Image Registration for a Dynamic Breathing Model

Pia F. Schulz; Andra Oltmann; Johannes Bostelmann; Ole Gildemeister; Franz Wegner; Jan Lellmann; Philipp Rostalski; Jan Modersitzki

doi:10.1007/978-3-658-47422-5_4

Image Registration for a Dynamic Breathing Model

Pia F. Schulz^*, Andra Oltmann, Johannes Bostelmann, Ole Gildemeister, Franz Wegner, Jan Lellmann, Philipp Rostalski, Jan Modersitzki

^*Corresponding author for this work

Abstract

Respiratory surface electromyography measures the electrical muscle activity during breathing non-invasively. Electrophysiological modeling of the respiratory cycle is a valuable tool for analysis of the signals. A promising approach for dynamic simulations is based on knowing the deformation of the torso at a finite number of time steps between expiration and inspiration. In order to provide a foundation for such models, we present a new image registration method that determines the torso transformation during the respiratory cycle. For this purpose, we extend a ResNet-LDDMM based 3D/3D registration approach. We modify the network structure and add 2D data taken during respiration into the registration to include information about the breathing motion. Our experiments show that these modifications improve the registration quality, thereby providing a step towards a more realistic model of electrical transfer behavior over the respiratory cycle. The code is publicly available at https://github.com/schulz-p/Image-Registration-for-a-Dynamic-Breathing-Model.

Original language	German
Title of host publication	Bildverarbeitung für die Medizin
Editors	C Palm, K Breininger, T Deserno, H Handels, A Maier, K.H. Maier-Hein, T.M. Tolxdorff
Number of pages	7
Publisher	Springer Science and Business Media Deutschland GmbH
Publication date	2025
Pages	5-11
ISBN (Print)	978-365847421-8
DOIs	https://doi.org/10.1007/978-3-658-47422-5_4
Publication status	Published - 2025

Research Areas and Centers

Academic Focus: Biomedical Engineering

DFG Research Classification Scheme

2.22-07 Medical Informatics and Medical Bioinformatics

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-658-47422-5_4

Cite this

Schulz, P. F., Oltmann, A., Bostelmann, J., Gildemeister, O., Wegner, F., Lellmann, J., Rostalski, P., & Modersitzki, J. (2025). Image Registration for a Dynamic Breathing Model. In C. Palm, K. Breininger, T. Deserno, H. Handels, A. Maier, K. H. Maier-Hein, & T. M. Tolxdorff (Eds.), Bildverarbeitung für die Medizin (pp. 5-11). (Informatik aktuell). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-658-47422-5_4

@inproceedings{76ae919922c843f8addcdcdb6a19c172,

title = "Image Registration for a Dynamic Breathing Model",

abstract = "Respiratory surface electromyography measures the electrical muscle activity during breathing non-invasively. Electrophysiological modeling of the respiratory cycle is a valuable tool for analysis of the signals. A promising approach for dynamic simulations is based on knowing the deformation of the torso at a finite number of time steps between expiration and inspiration. In order to provide a foundation for such models, we present a new image registration method that determines the torso transformation during the respiratory cycle. For this purpose, we extend a ResNet-LDDMM based 3D/3D registration approach. We modify the network structure and add 2D data taken during respiration into the registration to include information about the breathing motion. Our experiments show that these modifications improve the registration quality, thereby providing a step towards a more realistic model of electrical transfer behavior over the respiratory cycle. The code is publicly available at https://github.com/schulz-p/Image-Registration-for-a-Dynamic-Breathing-Model.",

author = "Schulz, {Pia F.} and Andra Oltmann and Johannes Bostelmann and Ole Gildemeister and Franz Wegner and Jan Lellmann and Philipp Rostalski and Jan Modersitzki",

note = "Publisher Copyright: {\textcopyright} Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2025. DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.",

year = "2025",

doi = "10.1007/978-3-658-47422-5_4",

language = "Deutsch",

isbn = "978-365847421-8",

series = "Informatik aktuell",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "5--11",

editor = "C Palm and K Breininger and T Deserno and H Handels and A Maier and K.H. Maier-Hein and T.M. Tolxdorff",

