Dose evaluation of fast synthetic-CT generation using a generative adversarial network for general pelvis MR-only radiotherapy

Abstract

To enable magnetic resonance (MR)-only radiotherapy and facilitate modelling of radiation attenuation in humans, synthetic CT (sCT) images need to be generated. Considering the application of MR-guided radiotherapy and online adaptive replanning, sCT generation should occur within minutes. This work aims at assessing whether an existing deep learning network can rapidly generate sCT images for accurate MR-based dose calculations in the entire pelvis. A study was conducted on data of 91 patients with prostate (59), rectal (18) and cervical (14) cancer who underwent external beam radiotherapy acquiring both CT and MRI for patients' simulation. Dixon reconstructed water, fat and in-phase images obtained from a conventional dual gradient-recalled echo sequence were used to generate sCT images. A conditional generative adversarial network (cGAN) was trained in a paired fashion on 2D transverse slices of 32 prostate cancer patients. The trained network was tested on the remaining patients to generate sCT images. For 30 patients in the test set, dose recalculations of the clinical plan were performed on sCT images. Dose distributions were evaluated comparing voxel-based dose differences, gamma and dose-volume histogram (DVH) analysis. The sCT generation required 5.6 s and 21 s for a single patient volume on a GPU and CPU, respectively. On average, sCT images resulted in a higher dose to the target of maximum 0.3%. The average gamma pass rates using the 3%, 3 mm and 2%, 2 mm criteria were above 97 and 91%, respectively, for all volumes of interests considered. All DVH points calculated on sCT differed less than ±2.5% from the corresponding points on CT. Results suggest that accurate MR-based dose calculation using sCT images generated with a cGAN trained on prostate cancer patients is feasible for the entire pelvis. The sCT generation was sufficiently fast for integration in an MR-guided radiotherapy workflow.

Deep learning approaches using 2D and 3D convolutional neural networks for generating male pelvic synthetic computed tomography from magnetic resonance imaging

“…Recently, deep learning models proposed to estimate sCT images from MR images have demonstrated promising results . Nie et al .…”

Dosimetric evaluation of synthetic CT for head and neck radiotherapy generated by a patch‐based three‐dimensional convolutional neural network

“…A relatively new approach in sCT generation is to train a convolutional neural network (CNN) to convert the MR images to CT . A CNN is a fully trainable model that can learn the mapping between input MR and output CT images.…”

Technical Note: U‐net‐generated synthetic CT images for magnetic resonance imaging‐only prostate intensity‐modulated radiation therapy treatment planning

“…The input contours can be regarded as additional knowledge to help the model predict CT number. Compared with other deep learning‐based methods evaluated on the prostate/pelvic data, our results achieved a relatively smaller MAE. Partly because we adopted extensively data augmentation by applying artificially created deformations to the training subjects to increase the number of training samples.…”

“…Such CT equivalent data usually referred to as pseudo‐CT or synthetic‐CT (S‐CT) images. Various methods of S‐CT image generation has been proposed which can be categorized as (a) statistical modeling; (b) multi‐atlas deformable image registration alone or combined with local weighting or patch fusion algorithms; (c) random forest‐based methods; and (d) deep learning‐based methods …”