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Continuous registration based on computed tomography for breathing motion compensation
INTRODUCTION: Image guidance for intervention is applied for complex and difficult anatomical regions. Nowadays, it is typically used in neurosurgery, otolaryngology, orthopedics and dentistry. The application of the image-guided system for soft tissues is challenging due to various deformations cau...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Termedia Publishing House
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3908646/ https://www.ncbi.nlm.nih.gov/pubmed/24501595 http://dx.doi.org/10.5114/wiitm.2013.39505 |
Sumario: | INTRODUCTION: Image guidance for intervention is applied for complex and difficult anatomical regions. Nowadays, it is typically used in neurosurgery, otolaryngology, orthopedics and dentistry. The application of the image-guided system for soft tissues is challenging due to various deformations caused by respiratory motion, tissue elasticity and peristalsis. AIM: The main task for the presented approach is continuous registration of preoperative computed tomography (CT) and patient position in the operating room (OR) without touching the patient and compensation of breathing motion. This approach is being developed as a step to image-guided percutaneous liver RF tumor ablation. MATERIAL AND METHODS: Up to ten integrated radiological markers are placed on the patient's skin before CT scans. Then the anatomical model based on CT images is calculated. Point-to-point registration based on the Horn algorithm during a few breathing cycles is performed using a videometric tracking system. The transformation which corresponds to the minimum fiducial registration error (FRE) is found during the registration and it is treated as the initial transformation for calculating local deformation field of breathing motion compensation based on the spline approach. RESULTS: For manual registration of the abdominal phantom, the mean values of target registration error (TRE), fiducial localization error (FLE) and FRE are all below 4 mm for the rigid transformation and are below 1 mm for the affine transformation. For the patient's data they are all below 9 mm and 6 mm, respectively. For the automatic method, different marker configurations have been evaluated while dividing the respiratory cycle into inhale and exhale. Average median values for FRE, TRE rigid estimation and TRE based on spline deformation were 15.56 mm, 0.82 mm and 7.21 mm respectively. CONCLUSIONS: In this application two registration methods of abdominal preoperative CT anatomical model and physical patient position in OR were presented and compared. The presented approach is being developed as a step to image-guided percutaneous liver radiofrequency ablation tumor ablation. Implementation of the automated registration method to clinical practice is easier because of shortening of preparation time in OR, no necessity of touching the patient, and no dependency on the physician's experience. |
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