title: Adaptive patch-based POCS approach for super resolution reconstruction of 4D-CT lung data.
abstract: Image enhancement of lung four-dimensional computed tomography (4D-CT) data is highly important because image resolution remains a crucial point in lung cancer radiotherapy. In this paper, we proposed a method for lung 4D-CT super resolution (SR) by using an adaptive-patch-based projection onto convex sets (POCS) approach, which is in contrast with the global POCS SR algorithm, to recover fine details with lesser artifacts in images. The main contribution of this patch-based approach is that the interfering local structure from other phases can be rejected by employing a similar patch adaptive selection strategy. The effectiveness of our approach is demonstrated through experiments on simulated images and real lung 4D-CT datasets. A comparison with previously published SR reconstruction methods highlights the favorable characteristics of the proposed method.
authors.forename: Tingting
authors.initials: T
authors.lastname: Wang
authors.forename: Lei
authors.initials: L
authors.lastname: Cao
authors.forename: Wei
authors.initials: W
authors.lastname: Yang
authors.forename: Qianjin
authors.initials: Q
authors.lastname: Feng
authors.forename: Wufan
authors.initials: W
authors.lastname: Chen
authors.forename: Yu
authors.initials: Y
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D016477:Artifacts; D056973:Four-Dimensional Computed Tomography / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D008168:Lung / Q000000981:diagnostic imaging*