A novel marker-less lung tumor localization strategy on low-rank fluoroscopic images with similarity learning

Abstract

Fluoroscopic images depicting the movement of lung tumor lesions along with patients' respirations are essential in contemporary image-guided lung cancer radiotherapy, as the accurate delivery of radiation dose on lung tumor lesions can be facilitated with the help of fluoroscopic images. However, the quality of fluoroscopic images is often not high, and several factors including image noise, artifact, ribs occlusion often prevent the tumor lesion from being accurate localized. In this study, a novel marker-less lung tumor localization strategy is proposed. Unlike conventional lung tumor localization strategies, it doesn't require placing external surrogates on patients or implanting internal fiducial markers in patients. Thus ambiguous movement correlations between moving tumor lesions and surrogates as well as the risk of patients pneumothorax can be totally avoided. In this new strategy, fluoroscopic images are first decomposed into low-rank and sparse components via the split Bregman method, and then spectral clustering techniques are incorporated for similarity learning to realize the tumor localization task. Clinical data obtained from 60 patients with lung tumor lesions is utilized for experimental evaluation, and promising results obtained by the new strategy are demonstrated from the statistical point of view.