title: Estimating dynamic lung images from high-dimension chest surface motion using 4D statistical model.
abstract: Computed Tomography (CT) has been widely used in image-guided procedures such as intervention and radiotherapy of lung cancer. However, due to poor reproducibility of breath holding or respiratory cycles, discrepancies between static images and patient's current lung shape and tumor location could potentially reduce the accuracy for image guidance. Current methods are either using multiple intra-procedural scans or monitoring respiratory motion with tracking sensors. Although intra-procedural scanning provides more accurate information, it increases the radiation dose and still only provides snapshots of patient's chest. Tracking-based breath monitoring techniques can effectively detect respiratory phases but have not yet provided accurate tumor shape and location due to low dimensional signals. Therefore, estimating the lung motion and generating dynamic CT images from real-time captured high-dimensional sensor signals acts as a key component for image-guided procedures. This paper applies a principal component analysis (PCA)-based statistical model to establish the relationship between lung motion and chest surface motion from training samples, on a template space, and then uses this model to estimate dynamic images for a new patient from the chest surface motion. Qualitative and quantitative results showed that the proposed high-dimensional estimation algorithm yielded more accurate 4D-CT compared to fiducial marker-based estimation.
authors.forename: Tiancheng
authors.initials: T
authors.lastname: He
authors.forename: Zhong
authors.initials: Z
authors.lastname: Xue
authors.forename: Nam
authors.initials: N
authors.lastname: Yu
authors.forename: Paige L
authors.initials: PL
authors.lastname: Nitsch
authors.forename: Bin S
authors.initials: BS
authors.lastname: Teh
authors.forename: Stephen T
authors.initials: ST
authors.lastname: Wong
mesh_terms: D000465:Algorithms; D016477:Artifacts; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D056973:Four-Dimensional Computed Tomography / Q000379:methods*; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D015233:Models, Statistical; D009038:Motion; D009068:Movement; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique