In Physics and imaging in radiation oncology
BACKGROUND AND PURPOSE : Magnetic Resonance guided Radiotherapy (MRgRT) still needs the acquisition of Computed Tomography (CT) images and co-registration between CT and Magnetic Resonance Imaging (MRI). The generation of synthetic CT (sCT) images from the MR data can overcome this limitation. In this study we aim to propose a Deep Learning (DL) based approach for sCT image generation for abdominal Radiotherapy using low field MR images.
MATERIALS AND METHODS : CT and MR images were collected from 76 patients treated on abdominal sites. U-Net and conditional Generative Adversarial Network (cGAN) architectures were used to generate sCT images. Additionally, sCT images composed of only six bulk densities were generated with the aim of having a Simplified sCT.Radiotherapy plans calculated using the generated images were compared to the original plan in terms of gamma pass rate and Dose Volume Histogram (DVH) parameters.
RESULTS : sCT images were generated in 2 s and 2.5 s with U-Net and cGAN architectures respectively.Gamma pass rates for 2%/2mm and 3%/3mm criteria were 91% and 95% respectively. Dose differences within 1% for DVH parameters on the target volume and organs at risk were obtained.
CONCLUSION : U-Net and cGAN architectures are able to generate abdominal sCT images fast and accurately from low field MRI.
Garcia Hernandez Armando, Fau Pierre, Wojak Julien, Mailleux Hugues, Benkreira Mohamed, Rapacchi Stanislas, Adel Mouloud
2023-Jan
Deep Learning, Low-field MRI, MR-only treatment planning, Synthetic CT
