Clinical feasibility of deep learning-based synthetic CT images from T2-weighted MR images for cervical cancer patients compared to MRCAT

Clinical feasibility of deep learning-based synthetic CT images from T2-weighted MR images for cervical cancer patients compared to MRCAT

Abstract This work aims to investigate the clinical feasibility of deep learning-based synthetic CT images for cervix cancer, comparing them to MR for calculating attenuation (MRCAT). Patient cohort with 50 pairs of T2-weighted MR and CT images from cervical cancer patients was split into 40 for tra...

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Bibliographic Details
Main Authors: Hojin Kim, Sang Kyun Yoo, Jin Sung Kim, Yong Tae Kim, Jai Wo Lee, Changhwan Kim, Chae-Seon Hong, Ho Lee, Min Cheol Han, Dong Wook Kim, Se Young Kim, Tae Min Kim, Woo Hyoung Kim, Jayoung Kong, Yong Bae Kim
Format: Article
Language:English
Published: Nature Portfolio 2024-04-01
Series:Scientific Reports
Subjects:
Synthetic CT images
MR images
Deep learning
MRCAT
Cervical cancer
Online Access:https://doi.org/10.1038/s41598-024-59014-6

Internet

https://doi.org/10.1038/s41598-024-59014-6

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