MR to CT synthesis with multicenter data in the pelvic area using a conditional generative adversarial network
- 13 February 2020
- journal article
- research article
- Published by IOP Publishing in Physics in Medicine & Biology
- Vol. 65 (7), 075002
- https://doi.org/10.1088/1361-6560/ab7633
Abstract
The establishment of an MRI-only workflow in radiotherapy depends on the ability to generate an accurate synthetic-CT (sCT) for dose calculation. Previously proposed methods have used a Generative Adversarial Network (GAN) for fast sCT generation in order to simplify the clinical workflow and reduces uncertainties. In the current paper we use a conditional Generative Adversarial Network (cGAN) framework called pix2pixHD to create a robust model prone to multicenter data. This study included T2-weighted MR and CT images of 19 patients in treatment position from 3 different sites. The cGAN was trained on 2D transverse slices of 11 patients from 2 different sites. Once trained, the network was used to generate sCT images of 8 patients coming from a third site. The Mean Absolute Errors (MAE) for each patient were evaluated between real and synthetic CTs. A radiotherapy plan was optimized on the sCT series and re-calculated on CTs to assess the dose distribution in terms of voxel-wise dose difference and Dose Volume Histograms (DVH) analysis. It takes on average of 7.5 s to generate a complete sCT (88 slices) for a patient on our GPU. The average MAE in HU between the sCT and actual patient CT (within the body contour) is 48.5 ± 6 HU with our method. The maximum dose difference to the target is 1.3%. This study demonstrates that an sCT can be generated in a multicentric context, with fewer pre-processing steps while being fast and accurate.Keywords
This publication has 10 references indexed in Scilit:
- Generative adversarial networksCommunications of the ACM, 2020
- MR-Only Brain Radiation Therapy: Dosimetric Evaluation of Synthetic CTs Generated by a Dilated Convolutional Neural NetworkInternational Journal of Radiation Oncology*Biology*Physics, 2018
- Dose evaluation of fast synthetic-CT generation using a generative adversarial network for general pelvis MR-only radiotherapyPhysics in Medicine & Biology, 2018
- High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2018
- MR and CT data with multiobserver delineations of organs in the pelvic area—Part of the Gold Atlas projectMedical Physics, 2018
- Least Squares Generative Adversarial NetworksPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2017
- Deep MR to CT Synthesis Using Unpaired DataLecture Notes in Computer Science, 2017
- Medical Image Synthesis with Context-Aware Generative Adversarial NetworksLecture Notes in Computer Science, 2017
- MR-based synthetic CT generation using a deep convolutional neural network methodMedical Physics, 2017
- Perceptual Losses for Real-Time Style Transfer and Super-ResolutionPublished by Springer Science and Business Media LLC ,2016