A NOVEL MARKER-LESS TUMOR TRACKING STRATEGY ON LOW-RANK FLUOROSCOPIC IMAGES FOR IMAGE-GUIDED LUNG CANCER RADIOTHERAPY

摘要

Fluoroscopic images recording the real-time motion of lung tumor lesion play an important role on lung cancer radiotherapy, as these images help to facilitate the accurate delivery of radiation dose on target tumor lesion. Derivation of tumor position in conventional lung tumor tracking strategies is realized via either placing external surrogates on patients or implanting internal fiducial markers in patients. Inaccurate tumor tracking and patient safety problems are often inevitable for these strategies. In this study, a novel marker-less tumor tracking strategy is presented for image-guided lung cancer radiotherapy. A fluoroscopic image is first decomposed into low-rank and sparse components based on robust-PCA via a split Bregman method. Then, a series of techniques, including K-means clustering, morphological processing, connected component analysis, etc are employed on obtained low-rank fluoroscopic images for tumor tracking. Clinical data obtained from 45 patients is incorporated for experimental evaluation. Promising results are demonstrated from the introduced strategy.