by Georg Hille, Sylvia Saalfeld, Steffen Serowy, Klaus Tönnies
Abstract:
Background Radiofrequency ablation was introduced recently to treat spinal metastases, which are among the most common metastases. These minimally-invasive interventions are most often image-guided by flat-panel CT scans, withholding soft tissue contrast like MR imaging. Image fusion of diagnostic MR and operative CT images could provide important and useful information during interventions. Method Diagnostic MR and interventional flat-panel CT scans of 19 patients, who underwent radiofrequency ablations of spinal metastases were obtained. Our presented approach piecewise rigidly registers single vertebrae using normalized gradient fields and embeds them within a fused image. Registration accuracy was determined via Euclidean distances between corresponding landmark pairs of ground truth data. Results Our method resulted in an average registration error of 2.35mm. An optimal image fusion performed by landmark registrations achieved an average registration error of 1.70mm. Additionally, intra- and inter-reader variability was determined, resulting in mean distances of corresponding landmark pairs of 1.05mm (MRI) and 1.03mm (flat-panel CT) for the intra-reader variability and 1.36mm and 1.28mm for the inter-reader variability, respectively. Conclusions Our multi-segmental approach with normalized gradient fields as image similarity measure can handle spine deformations due to patient positioning and avoid time-consuming manually performed registration. Thus, our method can provide practical and applicable intervention support without significantly delaying the clinical workflow or additional workload.
Reference:
Multi-segmental spine image registration supporting image-guided interventions of spinal metastases (Georg Hille, Sylvia Saalfeld, Steffen Serowy, Klaus Tönnies), In Computers in Biology and Medicine, volume 102, 2018.
Bibtex Entry:
@article{hille_multi-segmental_2018,
title = {Multi-segmental spine image registration supporting image-guided interventions of spinal metastases},
volume = {102},
issn = {0010-4825},
url = {http://www.sciencedirect.com/science/article/pii/S0010482518302592},
doi = {https://doi.org/10.1016/j.compbiomed.2018.09.003},
abstract = {Background Radiofrequency ablation was introduced recently to treat spinal metastases, which are among the most common metastases. These minimally-invasive interventions are most often image-guided by flat-panel CT scans, withholding soft tissue contrast like MR imaging. Image fusion of diagnostic MR and operative CT images could provide important and useful information during interventions. Method Diagnostic MR and interventional flat-panel CT scans of 19 patients, who underwent radiofrequency ablations of spinal metastases were obtained. Our presented approach piecewise rigidly registers single vertebrae using normalized gradient fields and embeds them within a fused image. Registration accuracy was determined via Euclidean distances between corresponding landmark pairs of ground truth data. Results Our method resulted in an average registration error of 2.35mm. An optimal image fusion performed by landmark registrations achieved an average registration error of 1.70mm. Additionally, intra- and inter-reader variability was determined, resulting in mean distances of corresponding landmark pairs of 1.05mm (MRI) and 1.03mm (flat-panel CT) for the intra-reader variability and 1.36mm and 1.28mm for the inter-reader variability, respectively. Conclusions Our multi-segmental approach with normalized gradient fields as image similarity measure can handle spine deformations due to patient positioning and avoid time-consuming manually performed registration. Thus, our method can provide practical and applicable intervention support without significantly delaying the clinical workflow or additional workload.},
journal = {Computers in Biology and Medicine},
author = {Hille, Georg and Saalfeld, Sylvia and Serowy, Steffen and Tönnies, Klaus},
year = {2018},
keywords = {Automatic image registration, Interventional imaging, Multi-segmental image fusion, Normalized gradient fields, Spine intervention},
pages = {16 -- 20}
}