Physics-/Model-Based and Data-Driven Methods for Low-Dose Computed Tomography: A survey

• URL: http://arxiv.org/abs/2203.15725v2
• Date: Sat, 25 Mar 2023 02:49:57 GMT
• Title: Physics-/Model-Based and Data-Driven Methods for Low-Dose Computed Tomography: A survey
• Authors: Wenjun Xia, Hongming Shan, Ge Wang and Yi Zhang
• Abstract summary: In 2016, deep learning (DL) has advanced tomographic imaging with remarkable successes, especially in low-dose computed tomography (LDCT) imaging. Despite being driven by big data, the LDCT denoising and pure end-to-end reconstruction networks often suffer from the black box nature and major issues such as instabilities. An emerging trend is to integrate imaging physics and model into deep networks, enabling a hybridization of physics/model-based and data-driven elements.
• Score: 21.430431819394414
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Since 2016, deep learning (DL) has advanced tomographic imaging with remarkable successes, especially in low-dose computed tomography (LDCT) imaging. Despite being driven by big data, the LDCT denoising and pure end-to-end reconstruction networks often suffer from the black box nature and major issues such as instabilities, which is a major barrier to apply deep learning methods in low-dose CT applications. An emerging trend is to integrate imaging physics and model into deep networks, enabling a hybridization of physics/model-based and data-driven elements. %This type of hybrid methods has become increasingly influential. In this paper, we systematically review the physics/model-based data-driven methods for LDCT, summarize the loss functions and training strategies, evaluate the performance of different methods, and discuss relevant issues and future directions.

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