Model-based Deep Medical Imaging: the roadmap of generalizing iterative reconstruction model using deep learning
This work addresses critical issues in medical imaging for clinical applications, but it appears incremental as it builds on existing deep learning and model-based methods.
The paper tackles the problem of slow imaging speed and radiation dose in medical imaging by proposing a general framework that combines reconstruction models with deep learning to accelerate MRI and reduce radiation in CT/PET, demonstrating performance through examples of unrolling algorithms.
Medical imaging is playing a more and more important role in clinics. However, there are several issues in different imaging modalities such as slow imaging speed in MRI, radiation injury in CT and PET. Therefore, accelerating MRI, reducing radiation dose in CT and PET have been ongoing research topics since their invention. Usually, acquiring less data is a direct but important strategy to address these issues. However, less acquisition usually results in aliasing artifacts in reconstructions. Recently, deep learning (DL) has been introduced in medical image reconstruction and shown potential on significantly speeding up MR reconstruction and reducing radiation dose. In this paper, we propose a general framework on combining the reconstruction model with deep learning to maximize the potential of deep learning and model-based reconstruction, and give the examples to demonstrate the performance and requirements of unrolling different algorithms using deep learning.