proposed and evaluated a deep learning technique for refining four-dimensional magnetic resonance imaging (4D-MRI) in the post-processing stage. More specifically, we designed an implicit temporal-compensated adversarial network (ITAN) based on the intrinsic property of 4D-MRI to improve image quality with neighboring phases. It can overcome its inherent challenges of data deficiency, misalignment between training pairs, and complex texture details. The qualitative and quantitative results demonstrated that the proposed model can suppress the noise and artifacts in 4D-MR images, recover the missing details and perform better than a state-of-the-art method.
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