Scientific Reports (Apr 2024)

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

  • 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

DOI
https://doi.org/10.1038/s41598-024-59014-6
Journal volume & issue
Vol. 14, no. 1
pp. 1 – 12

Abstract

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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 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. To prove clinical feasibility, we assessed the accuracy of synthetic CT images in image similarity using structural similarity (SSIM) and mean-absolute-error (MAE) and dosimetry similarity using gamma passing rate (GPR). Dose calculation was performed on the true and synthetic CT images with a commercial Monte Carlo algorithm. Synthetic CT images generated by deep learning outperformed MRCAT images in image similarity by 1.5% in SSIM, and 18.5 HU in MAE. In dosimetry, the DL-based synthetic CT images achieved 98.71% and 96.39% in the GPR at 1% and 1 mm criterion with 10% and 60% cut-off values of the prescription dose, which were 0.9% and 5.1% greater GPRs over MRCAT images.

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