Mass preserving image registration for lung CT

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Standard

Mass preserving image registration for lung CT. / Gorbunova, Vladlena; Sporring, Jon; Lo, Pechin Chien Pau; Loeve, Martine; Tiddens, Harm A; Nielsen, Mads; Dirksen, Asger; de Bruijne, Marleen.

I: Medical Image Analysis, Bind 16, Nr. 4, 2012, s. 786-795.

Publikation: Bidrag til tidsskriftTidsskriftartikelfagfællebedømt

Harvard

Gorbunova, V, Sporring, J, Lo, PCP, Loeve, M, Tiddens, HA, Nielsen, M, Dirksen, A & de Bruijne, M 2012, 'Mass preserving image registration for lung CT', Medical Image Analysis, bind 16, nr. 4, s. 786-795. https://doi.org/10.1016/j.media.2011.11.001

APA

Gorbunova, V., Sporring, J., Lo, P. C. P., Loeve, M., Tiddens, H. A., Nielsen, M., Dirksen, A., & de Bruijne, M. (2012). Mass preserving image registration for lung CT. Medical Image Analysis, 16(4), 786-795. https://doi.org/10.1016/j.media.2011.11.001

Vancouver

Gorbunova V, Sporring J, Lo PCP, Loeve M, Tiddens HA, Nielsen M o.a. Mass preserving image registration for lung CT. Medical Image Analysis. 2012;16(4):786-795. https://doi.org/10.1016/j.media.2011.11.001

Author

Gorbunova, Vladlena ; Sporring, Jon ; Lo, Pechin Chien Pau ; Loeve, Martine ; Tiddens, Harm A ; Nielsen, Mads ; Dirksen, Asger ; de Bruijne, Marleen. / Mass preserving image registration for lung CT. I: Medical Image Analysis. 2012 ; Bind 16, Nr. 4. s. 786-795.

Bibtex

@article{86219ded126f4d5fa530f54c9ea63192,
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.",
author = "Vladlena Gorbunova and Jon Sporring and Lo, {Pechin Chien Pau} and Martine Loeve and Tiddens, {Harm A} and Mads Nielsen and Asger Dirksen and {de Bruijne}, Marleen",
note = "Copyright {\^A}{\textcopyright} 2012 Elsevier B.V. All rights reserved.",
year = "2012",
doi = "10.1016/j.media.2011.11.001",
language = "English",
volume = "16",
pages = "786--795",
journal = "Medical Image Analysis",
issn = "1361-8415",
publisher = "Elsevier",
number = "4",

}

RIS

TY - JOUR

T1 - Mass preserving image registration for lung CT

AU - Gorbunova, Vladlena

AU - Sporring, Jon

AU - Lo, Pechin Chien Pau

AU - Loeve, Martine

AU - Tiddens, Harm A

AU - Nielsen, Mads

AU - Dirksen, Asger

AU - de Bruijne, Marleen

N1 - Copyright © 2012 Elsevier B.V. All rights reserved.

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

U2 - 10.1016/j.media.2011.11.001

DO - 10.1016/j.media.2011.11.001

M3 - Journal article

C2 - 22336692

VL - 16

SP - 786

EP - 795

JO - Medical Image Analysis

JF - Medical Image Analysis

SN - 1361-8415

IS - 4

ER -

ID: 37603358