OBJECTIVE: We compared the application accuracy of an infrared-based neuronavigation system when used with a novel automatic registration with its application accuracy when standard fiducial-based registration is performed. METHODS: The automatic referencing tool is based on markers that are integrated in the headrest holder we routinely use in our intraoperative magnetic resonance imaging (MRI) setting and can be detected by the navigation software automatically. For navigation targeting we used a Plexiglas phantom with 32 notched rods of different heights. The phantom was fixed in the head holder and multiple optimized gradient echo slices containing the clamp-integrated markers were acquired. After that we measured a T1 MPRAGE sequence with a slice thickness of 1.0 mm for navigation. The deepest points of the surface of the rods were defined as target points in image space. In three measurement series we referenced the phantom once with 4, once with 7 fiducials and twice automatically. In one series we performed only one automatic registration. The localization error was measured 3 times per rod and registration. RESULTS: The median localization errors for standard registration with 7 fiducials were between 1.2 and 3.05 mm. With 4 fiducials, medians were in the range from 1.87 to 2.21 mm. For the automatic registration we obtained median localization errors between 0.88 and 2.13 mm. In 6 of the 8 samples that were compared the automatic registration showed an application accuracy that was highly significantly better (p < 0.001 in most cases) than that of fiducial-based standard registration. CONCLUSION: The application accuracy found for automatic referencing is at least not worse than that for standard registration no matter whether 4 or 7 fiducial markers were used. Therefore, its use in the operating room is feasible. In combination with intraoperative MRI it may become a favorable alternative to standard fiducial-based registration especially when an intraoperative update of navigation data is necessary. Copyright 2006 S. Karger AG, Basel.
OBJECTIVE: We compared the application accuracy of an infrared-based neuronavigation system when used with a novel automatic registration with its application accuracy when standard fiducial-based registration is performed. METHODS: The automatic referencing tool is based on markers that are integrated in the headrest holder we routinely use in our intraoperative magnetic resonance imaging (MRI) setting and can be detected by the navigation software automatically. For navigation targeting we used a Plexiglas phantom with 32 notched rods of different heights. The phantom was fixed in the head holder and multiple optimized gradient echo slices containing the clamp-integrated markers were acquired. After that we measured a T1 MPRAGE sequence with a slice thickness of 1.0 mm for navigation. The deepest points of the surface of the rods were defined as target points in image space. In three measurement series we referenced the phantom once with 4, once with 7 fiducials and twice automatically. In one series we performed only one automatic registration. The localization error was measured 3 times per rod and registration. RESULTS: The median localization errors for standard registration with 7 fiducials were between 1.2 and 3.05 mm. With 4 fiducials, medians were in the range from 1.87 to 2.21 mm. For the automatic registration we obtained median localization errors between 0.88 and 2.13 mm. In 6 of the 8 samples that were compared the automatic registration showed an application accuracy that was highly significantly better (p < 0.001 in most cases) than that of fiducial-based standard registration. CONCLUSION: The application accuracy found for automatic referencing is at least not worse than that for standard registration no matter whether 4 or 7 fiducial markers were used. Therefore, its use in the operating room is feasible. In combination with intraoperative MRI it may become a favorable alternative to standard fiducial-based registration especially when an intraoperative update of navigation data is necessary. Copyright 2006 S. Karger AG, Basel.
Authors: Christopher P Favazza; Krzysztof R Gorny; Matthew R Callstrom; Anil N Kurup; Michael Washburn; Pamela S Trester; Charles L Fowler; Nicholas J Hangiandreou Journal: J Appl Clin Med Phys Date: 2018-05-21 Impact factor: 2.102