@inbook{87e9b9ce00754d43a8082b7c1561e263,
title = "Towards automated spine mobility quantification: a locally rigid ct to x-ray registration framework",
abstract = "Different pathologies of the vertebral column, such as scoliosis, require quantification of the mobility of individual vertebrae or of curves of the spine for treatment planning. Without the necessary mobility, vertebrae can not be safely re-positioned and fused. The current clinical workflow consists of radiologists or surgeons estimating angular differences of neighbouring vertebrae from different x-ray images. This procedure is time consuming and prone to inaccuracy. The proposed method automates this quantification by deforming a CT image in a physiologically reasonable way and matching it to the x-ray images of interest. We propose a proof of concept evaluation on synthetic data. The automatic and quantitative analysis enables reproducible results independent of the investigator.",
keywords = "3D-2D registration, Mobility quantification, Spine, Vertebra, Volume-projection registration",
author = "David Drobny and Marta Ranzini and Amanda Isaac and Tom Vercauteren and S{\'e}bastien Ourselin and David Choi and Marc Modat",
year = "2020",
month = jan,
day = "1",
doi = "10.1007/978-3-030-50120-4_7",
language = "English",
isbn = "9783030501198",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "SPRINGER",
pages = "67--77",
editor = "Ziga Spiclin and Jamie McClelland and Jan Kybic and Orcun Goksel",
booktitle = "Biomedical Image Registration - 9th International Workshop, WBIR 2020, Proceedings",
note = "9th International Workshop on Biomedical Image Registration, WBIR 2020 ; Conference date: 01-12-2020 Through 02-12-2020",
}