Unsupervised pseudo CT generation using heterogenous multicentric CT/MR images and CycleGAN: Dosimetric assessment for 3D conformal radiotherapy

Computers in Biology and Medicine, vol. 143, pp. 105277

Abstract

PURPOSE: Absorbed dose calculation in magnetic resonance-guided radiation therapy (MRgRT) is commonly based on pseudo CT (pCT) images. This study investigated the feasibility of unsupervised pCT generation from MRI using a cycle generative adversarial network (CycleGAN) and a heterogenous multicentric dataset. A dosimetric analysis in three-dimensional conformal radiotherapy (3DCRT) planning was also performed. MATERIAL AND METHODS: Overall, 87 T1-weighted and 102 T2-weighted MR images alongside with their corresponding computed tomography (CT) images of brain cancer patients from multiple centers were used. Initially, images underwent a number of preprocessing steps, including rigid registration, novel CT Masker, N4 bias field correction, resampling, resizing, and rescaling. To overcome the gradient vanishing problem, residual blocks and mean squared error (MSE) loss function were utilized in the generator and in both networks (generator and discriminator), respectively. The CycleGAN was trained and validated using 70 T1 and 80 T2 randomly selected patients in an unsupervised manner. The remaining patients were used as a holdout test set to report final evaluation metrics. The generated pCTs were validated in the context of 3DCRT. RESULTS: The CycleGAN model using masked T2 images achieved better performance with a mean absolute error (MAE) of 61.87 ± 22.58 HU, peak signal to noise ratio (PSNR) of 27.05 ± 2.25 (dB), and structural similarity index metric (SSIM) of 0.84 ± 0.05 on the test dataset. T1-weighted MR images used for dosimetric assessment revealed a gamma index of 3%, 3 mm, 2%, 2 mm and 1%, 1 mm with acceptance criteria of 98.96% ± 1.1%, 95% ± 3.68%, 90.1% ± 6.05%, respectively. The DVH differences between CTs and pCTs were within 2%. CONCLUSIONS: A promising pCT generation model capable of handling heterogenous multicenteric datasets was proposed. All MR sequences performed competitively with no significant difference in pCT generation. The proposed CT Masker proved promising in improving the model accuracy and robustness. There was no significant difference between using T1-weighted and T2-weighted MR images for pCT generation.

Authors 6

1. Amir Jabbarpour

   Iran University of Medical Sciences

   Affiliation as printed

   Medical Physics Department, School of Medicine, Iran University of Medical Sciences, Tehran, Iran

2. Seied Rabi Mahdavi corresponding

   Iran University of Medical Sciences · Tehran University of Medical Sciences

   Affiliation as printed

   Medical Physics Department, School of Medicine, Iran University of Medical Sciences, Tehran, Iran; Radiation Biology Research Center, Iran University of Medical Sciences, Tehran, Iran. Electronic address: mahdavi.r@iums.ac.ir

   Medical Physics Department, School of Medicine, Iran University of Medical Sciences, Tehran, Iran

   Radiation Biology Research Center, Iran University of Medical Sciences, Tehran, Iran

3. Alireza Vafaei Sadr Aachen Institute of Pathology

   University of Geneva · Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany; Department of Theoretical Physics and Center for Astroparticle Physics, Geneva University, Geneva, Switzerland

   Department of Theoretical Physics and Center for Astroparticle Physics, Geneva University, Geneva, Switzerland

   Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany

4. Golbarg Esmaili
   Affiliation as printed

   Pars Hospital, Tehran, Iran

5. Isaac Shiri

   University Hospital of Geneva

   Affiliation as printed

   Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland

6. Habib Zaidi

   University of Geneva · University Medical Center Groningen · University of Groningen · University of Southern Denmark · University Hospital of Geneva

   Affiliation as printed

   Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland; Geneva University Neurocenter, Geneva University, Geneva, Switzerland; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands; Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark

   Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands

   Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark

   Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland

   Geneva University Neurocenter, Geneva University, Geneva, Switzerland