A Framework for Semi-automatic Fiducial Localization in Volumetric Images

Abstract

Abstract. Fiducial localization in volumetric images is a common task performed by image-guided navigation and augmented reality systems. These systems often rely on fiducials for image-space to physical-space registration, or as easily identifiable structures for registration validation purposes. Automated methods for fiducial localization in volumetric images are available. Unfortunately, these methods are not generalizable as they explicitly utilize strong a priori knowledge such as fiducial intensity values in CT, or known spatial configurations as part of the algorithm. Thus, manual localization has remained the most general approach, readily applicable across fiducial types and imaging modalities. The main drawbacks of manual localization are the variability and accuracy errors associated with visual localization. We describe a semi-automatic fiducial localization approach that combines the strengths of the human operator and an underlying computational system. The operator identifies the rough location of the fiducial, and the computational system accurately localizes it via intensity based registration using the mutual information similarity measure. This approach is generic, implicitly accommodating for all fiducial types and imaging modalities. The framework was evaluated using five fiducial types and three imaging modalities. We obtained a maximal localization accuracy error of 0.35mm, with a maximal precision variability of 0.5mm.

T-Phantom: a New Phantom Design for Neurosurgical Robotics

“…1h. Other phantom designs include gel based phantoms [15] dedicated for MRI scanning, special phantoms with divots [16], hollow cylindrical phantoms [17] with different target materials and resin based phantoms [18] with implantable titanium screws.…”

“…Angular error Anthropomorphic phantom Neuromate® [7] yes no yes no no yes Neuromate® [8] yes no yes no no yes Pathfinder [9] yes no yes no no no Pathfinder [4] yes yes yes no no yes ROSA™ [10] yes yes yes yes no no MARS [11] yes yes yes no no yes NeuroMaster [12] yes no yes no no no CRW™ frame [13] no yes yes no no yes Leksell® frame [14] yes yes yes no no no Gel based phantom [15] no yes yes no no no Phantom with divots [16] It can be observed that only the ROSA™ phantom design enables simultaneous measurements of both errors in target and entry points. The ROSA phantom design with hollow cylinders does not enable objective quantitative measurements but only measurements of type "better than".…”

Automated fiducial marker detection and localization in volumetric computed tomography images: a three-step hybrid approach with deep learning

“…Attempts with automated detection algorithms to localize fiducial markers in image space are known. 9 – 21 As demonstrated in clinical interventions, 1 , 19 automating this task can not only provide reproducible results, reduce human errors, and shorten intervention time but also lead to lower FLEs. Wang et al.…”

“…The same marker, among others with cylindrical and spherical shapes, were employed in a universal semi-automatic solution by Nagy et al. 18 The localization method utilizes an a priori fiducial model (with known actual point) that is co-registered to a local region of interest (manually marked) containing the fiducial marker in the image.…”

“…In this paper, several approaches for image FLE estimation are reported. Some authors 12 , 15 , 16 , 18 , 20 assess localization accuracy by verifying the measured position with the position detected by an individual. It was experimentally shown that the individuals are conditioned to deviate from the real ground-truth positions.…”