Evaluating the Effects of a Priori Deep Learning Image Synthesis on Multi-Modal MR-to-CT Image Registration Performance

Nils Frohwitter, Nils Frohwitter, Alessa Hering, Ralf Möller, Ralf Möller, Mattis Hartwig, Mattis Hartwig

2023

Abstract

Radiation therapy often requires a computed tomography (CT) for treatment planning and an additional magnetic resonance (MR) imaging prior to the treatment for adaptation. With two different images from the same scene, multi-modal image registration is needed to align areas of interest in both images. One idea to improve the registration process is to perform an image synthesis that converts one image mode into another mode prior to the registration. In this paper, we address the research needed to perform a thorough evaluation of the synthesis step on overall registration performance using different well-known registration methods of the Advanced Normalization Tools (ANTs) framework. Given abdominal images, we use CycleGAN for synthesis and compare the registration performance to the one without synthesis by using four different well-known registration methods. We show that good image synthesizing results lead to an average improvement in all registration methods, biggest improvement being achieved for the ‘Symmetric Normalization’ method with 8% (measured with Dice-score). The overall best registration method with prior synthesis is ‘Symmetric Normalization and Rigid’. Furthermore, we show that the images with bad synthetic results lead to worse registration, thus suggesting the correlation between synthesizing quality and registration performance.

Paper Citation

in Harvard Style

Frohwitter N., Hering A., Möller R. and Hartwig M. (2023). Evaluating the Effects of a Priori Deep Learning Image Synthesis on Multi-Modal MR-to-CT Image Registration Performance. In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 5: HEALTHINF; ISBN 978-989-758-631-6, SciTePress, pages 322-329. DOI: 10.5220/0011669000003414

in Bibtex Style

@conference{healthinf23,
author={Nils Frohwitter and Alessa Hering and Ralf Möller and Mattis Hartwig},
title={Evaluating the Effects of a Priori Deep Learning Image Synthesis on Multi-Modal MR-to-CT Image Registration Performance},
booktitle={Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 5: HEALTHINF},
year={2023},
pages={322-329},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011669000003414},
isbn={978-989-758-631-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 5: HEALTHINF
TI - Evaluating the Effects of a Priori Deep Learning Image Synthesis on Multi-Modal MR-to-CT Image Registration Performance
SN - 978-989-758-631-6
AU - Frohwitter N.
AU - Hering A.
AU - Möller R.
AU - Hartwig M.
PY - 2023
SP - 322
EP - 329
DO - 10.5220/0011669000003414
PB - SciTePress