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Article

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

1
Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea
2
Department of Bio and Brain Engineering, Korean Advanced Institute of Science and Technology, Daejeon 34141, Korea
3
Department of Radiation Oncology, Samsung Medical Center, Seoul 06351, Korea
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Andreas Stadlbauer, Anke Meyer-Baese and Max Zimmermann
Cancers 2022, 14(1), 40; https://doi.org/10.3390/cancers14010040
Received: 23 November 2021 / Revised: 17 December 2021 / Accepted: 20 December 2021 / Published: 23 December 2021
(This article belongs to the Collection Artificial Intelligence in Oncology)
MRI-only simulation in radiation therapy (RT) planning has received attention because the CT scan can be omitted. For MRI-only simulation, synthetic CT (sCT) is necessary for the dose calculation. Various methodologies have been suggested for the generation of sCT and, recently, methods using the deep learning approaches are actively investigated. GAN and cycle-consistent GAN (CycGAN) have been mainly tested, however, very limited studies compared the qualities of sCTs generated from these methods or suggested other models for sCT generation. We have compared GAN, CycGAN, and, reference-guided GAN (RgGAN), a new model of deep learning method. We found that the performance in the HU conservation for soft tissue was poorest for GAN. All methods could generate sCTs feasible for VMAT planning with the trend that sCT generated from the RgGAN showed best performance in dosimetric conservation D98% and D95% than sCTs from other methodologies.
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. View Full-Text
Keywords: prostate neoplasm; magnetic resonance imaging; synthetic computed tomography; deep learning; radiotherapy prostate neoplasm; magnetic resonance imaging; synthetic computed tomography; deep learning; radiotherapy
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MDPI and ACS Style

Yoo, G.S.; Luu, H.M.; Kim, H.; Park, W.; Pyo, H.; Han, Y.; Park, J.Y.; Park, S.-H. 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. Cancers 2022, 14, 40. https://doi.org/10.3390/cancers14010040

AMA Style

Yoo GS, Luu HM, Kim H, Park W, Pyo H, Han Y, Park JY, Park S-H. 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. Cancers. 2022; 14(1):40. https://doi.org/10.3390/cancers14010040

Chicago/Turabian Style

Yoo, Gyu Sang, Huan Minh Luu, Heejung Kim, Won Park, Hongryull Pyo, Youngyih Han, Ju Young Park, and Sung-Hong Park. 2022. "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" Cancers 14, no. 1: 40. https://doi.org/10.3390/cancers14010040

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