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
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 training and 10 for testing phases. We conducted deformable image registration and Nyul intensity normalization for MR images to maximize the similarity between MR and CT images as a preprocessing step. The processed images were plugged into a deep learning model, generative adversarial network...
April 12, 2024: Scientific Reports