Feasibility of synthetic computed tomography generated with an adversarial network for multi-sequence magnetic resonance-based brain radiotherapy

Abstract

The aim of this work is to generate synthetic computed tomography (sCT) images from multi-sequence magnetic resonance (MR) images using an adversarial network and to assess the feasibility of sCT-based treatment planning for brain radiotherapy. Datasets for 15 patients with glioblastoma were selected and 580 pairs of CT and MR images were used. T1-weighted, T2-weighted and fluid-attenuated inversion recovery MR sequences were combined to create a three-channel image as input data. A conditional generative adversarial network (cGAN) was trained using image patches. The image quality was evaluated using voxel-wise mean absolute errors (MAEs) of the CT number. For the dosimetric evaluation, 3D conformal radiotherapy (3D-CRT) and volumetric modulated arc therapy (VMAT) plans were generated using the original CT set and recalculated using the sCT images. The isocenter dose and dose–volume parameters were compared for 3D-CRT and VMAT plans, respectively. The equivalent path length was also compared. The mean MAEs for the whole body, soft tissue and bone region were 108.1 ± 24.0, 38.9 ± 10.7 and 366.2 ± 62.0 hounsfield unit, respectively. The dosimetric evaluation revealed no significant difference in the isocenter dose for 3D-CRT plans. The differences in the dose received by 2% of the volume (D2%), D50% and D98% relative to the prescribed dose were <1.0%. The overall equivalent path length was shorter than that for real CT by 0.6 ± 1.9 mm. A treatment planning study using generated sCT detected only small, clinically negligible differences. These findings demonstrated the feasibility of generating sCT images for MR-only radiotherapy from multi-sequence MR images using cGAN.

Monte Carlo Dose Calculation Using MRI Based Synthetic CT Generated by Fully Convolutional Neural Network for Gamma Knife Radiosurgery

“…The HU ranges for soft tissue and bone are defined differently in the literature. 17 , 25 , 31 , 32 In our work, we define the HU range for soft tissue as greater than −200 HU and less than 200 HU, and the HU range for bone as greater than 200 HU. The MAE was 32.2 ± 2.9 HU and 277.7 ± 64.4 for soft tissue and bones, respectively.…”

“…Our method produced an overall average MAE of 86.5 HU and an MSE of 160.9 using 30 test cases, which is similar to the results from Han 15 (84.8 HU and 188.6, respectively) based on 18 test subjects. Other examples of reported MAE results using CNN in the literature for the brain sCT are 131 HU 31 based on 10 test patients and 108.1 HU 32 using 15 patients.…”

“…34 ‐ 37 A GAN consists of 2 CNNs: a generative network generating candidates and a discriminative network evaluating them. Emami et al 37 achieved an MAE of 89.3 HU using 15 patient data and Kazemifar et al 38 achieved an MAE of 47.2 HU with training dataset of 77 patients for sCT generation from T1-weighted MR images, while Koike et al 32 reported an MAE of 108.1 HU for multisequence (T1- and T2-weighted, fluid-attenuated inversion recovery) MR images. The results demonstrate that the performance metrics not only depends on the model but also on underlying training data.…”

Cycle-Consistent Generative Adversarial Network: Effect on Radiation Dose Reduction and Image Quality Improvement in Ultralow-Dose CT for Evaluation of Pulmonary Tuberculosis

“…GAN is a promising method for image-to-image translation with several variants, including WGAN [ 17 ], CycleGAN [ 16 29 ], and pix2pix conditional GAN [ 30 ]. In this study, breath movement between two successive CT scans resulted in a mismatch in pixel-to-pixel correspondence.…”

Deep learning for brain disorders: from data processing to disease treatment

“…Image translation has also been used in a cross-modality context, such as to generate computed tomography (CT) from MR images [69][70][71][72][73][74][75][76][77][78][79][80][81][82][83][84] or to generate MR images of a certain sequence from MR images of another sequence [74,[85][86][87][88][89][90] or a set of other sequences [83,[91][92][93][94][95][96][97]. A large number of methods have been applied to improve attenuation correction on PET/MR scanners [73,76,78,81,98,99] or to enable radiotherapy treatment planning from MRI only [71,77,80,84]. Other studies aim to improve subsequent image processing steps such as segmentation or registration [90], improve classification in case of missing data [86,91,100,...…”