title: Improving thoracic four-dimensional cone-beam CT reconstruction with anatomical-adaptive image regularization (AAIR).
abstract: Total-variation (TV) minimization reconstructions can significantly reduce noise and streaks in thoracic four-dimensional cone-beam computed tomography (4D CBCT) images compared to the Feldkamp-Davis-Kress (FDK) algorithm currently used in practice. TV minimization reconstructions are, however, prone to over-smoothing anatomical details and are also computationally inefficient. The aim of this study is to demonstrate a proof of concept that these disadvantages can be overcome by incorporating the general knowledge of the thoracic anatomy via anatomy segmentation into the reconstruction. The proposed method, referred as the anatomical-adaptive image regularization (AAIR) method, utilizes the adaptive-steepest-descent projection-onto-convex-sets (ASD-POCS) framework, but introduces an additional anatomy segmentation step in every iteration. The anatomy segmentation information is implemented in the reconstruction using a heuristic approach to adaptively suppress over-smoothing at anatomical structures of interest. The performance of AAIR depends on parameters describing the weighting of the anatomy segmentation prior and segmentation threshold values. A sensitivity study revealed that the reconstruction outcome is not sensitive to these parameters as long as they are chosen within a suitable range. AAIR was validated using a digital phantom and a patient scan and was compared to FDK, ASD-POCS and the prior image constrained compressed sensing (PICCS) method. For the phantom case, AAIR reconstruction was quantitatively shown to be the most accurate as indicated by the mean absolute difference and the structural similarity index. For the patient case, AAIR resulted in the highest signal-to-noise ratio (i.e. the lowest level of noise and streaking) and the highest contrast-to-noise ratios for the tumor and the bony anatomy (i.e. the best visibility of anatomical details). Overall, AAIR was much less prone to over-smoothing anatomical details compared to ASD-POCS and did not suffer from residual noise/streaking and motion blur migrated from the prior image as in PICCS. AAIR was also found to be more computationally efficient than both ASD-POCS and PICCS, with a reduction in computation time of over 50% compared to ASD-POCS. The use of anatomy segmentation was, for the first time, demonstrated to significantly improve image quality and computational efficiency for thoracic 4D CBCT reconstruction. Further developments are required to facilitate AAIR for practical use.
authors.affiliation: Radiation Physics Laboratory, Sydney Medical School, The University of Sydney, NSW 2006, Australia. Institute of Medical Physics, School of Physics, The University of Sydney, NSW 2006, Australia.
authors.forename: Chun-Chien
authors.initials: CC
authors.lastname: Shieh
authors.forename: John
authors.initials: J
authors.lastname: Kipritidis
authors.forename: Ricky T
authors.initials: RT
authors.lastname: O'Brien
authors.forename: Benjamin J
authors.initials: BJ
authors.lastname: Cooper
authors.forename: Zdenka
authors.initials: Z
authors.lastname: Kuncic
authors.forename: Paul J
authors.initials: PJ
authors.lastname: Keall
mesh_terms: D000465:Algorithms; D054893:Cone-Beam Computed Tomography / Q000379:methods*; D056973:Four-Dimensional Computed Tomography / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D009038:Motion; D019047:Phantoms, Imaging; D011786:Quality Control; D011857:Radiographic Image Interpretation, Computer-Assisted; D013902:Radiography, Thoracic / Q000379:methods*; D059629:Signal-To-Noise Ratio