RESUMEN
BACKGROUND: Rigid image coregistration is an established technique that allows spatial aligning. However, rigid fusion is prone to deformation of the imaged anatomies. In this work, a novel fully automated elastic image registration method is evaluated. METHODS: Cervical CT and MRI data of 10 patients were evaluated. The MRI was acquired with the patient in neutral, flexed, and rotated head position. Vertebrawise rigid fusions were performed to transfer bony landmarks for each vertebra from the CT to the MRI space serving as a reference. RESULTS: Elastic fusion of 3D MRI data showed the highest image registration accuracy (target registration error of 3.26 mm with 95% confidence). Further, an elastic fusion of 2D axial MRI data (<4.75 mm with 95% c.) was more reliable than for 2D sagittal sequences (<6.02 mm with 95% c.). CONCLUSIONS: The novel method enables elastic MRI-to-CT image coregistration for cervical indications with changes of the head position.
Asunto(s)
Vértebras Cervicales/diagnóstico por imagen , Procesamiento de Imagen Asistido por Computador/métodos , Reconocimiento de Normas Patrones Automatizadas , Algoritmos , Artefactos , Automatización , Elasticidad , Humanos , Interpretación de Imagen Asistida por Computador/métodos , Imagenología Tridimensional/métodos , Imagen por Resonancia Magnética , Proyectos Piloto , Columna Vertebral , Tomografía Computarizada por Rayos XRESUMEN
INTRODUCTION: In the treatment of cancer in the head and neck region, computer-assisted surgery can be used to estimate location and extent by segmentation of the tumor. This article presents a new tool (Smartbrush), which allows for faster automated segmentation of the tumor. METHODS: This new method was compared with other well-known techniques of segmentation. Thirty-eight patients with keratocystic odontogenic tumors were included in this study. The tumors were segmented using manual segmentation, threshold-based segmentation and segmentation using Smartbrush. All three methods were compared concerning usability, time expenditure and accuracy. RESULTS: The results suggest that segmentation using Smartbrush is significantly faster with comparable accuracy. CONCLUSIONS: After a period of adjustment to the program, one can comfortably get reliable results that, compared with other methods, are not as dependent on the user's experience. Smartbrush segmentation is a reliable and fast method of segmentation in tumor surgery.
