Dosimetric evaluation of synthetic CT generated with GANs for MRI‐only proton therapy treatment planning of brain tumors. Issue 5 (26th March 2020)

• Record Type: Journal Article
• Authors: Kazemifar, Samaneh; Barragán Montero, Ana M.; Souris, Kevin; Rivas, Sara T.; Timmerman, Robert; Park, Yang K.; Jiang, Steve; Geets, Xavier; Sterpin, Edmond; Owrangi, Amir
• Is Part Of: Journal of applied clinical medical physics. Volume 21:Issue 5(2020)
• Page Start: 76
• Page End: 86
• Publication Date: 2020-03-26
• Subjects: artificial intelligence -- brain MRI -- deep learning -- GAN structure -- proton therapy -- synthetic CT; Medical physics -- Periodicals; Clinical medicine -- Periodicals; Health Physics; Clinical Medicine; Electronic journals; Periodicals; Periodicals; Fulltext; Internet Resources; 610.153
• DOI: 10.1002/acm2.12856
• Languages: English
• ISSNs: 1526-9914

Abstract

Abstract: Purpose: The purpose of this study was to address the dosimetric accuracy of synthetic computed tomography (sCT) images of patients with brain tumor generated using a modified generative adversarial network (GAN) method, for their use in magnetic resonance imaging (MRI)‐only treatment planning for proton therapy. Methods: Dose volume histogram (DVH) analysis was performed on CT and sCT images of patients with brain tumor for plans generated for intensity‐modulated proton therapy (IMPT). All plans were robustly optimized using a commercially available treatment planning system (RayStation, from RaySearch Laboratories) and standard robust parameters reported in the literature. The IMPT plan was then used to compute the dose on CT and sCT images for dosimetric comparison, using RayStation analytical (pencil beam) dose algorithm. We used a second, independent Monte Carlo dose calculation engine to recompute the dose on both CT and sCT images to ensure a proper analysis of the dosimetric accuracy of the sCT images. Results: The results extracted from RayStation showed excellent agreement for most DVH metrics computed on the CT and sCT for the nominal case, with a mean absolute difference below 0.5% (0.3 Gy) of the prescription dose for the clinical target volume (CTV) and below 2% (1.2 Gy) for the organs at risk (OARs) considered. This demonstrates a high dosimetric accuracy for the generated sCT images, especially in the target volume. The metrics obtained from the Monte Carlo doses mostly agreed with the values extracted from RayStation for the nominal and worst‐case scenarios (mean difference below 3%). Conclusions: This work demonstrated the feasibility of using sCT generated with a GAN‐based deep learning method for MRI‐only treatment planning of patients with brain tumor in intensity‐modulated proton therapy.