title: Mass preserving image registration for lung CT.
abstract: This paper presents a mass preserving image registration algorithm for lung CT images. To account for the local change in lung tissue intensity during the breathing cycle, a tissue appearance model based on the principle of preservation of total lung mass is proposed. This model is incorporated into a standard image registration framework with a composition of a global affine and several free-form B-Spline transformations with increasing grid resolution. The proposed mass preserving registration method is compared to registration using the sum of squared intensity differences as a similarity function on four groups of data: 44 pairs of longitudinal inspiratory chest CT scans with small difference in lung volume; 44 pairs of longitudinal inspiratory chest CT scans with large difference in lung volume; 16 pairs of expiratory and inspiratory CT scans; and 5 pairs of images extracted at end exhale and end inhale phases of 4D-CT images. Registration errors, measured as the average distance between vessel tree centerlines in the matched images, are significantly lower for the proposed mass preserving image registration method in the second, third and fourth group, while there is no statistically significant difference between the two methods in the first group. Target registration error, assessed via a set of manually annotated landmarks in the last group, was significantly smaller for the proposed registration method.
authors.affiliation: Image Group, Department of Computer Science, University of Copenhagen, Denmark. vladlena@diku.dk
authors.forename: Vladlena
authors.initials: V
authors.lastname: Gorbunova
authors.forename: Jon
authors.initials: J
authors.lastname: Sporring
authors.forename: Pechin
authors.initials: P
authors.lastname: Lo
authors.forename: Martine
authors.initials: M
authors.lastname: Loeve
authors.forename: Harm A
authors.initials: HA
authors.lastname: Tiddens
authors.forename: Mads
authors.initials: M
authors.lastname: Nielsen
authors.forename: Asger
authors.initials: A
authors.lastname: Dirksen
authors.forename: Marleen
authors.initials: M
authors.lastname: de Bruijne
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008168:Lung / Q000000981:diagnostic imaging*; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods; D015203:Reproducibility of Results; D055425:Respiratory-Gated Imaging Techniques / Q000379:methods*; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*