Garcia Hernandez Armando, Fau Pierre, Wojak Julien, Mailleux Hugues, Benkreira Mohamed, Rapacchi Stanislas, Adel Mouloud
Aix Marseille Univ, CNRS, Centrale Marseille, Institut Fresnel, Marseille, France.
Institut Paoli-Calmettes, Bouches du Rhône, Marseille, France.
Phys Imaging Radiat Oncol. 2023 Feb 23;25:100425. doi: 10.1016/j.phro.2023.100425. eCollection 2023 Jan.
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.
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.
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.
U-Net and cGAN architectures are able to generate abdominal sCT images fast and accurately from low field MRI.
磁共振引导放射治疗（MRgRT）仍需要获取计算机断层扫描（CT）图像以及CT与磁共振成像（MRI）之间的配准。从MR数据生成合成CT（sCT）图像可以克服这一局限性。在本研究中，我们旨在提出一种基于深度学习（DL）的方法，用于使用低场MR图像生成腹部放射治疗的sCT图像。
收集了76例接受腹部部位治疗患者的CT和MR图像。使用U-Net和条件生成对抗网络（cGAN）架构生成sCT图像。此外，为了得到简化的sCT，还生成了仅由六种体密度组成的sCT图像。将使用生成图像计算的放射治疗计划在伽马通过率和剂量体积直方图（DVH）参数方面与原始计划进行比较。
使用U-Net和cGAN架构分别在2秒和2.5秒内生成了sCT图像。2%/2mm和3%/3mm标准的伽马通过率分别为91%和95%。在靶区体积和危及器官上获得了DVH参数1%以内的剂量差异。
U-Net和cGAN架构能够从低场MRI快速准确地生成腹部sCT图像。
