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Medical imaging is playing a more and more important role in clinics.However, there are several issues in different imaging modalities such as slowimaging speed in MRI, radiation injury in CT and PET. Therefore, acceleratingMRI, reducing radiation dose in CT and PET have been ongoing research topicssince their invention. Usually, acquiring less data is a direct but importantstrategy to address these issues. However, less acquisition usually results inaliasing artifacts in reconstructions. Recently, deep learning (DL) has beenintroduced in medical image reconstruction and shown potential on significantlyspeeding up MR reconstruction and reducing radiation dose. In this paper, wepropose a general framework on combining the reconstruction model with deeplearning to maximize the potential of deep learning and model-basedreconstruction, and give the examples to demonstrate the performance andrequirements of unrolling different algorithms using deep learning.

Model-based Deep Medical Imaging: the roadmap of generalizing iterative reconstruction model using deep learning