Feasibility of synthetic computed tomography images generated from magnetic resonance imaging scans using various deep learning methods in the planning of radiation therapy for prostate cancer

Gyu Sang Yoo, Huan Minh Luu, Heejung Kim, Won Park, Hongryull Pyo, Youngyih Han, Ju Young Park, Sung Hong Park

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

We aimed to evaluate and compare the qualities of synthetic computed tomography (sCT) generated by various deep-learning methods in volumetric modulated arc therapy (VMAT) planning for prostate cancer. Simulation computed tomography (CT) and T2-weighted simulation magnetic resonance image from 113 patients were used in the sCT generation by three deep-learning approaches: generative adversarial network (GAN), cycle-consistent GAN (CycGAN), and reference-guided CycGAN (RgGAN), a new model which performed further adjustment of sCTs generated by CycGAN with available paired images. VMAT plans on the original simulation CT images were recalculated on the sCTs and the dosimetric differences were evaluated. For soft tissue, a significant difference in the mean Hounsfield unites (HUs) was observed between the original CT images and only sCTs from GAN (p = 0.03). The mean relative dose differences for planning target volumes or organs at risk were within 2% among the sCTs from the three deep-learning approaches. The differences in dosimetric parameters for D98% and D95% from original CT were lowest in sCT from RgGAN. In conclusion, HU conservation for soft tissue was poorest for GAN. There was the trend that sCT generated from the RgGAN showed best performance in dosimetric conservation D98% and D95% than sCTs from other methodologies.

Original languageEnglish (US)
Article number40
JournalCancers
Volume14
Issue number1
DOIs
StatePublished - Jan 1 2022
Externally publishedYes

Keywords

  • Deep learning
  • Magnetic resonance imaging
  • Prostate neoplasm
  • Radiotherapy
  • Synthetic computed tomography

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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