Automated and robust estimation framework for lung tumor location in kilovolt cone-beam computed tomography images for target-based patient positioning in lung stereotactic body radiotherapy

Abstract

Image-guided radiotherapy （IGRT） systems using kilovolt cone-beam computed tomography （kV-CBCT） images are being commonly used for highly accurate patient positioning in lung stereotactic body radiotherapy （SBRT）. However, current IGRT procedures are based on bone structure and subjective correction. Therefore, the purpose of this study was to investigate an automated and robust estimation framework for lung tumor location in kV-CBCT images to improve target-based patient positioning for lung SBRT. One-hundred-and-sixty kV-CBCT images from 40 clinical cases treated with SBRT were used. The proposed framework comprised four steps, i.e., determination of a search region,extraction of a tumor template, preprocessing for enhancement of the tumor region, and estimation of the tumor location by a template-matching technique. Original, edge enhancement, and tumor enhancement images were obtained by enhancement of a tumor region based on each form of preprocessing and were used for template matching. The mean Euclidean distances of location errors for original, edge enhancement, and tumor enhancement images were 1.2 ± 0.7 mm, 5.5 ± 10.1 mm and 2.7 ± 4.4 mm, respectively. These findings suggested that the proposed automated framework may be robust for estimating the location of lung tumors in kV-CBCT images for lung SBRT.