Shupeng Chen1, An Qin1, Dingyi Zhou2, Di Yan1. 1. Department of Radiation Oncology, William Beaumont Hospital, 3601 W. 13 Mile Rd, Royal Oak, MI, 48073, USA. 2. Department of Oncology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China.
Abstract
PURPOSE: Clinical implementation of magnetic resonance imaging (MRI)-only radiotherapy requires a method to derive synthetic CT image (S-CT) for dose calculation. This study investigated the feasibility of building a deep convolutional neural network for MRI-based S-CT generation and evaluated the dosimetric accuracy on prostate IMRT planning. METHODS: A paired CT and T2-weighted MR images were acquired from each of 51 prostate cancer patients. Fifteen pairs were randomly chosen as tested set and the remaining 36 pairs as training set. The training subjects were augmented by applying artificial deformations and feed to a two-dimensional U-net which contains 23 convolutional layers and 25.29 million trainable parameters. The U-net represents a nonlinear function with input an MR slice and output the corresponding S-CT slice. The mean absolute error (MAE) of Hounsfield unit (HU) between the true CT and S-CT images was used to evaluate the HU estimation accuracy. IMRT plans with dose 79.2 Gy prescribed to the PTV were applied using the true CT images. The true CT images then were replaced by the S-CT images and the dose matrices were recalculated on the same plan and compared to the one obtained from the true CT using gamma index analysis and absolute point dose discrepancy. RESULTS: The U-net was trained from scratch in 58.67 h using a GP100-GPU. The computation time for generating a new S-CT volume image was 3.84-7.65 s. Within body, the (mean ± SD) of MAE was (29.96 ± 4.87) HU. The 1%/1 mm and 2%/2 mm gamma pass rates were over 98.03% and 99.36% respectively. The DVH parameters discrepancy was less than 0.87% and the maximum point dose discrepancy within PTV was less than 1.01% respect to the prescription. CONCLUSION: The U-net can generate S-CT images from conventional MR image within seconds with high dosimetric accuracy for prostate IMRT plan.
PURPOSE: Clinical implementation of magnetic resonance imaging (MRI)-only radiotherapy requires a method to derive synthetic CT image (S-CT) for dose calculation. This study investigated the feasibility of building a deep convolutional neural network for MRI-based S-CT generation and evaluated the dosimetric accuracy on prostate IMRT planning. METHODS: A paired CT and T2-weighted MR images were acquired from each of 51 prostate cancerpatients. Fifteen pairs were randomly chosen as tested set and the remaining 36 pairs as training set. The training subjects were augmented by applying artificial deformations and feed to a two-dimensional U-net which contains 23 convolutional layers and 25.29 million trainable parameters. The U-net represents a nonlinear function with input an MR slice and output the corresponding S-CT slice. The mean absolute error (MAE) of Hounsfield unit (HU) between the true CT and S-CT images was used to evaluate the HU estimation accuracy. IMRT plans with dose 79.2 Gy prescribed to the PTV were applied using the true CT images. The true CT images then were replaced by the S-CT images and the dose matrices were recalculated on the same plan and compared to the one obtained from the true CT using gamma index analysis and absolute point dose discrepancy. RESULTS: The U-net was trained from scratch in 58.67 h using a GP100-GPU. The computation time for generating a new S-CT volume image was 3.84-7.65 s. Within body, the (mean ± SD) of MAE was (29.96 ± 4.87) HU. The 1%/1 mm and 2%/2 mm gamma pass rates were over 98.03% and 99.36% respectively. The DVH parameters discrepancy was less than 0.87% and the maximum point dose discrepancy within PTV was less than 1.01% respect to the prescription. CONCLUSION: The U-net can generate S-CT images from conventional MR image within seconds with high dosimetric accuracy for prostate IMRT plan.
Authors: Yang Lei; Joseph Harms; Tonghe Wang; Sibo Tian; Jun Zhou; Hui-Kuo Shu; Jim Zhong; Hui Mao; Walter J Curran; Tian Liu; Xiaofeng Yang Journal: Phys Med Biol Date: 2019-04-05 Impact factor: 3.609
Authors: Carri K Glide-Hurst; Eric S Paulson; Kiaran McGee; Neelam Tyagi; Yanle Hu; James Balter; John Bayouth Journal: Med Phys Date: 2021-07 Impact factor: 4.071