MRI-based Treatment Planning for Proton Radiotherapy: Dosimetric Validation of a Deep Learning-based Liver Synthetic CT Generation Method

• Authors: Yingzi Liu, Emory University; Yang Lei, Emory University; Yinan Wang, Emory University; Tonghe Wang, Emory University; Lei Ren, Duke University; Liyong Lin, Emory University; Mark McDonald, Emory University; Walter Curran Jr, Emory University; Tian Liu, Emory University; Jun Zhou, Emory University; Xiaofeng Yang, Emory University
• Journal Title: PHYSICS IN MEDICINE AND BIOLOGY
• Volume: Volume 64, Number 14
• Publisher: IOP Publishing | 2019
• Type of Work: Article | Post-print: After Peer Review
• Publisher DOI: http://doi.org/10.1088/1361-6560/ab25bc
• Subjects: Physics, Radiation; Health Sciences, Oncology
• Keywords: magnetic resonance imaging (MRI); computed tomography (CT); radiation therapy; accuracy; reliability; delineation; soft tissue contrast; synthetic CT (sCT); treatment planning system; 3D cycle-consistent generative adversarial networks (cycle GAN); workflow

Abstract

Magnetic resonance imaging (MRI) has been widely used in combination with computed tomography (CT) radiation therapy because MRI improves the accuracy and reliability of target delineation due to its superior soft tissue contrast over CT. The MRI-only treatment process is currently an active field of research since it could eliminate systematic MR-CT co-registration errors, reduce medical cost, avoid diagnostic radiation exposure, and simplify clinical workflow. The purpose of this work is to validate the application of a deep learning-based method for abdominal synthetic CT (sCT) generation by image evaluation and dosimetric assessment in a commercial proton pencil beam treatment planning system (TPS). This study proposes to integrate dense block into a 3D cycle-consistent generative adversarial networks (cycle GAN) framework in an effort to effectively learn the nonlinear mapping between MRI and CT pairs. A cohort of 21 patients with co-registered CT and MR pairs were used to test the deep learning-based sCT image quality by leave-one-out cross validation. The CT image quality, dosimetric accuracy and the distal range fidelity were rigorously checked, using side-by-side comparison against the corresponding original CT images. The average mean absolute error (MAE) was 72.87±18.16 HU. The relative differences of the statistics of the PTV dose volume histogram (DVH) metrics between sCT and CT were generally less than 1%. Mean 3D gamma analysis passing rate of 1mm/1%, 2mm/2%, 3mm/3% criteria with 10% dose threshold were 90.76±5.94%, 96.98±2.93% and 99.37±0.99%, respectively. The median, mean and standard deviation of absolute maximum range differences were 0.170 cm, 0.186 cm and 0.155 cm. The image similarity, dosimetric and distal range agreement between sCT and original CT suggests the feasibility of further development of an MRI-only workflow for liver proton radiotherapy.