Cycle-generative adversarial network-based bone suppression imaging for highly accurate markerless motion tracking of lung tumors for cyberknife irradiation therapy.

Abstract

Lung tumor tracking during stereotactic radiotherapy with the CyberKnife can misrecognize tumor location under conditions where similar patterns exist in the search area. This study aimed to develop a technique for bone signal suppression during kV-x-ray imaging. Paired CT images were created with or without bony structures using a 4D extended cardiac-torso phantom (XCAT phantom) in 56 cases. Subsequently, 3020 2D x-ray images were generated. Images with bone were input into cycle-consistent adversarial network (CycleGAN) and the bone suppressed images on the XCAT phantom (BSIphantom ) were created. They were then compared to images without bone using the structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR). Next, 1000 non-simulated treatment images from real cases were input into the training model, and bone-suppressed images of the patient (BSIpatient ) were created. Zero means normalized cross correlation (ZNCC) by template matching between each of the actual treatment images and BSIpatient were calculated. BSIphantom values were compared to their paired images without bone of the XCAT phantom test data; SSIM and PSNR were 0.90±0.06 and 24.54±4.48, respectively. It was visually confirmed that only bone was selectively suppressed without significantly affecting tumor visualization. The ZNCC values of the actual treatment images and BSIpatient were 0.763±0.136 and 0.773±0.143, respectively. The BSIpatient showed improved recognition accuracy over the actual treatment images. The proposed bone suppression imaging technique based on CycleGAN improves image recognition, making it possible to achieve highly accurate motion tracking irradiation.