Deep learning-based real-time volumetric imaging for lung stereotactic body radiation therapy: a proof of concept study

Abstract

Due to the inter- and intra- variation of respiratory motion, it is highly desired to provide real-time volumetric images during the treatment delivery of lung stereotactic body radiation therapy (SBRT) for accurate and active motion management. In this proof-of-concept study, we propose a novel generative adversarial network integrated with perceptual supervision to derive instantaneous volumetric images from a single 2D projection. Our proposed network, named TransNet, consists of three modules, i.e. encoding, transformation and decoding modules. Rather than only using image distance loss between the generated 3D images and the ground truth 3D CT images to supervise the network, perceptual loss in feature space is integrated into loss function to force the TransNet to yield accurate lung boundary. Adversarial supervision is also used to improve the realism of generated 3D images. We conducted a simulation study on 20 patient cases, who had received lung SBRT treatments in our institution and undergone 4D-CT simulation, and evaluated the efficacy and robustness of our method for four different projection angles, i.e. 0°, 30°, 60° and 90°. For each 3D CT image set of a breathing phase, we simulated its 2D projections at these angles. For each projection angle, a patient’s 3D CT images of 9 phases and the corresponding 2D projection data were used to train our network for that specific patient, with the remaining phase used for testing. The mean absolute error of the 3D images obtained by our method are 99.3 ± 14.1 HU. The peak signal-to-noise ratio and structural similarity index metric within the tumor region of interest are 15.4 ± 2.5 dB and 0.839 ± 0.090, respectively. The center of mass distance between the manual tumor contours on the 3D images obtained by our method and the manual tumor contours on the corresponding 3D phase CT images are within 2.6 mm, with a mean value of 1.26 mm averaged over all the cases. Our method has also been validated in a simulated challenging scenario with increased respiratory motion amplitude and tumor shrinkage, and achieved acceptable results. Our experimental results demonstrate the feasibility and efficacy of our 2D-to-3D method for lung cancer patients, which provides a potential solution for in-treatment real-time on-board volumetric imaging for tumor tracking and dose delivery verification to ensure the effectiveness of lung SBRT treatment.