Quantitative error analysis for computer assisted navigation: A feasibility study

Güler, Özgür; Perwög, Martina; Kral, Florian; Schwarm, Fritz-Walter; Bárdosi, Z; Göbel, Georg; Freysinger, Wolfgang

doi:10.1118/1.4773871

Cited by 19 publications

(27 citation statements)

References 74 publications

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“…Since measurement errors had occurred that we were not aware of in [16] (see "Data inspection and analysis" section), the results of the ULE are reported here correctly, calculated with formulae 8 and 9.…”

Section: Ulementioning

confidence: 75%

“…The present work extends [16] with a comprehensive analysis of the data by including isotropic and anisotropic errors of measurements, registrations, and prediction methods. The emphasis is on the most frequently used prediction method [6] or methods that fit the simulated surgery best: anisotropic prediction [8] and a general approach [10].…”

Section: Introductionmentioning

confidence: 99%

“…[6]; m is the number of fiducials used for registration. (c) The target localization error (TLE) is the error made in localizing the target (the probe is placed at target r, but the system reports target r [26] [16] anisotropy of fiducials, measurements, and the setup was detected. Thus, it is clear that anisotropic registration had to be used for this analysis.…”

Section: Formentioning

confidence: 99%

“…The user localization error (ULE) is the pure user error of placing the probe on a fiducial and has already been defined and evaluated in [16]. Two different approaches were defined: Predict the ULE F with TTE F,FRE or calculate the ULE with measured errors (TFLE, FLE img , FLE tracker , FLE probecalib ):…”

Section: Ulementioning

confidence: 99%

“…The first raw analysis of the data presented in this paper has already been published in [16], where only isotropic registration with an isotropic FLE model was investigated. The present work extends [16] with a comprehensive analysis of the data by including isotropic and anisotropic errors of measurements, registrations, and prediction methods.…”

Section: Introductionmentioning

confidence: 99%

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Experimental validation of predicted application accuracies for computer-assisted (CAS) intraoperative navigation with paired-point registration

2017

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Purpose The target registration error (TRE) is a crucial parameter to estimate the potential usefulness of computerassisted navigation intraoperatively. Both image-to-patient registration on base of rigid-body registration and TRE prediction methods are available for spatially isotropic and anisotropic data. This study presents a thorough validation of data obtained in an experimental operating room setting with CT images. Methods Optical tracking was used to register a plastic skull, an anatomic specimen, and a volunteer to their respective CT images. Plastic skull and anatomic specimen had implanted bone fiducials for registration; the volunteer was registered with anatomic landmarks. Fiducial localization error, fiducial registration error, and total target error (TTE) were measured; the TTE was compared to isotropic and anisotropic error prediction models. Numerical simulations of the experiment were done additionally. Results The user localization error and the TTE were measured and calculated using predictions, both leading to results as expected for anatomic landmarks and screws used as fiducials. TRE/TTE is submillimetric for the plastic skull and the anatomic specimen. In the experimental data a medium correlation was found between TRE and target localization error (TLE). Most of the predictions of the application accuracy (TRE) fall in the 68% confidence interval of the measured TTE. For the numerically simulated data, a prediction of TTE was not possible; TRE and TTE show a negligible correlation. B Martina Perwög

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Section: Ulementioning

confidence: 75%

Section: Introductionmentioning

confidence: 99%

Section: Formentioning

confidence: 99%

Section: Ulementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Experimental validation of predicted application accuracies for computer-assisted (CAS) intraoperative navigation with paired-point registration

2017

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Vector field analysis for surface registration in computer‐assisted ENT surgery

Diakov

Freysinger

2019

Robotics Computer Surgery

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Background Manual paired‐point registration for navigated ENT‐surgery is prone to human errors; automatic surface registration is often caught in local minima. Methods Anatomical features of the human occiput are integrated into an algorithm for surface registration. A vector force field is defined between the patient and operating room datasets; registration is facilitated through gradient‐based vector field analysis optimization of an energy function. The method is validated exemplarily on patient surface data provided by a mechanically positioned A‐mode ultrasound sensor. Results Successful registrations were achieved within the entire parameter space, as well as from positions of local minima that were found by the Gaussian fields algorithm for surface registration. Sub‐millimetric registration error was measured in clinically relevant anatomical areas on the anterior skull and within the generally accepted margin of 1.5 mm for the entire head. Conclusion The satisfactory behavior of this approach potentially suggests a wider clinical integration.

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Facial landmark‐guided surface matching for image‐to‐patient registration with an RGB‐D camera

Sun

Hosny

et al. 2022

Robotics Computer Surgery

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Background: Fiducial marker-based image-to-patient registration is the most common way in image-guided neurosurgery, which is labour-intensive, time consuming, invasive and error prone. Methods:We proposed a method of facial landmark-guided surface matching for image-to-patient registration using an RGB-D camera. Five facial landmarks are localised from preoperative magnetic resonance (MR) images using deep learning and RGB image using Adaboost with multi-scale block local binary patterns, respectively. The registration of two facial surface point clouds derived from MR images and RGB-D data is initialised by aligning these five landmarks and further refined by weighted iterative closest point algorithm.Results: Phantom experiment results show the target registration error is less than 3 mm when the distance from the camera to the phantom is less than 1000 mm. The registration takes less than 10 s. Conclusions:The proposed method is comparable to the state-of-the-arts in terms of the accuracy yet more time-saving and non-invasive.

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Quantitative error analysis for computer assisted navigation: A feasibility study

Cited by 19 publications

References 74 publications

Experimental validation of predicted application accuracies for computer-assisted (CAS) intraoperative navigation with paired-point registration

Experimental validation of predicted application accuracies for computer-assisted (CAS) intraoperative navigation with paired-point registration

Vector field analysis for surface registration in computer‐assisted ENT surgery

Facial landmark‐guided surface matching for image‐to‐patient registration with an RGB‐D camera

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