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Schulz, PF, Oltmann, A, Bostelmann, J , Gildemeister, O, Wegner, F, Lellmann, J , Rostalski, P & Modersitzki, J 2025, Image Registration for a Dynamic Breathing Model. in C Palm, K Breininger, T Deserno, H Handels, A Maier, KH Maier-Hein & TM Tolxdorff (eds), Bildverarbeitung für die Medizin. Informatik aktuell, Springer Science and Business Media Deutschland GmbH, pp. 5-11. https://doi.org/10.1007/978-3-658-47422-5_4

Image Registration for a Dynamic Breathing Model. / Schulz, Pia F.; Oltmann, Andra; Bostelmann, Johannes et al.
Bildverarbeitung für die Medizin. ed. / C Palm; K Breininger; T Deserno; H Handels; A Maier; K.H. Maier-Hein; T.M. Tolxdorff. Springer Science and Business Media Deutschland GmbH, 2025. p. 5-11 (Informatik aktuell).

Research output: Chapters in Books/Reports/Conference Proceedings › Conference contribution › peer-review

TY - GEN

T1 - Image Registration for a Dynamic Breathing Model

AU - Schulz, Pia F.

AU - Oltmann, Andra

AU - Bostelmann, Johannes

AU - Gildemeister, Ole

AU - Wegner, Franz

AU - Lellmann, Jan

AU - Rostalski, Philipp

AU - Modersitzki, Jan

N1 - Publisher Copyright: © Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2025. DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.

PY - 2025

Y1 - 2025

N2 - Respiratory surface electromyography measures the electrical muscle activity during breathing non-invasively. Electrophysiological modeling of the respiratory cycle is a valuable tool for analysis of the signals. A promising approach for dynamic simulations is based on knowing the deformation of the torso at a finite number of time steps between expiration and inspiration. In order to provide a foundation for such models, we present a new image registration method that determines the torso transformation during the respiratory cycle. For this purpose, we extend a ResNet-LDDMM based 3D/3D registration approach. We modify the network structure and add 2D data taken during respiration into the registration to include information about the breathing motion. Our experiments show that these modifications improve the registration quality, thereby providing a step towards a more realistic model of electrical transfer behavior over the respiratory cycle. The code is publicly available at https://github.com/schulz-p/Image-Registration-for-a-Dynamic-Breathing-Model.

AB - Respiratory surface electromyography measures the electrical muscle activity during breathing non-invasively. Electrophysiological modeling of the respiratory cycle is a valuable tool for analysis of the signals. A promising approach for dynamic simulations is based on knowing the deformation of the torso at a finite number of time steps between expiration and inspiration. In order to provide a foundation for such models, we present a new image registration method that determines the torso transformation during the respiratory cycle. For this purpose, we extend a ResNet-LDDMM based 3D/3D registration approach. We modify the network structure and add 2D data taken during respiration into the registration to include information about the breathing motion. Our experiments show that these modifications improve the registration quality, thereby providing a step towards a more realistic model of electrical transfer behavior over the respiratory cycle. The code is publicly available at https://github.com/schulz-p/Image-Registration-for-a-Dynamic-Breathing-Model.

UR - https://www.mendeley.com/catalogue/54651a7f-6e79-3de4-baf9-cb60bcee22af/

U2 - 10.1007/978-3-658-47422-5_4

DO - 10.1007/978-3-658-47422-5_4

M3 - Konferenzbeitrag

SN - 978-365847421-8

T3 - Informatik aktuell

SP - 5

EP - 11

BT - Bildverarbeitung für die Medizin

A2 - Palm, C

A2 - Breininger, K

A2 - Deserno, T

A2 - Handels, H

A2 - Maier, A

A2 - Maier-Hein, K.H.

A2 - Tolxdorff, T.M.

PB - Springer Science and Business Media Deutschland GmbH

ER -

Schulz PF, Oltmann A, Bostelmann J , Gildemeister O, Wegner F, Lellmann J et al. Image Registration for a Dynamic Breathing Model. In Palm C, Breininger K, Deserno T, Handels H, Maier A, Maier-Hein KH, Tolxdorff TM, editors, Bildverarbeitung für die Medizin. Springer Science and Business Media Deutschland GmbH. 2025. p. 5-11. (Informatik aktuell). doi: 10.1007/978-3-658-47422-5_4

Image Registration for a Dynamic Breathing Model

Abstract

Research Areas and Centers

DFG Research Classification Scheme

UN SDGs

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