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Review
. 2021 Jan;22(1):11-36.
doi: 10.1002/acm2.13121. Epub 2020 Dec 11.

A review on medical imaging synthesis using deep learning and its clinical applications

Tonghe Wang  1   2 , Yang Lei  1 , Yabo Fu  1 , Jacob F Wynne  1 , Walter J Curran  1   2 , Tian Liu  1   2 , Xiaofeng Yang  1   2
Affiliations
Review

A review on medical imaging synthesis using deep learning and its clinical applications

Tonghe Wang et al. J Appl Clin Med Phys. 2021 Jan.

Abstract

This paper reviewed the deep learning-based studies for medical imaging synthesis and its clinical application. Specifically, we summarized the recent developments of deep learning-based methods in inter- and intra-modality image synthesis by listing and highlighting the proposed methods, study designs, and reported performances with related clinical applications on representative studies. The challenges among the reviewed studies were then summarized with discussion.

Keywords: CT; MRI; PET; deep learning; image synthesis; radiation therapy.

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Conflict of interest statement

None.

Figures

Fig. 1
Fig. 1
Number of peer‐reviewed articles in medical imaging synthesis using deep learning with different neural networks. This study only covers the first 2 months of 2020. The dashed line predicting the total number of articles in 2020 is a linear extrapolation based on previous years.
Fig. 2
Fig. 2
Pie chart of numbers of articles in different categories of neural networks.
Fig. 3
Fig. 3
Pie chart of numbers of articles in different categories of applications. MR‐to‐CT: RT, MR‐to‐CT: PET, and MR‐to‐CT: Registration represent MR to CT image synthesis used in radiotherapy, PET, and image registration, respectively. PET: AC and PET: Low‐count represent PET image synthesis used in attenuation correction and low‐count to full‐count, respectively.
See this image and copyright information in PMC

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