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.