Georg Hille1, Sylvia Saalfeld2, Steffen Serowy3, Klaus Tönnies2. 1. Department of Simulation and Graphics, University of Magdeburg, Universitätsplatz 2, 39106, Magdeburg, Germany. Electronic address: georg.hille@ovgu.de. 2. Department of Simulation and Graphics, University of Magdeburg, Universitätsplatz 2, 39106, Magdeburg, Germany. 3. Department of Neuroradiology, University Hospital of Magdeburg, Leipziger Straße 44, 39120, Magdeburg, Germany.
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.
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.
Authors: Yunliang Cai; Shaoju Wu; Xiaoyao Fan; Jonathan Olson; Linton Evans; Scott Lollis; Sohail K Mirza; Keith D Paulsen; Songbai Ji Journal: Int J Comput Assist Radiol Surg Date: 2021-05-10 Impact factor: 3.421