Evaluation of a deep learning-based pelvic synthetic CT generation technique for MRI-based prostate proton treatment planning.

Evaluation of a deep learning-based pelvic synthetic CT generation technique for MRI-based prostate proton treatment planning. Phys Med Biol. 2019 Sep 05;: Authors: Liu Y, Lei Y, Wang Y, Shafai-Erfani G, Wang T, Tian S, Patel P, Jani AB, McDonald M, Curran WJ, Liu T, Zhou J, Yang X Abstract The purpose of this work is to validate the application of a deep learning-based method for pelvic synthetic CT (sCT) generation that can be used for prostate proton beam therapy treatment planning. We propose to integrate dense block minimization into 3D cycle-consistent generative adversarial networks (cycle GAN) framework to effectively learn the nonlinear mapping between MRI and CT pairs. A cohort of 17 patients with co-registered CT and MR pairs were used to test the deep learning-based sCT generation method by leave-one-out cross-validation. Image quality between the sCT and CT images, gamma analysis passing rate, dose-volume metrics, distal range displacement, and the individual pencil beam Bragg peak shift between sCT- and CT-based proton plans were evaluated. The average mean absolute error (MAE) was 51.32±16.91 HU. The relative differences of the statistics of the PTV dose volume histogram (DVH) metrics in between sCT and CT were generally less than 1%. Mean values of dose difference, absolute dose difference (in percent of prescribed dose) were -0.07±0.07% and 0.23±0.08%. Mean gamma analysis pass rate of 1mm/1%, 2mm/2%, 3mm/3% criter...
Source: Physics in Medicine and Biology - Category: Physics Authors: Tags: Phys Med Biol Source Type: research