A comprehensive review on MRI to CT and MRI to PET image synthesis using deep learning Online publication date: Mon, 30-Oct-2023
by M.S. Meharban; M.K. Sabu; T. Santhanakrishnan
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 43, No. 3, 2023
Abstract: Image synthesis is the process of generating a synthetic image with desired qualities. Although CT and PET images are suffering from ionising radiation, MRI images are free from such radiation. Due to this fact, we need a system to generate synthetic CT and PET images from MRI images. The system will be helpful to avoid such ionising radiation from CT and PET and makes a better patient treatment workflow. This work reviewed various deep learning synthetic CT and synthetic PET generation methods. More than 75 papers were selected from PubMed and ScienceDirect databases from 2017 to 2021. Recently, CycleGAN variants have produced better results with no need for paired data. However, an effective evaluation measure was not available to evaluate the efficacy of the proposed works. Additional blind tests involving radiologists are required to evaluate the visual quality of the synthesised image.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Biomedical Engineering and Technology (IJBET):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook