Integration of model-based weighting into an ICP variant to account for measurement errors in intra-operative A-Mode ultrasound-based registration

Fieten, Lorenz; Radermacher, Klaus; Kernenbach, Manuel A.; Heger, Stefan

doi:10.1109/iembs.2010.5628071

Cited by 5 publications

(10 citation statements)

References 17 publications

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“…However, we hypothesized that a more effective andwhen applied to anisotropic weighting -more effi cient strategy for minimization of the cost function could be found by combining the abovementioned weighting schemes in a way similar to that presented in [17] . In this strategy, at fi rst, 33 trials of the originally proposed R-ICP with random-sampling (i.e., binary weights) are performed.…”

Section: Resultsmentioning

confidence: 98%

“…Therefore, a straightforward extension including isotropic and inhomogeneous (see [17] ) or anisotropic and inhomogeneous weighting is obtained by combining these weighting schemes (instead of isotropic and homogeneous weighting) with random-sampling.…”

Section: Resultsmentioning

confidence: 99%

“…(3) only yields an approximation of the maximum likelihood estimate for R and t [17] . Whereas a closed-form solution for minimization of Eq.…”

Section: Point-to-point Registration With Anisotropic Weightingmentioning

confidence: 99%

“…Hence, different from the ICP, the R-ICP does not easily get stuck in a local minimum. For details regarding the R-ICP, the reader is referred to our previous publications [16,17] .…”

Section: Random-icp Algorithm With Anisotropic Weightingmentioning

confidence: 99%

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An ICP variant with anisotropic weighting to accommodate measurement errors in A-Mode ultrasound-based registration

Fieten

Radermacher

Heger

2012

Biomedizinische Technik/Biomedical Engineering

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In computer-assisted surgery, navigation based on pre-operative images and intra-operative tracking requires fusion of data from different coordinate systems. Intra-operative registration is used to determine the spatial relationship between these coordinate systems. Feature-based registration methods rely on reference structures localized both in the pre-operative images and in the surgical site. Optically tracked A-Mode ultrasound (US) allows for non-invasive and cost-efficient digitization of bone surface points needed as input data for registration algorithms. It is especially attractive in combination with surface-based registration algorithms, such as the Iterative Closest Point algorithm and its variants, because they automatically localize the corresponding points, which are covered by soft tissue and, hence, not visible in the site. However, as transcutaneous palpation relies on some assumptions, e.g., regarding the average speed of sound of the scanned soft tissue, that are only partly justified in practice, errors occur that make transcutaneous palpation less reliable than direct palpation. Furthermore, optical tracking causes errors that have to be considered, especially if a so-called dynamic reference base is attached to the patient. The present work investigates how to reduce the effect of important error sources in A-Mode US-based registration. The major contributions are techniques for application-specific error modeling and new methods for surface-based registration with anisotropic weighting. This includes a Newton-like optimization scheme for point-to-point registration and a modified kd-tree-based algorithm for closest point computation. Various combinations of registration algorithms and modeling techniques are tested in a simulation study addressing periacetabular osteotomy, and it is clearly demonstrated that standard methods are not recommendable. On the contrary, the new algorithms allow for a substantial increase in registration accuracy and encourage further research in that field.

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Section: Resultsmentioning

confidence: 98%

Section: Resultsmentioning

confidence: 99%

“…(3) only yields an approximation of the maximum likelihood estimate for R and t [17] . Whereas a closed-form solution for minimization of Eq.…”

Section: Point-to-point Registration With Anisotropic Weightingmentioning

confidence: 99%

“…Hence, different from the ICP, the R-ICP does not easily get stuck in a local minimum. For details regarding the R-ICP, the reader is referred to our previous publications [16,17] .…”

Section: Random-icp Algorithm With Anisotropic Weightingmentioning

confidence: 99%

See 2 more Smart Citations

An ICP variant with anisotropic weighting to accommodate measurement errors in A-Mode ultrasound-based registration

Fieten

Radermacher

Heger

2012

Biomedizinische Technik/Biomedical Engineering

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“…Image-based approaches to correct these errors can perform a segmentation of regions with different speeds of sound [13]. Other approaches take the unreliability of measurements into account by modifying the registration algorithm [2,10].…”

Section: Introductionmentioning

confidence: 99%

System architecture for intraoperative ultrasound registration in image-based medical navigation

Dekomien¹,

Roeschies²,

Winter³

2012

Biomedizinische Technik/Biomedical Engineering

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Medical navigation systems for orthopedic surgery are becoming more and more important with the increasing proportion of older people in the population, and hence the increasing incidence of diseases of the musculoskeletal system. The central problem for such systems is the exact transformation of the preoperatively acquired datasets to the coordinate system of the patient's body, which is crucial for the accuracy of navigation. Our approach, based on the use of intraoperative ultrasound for image registration, is capable of robustly registering bone structures for different applications, e.g., at the spine or the knee. Nevertheless, this new procedure demands additional steps of preparation of preoperative data. To increase the clinical acceptance of this procedure, it is useful to automate most of the data processing steps. In this article, we present the architecture of our system with focus on the automation of the data processing steps. In terms of accuracy, a mean target registration error of 0.68 mm was achieved for automatically segmented and registered phantom data where the reference transformation was obtained by performing point-based registration using artificial structures. As the overall accuracy for subject data cannot be determined non-invasively, automatic segmentation and registration were judged by visual inspection and precision, which showed a promising result of 1.76 mm standard deviation for 100 registration trials based on automatic segmentation of magnetic resonance imaging data of the spine.

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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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Integration of model-based weighting into an ICP variant to account for measurement errors in intra-operative A-Mode ultrasound-based registration

Cited by 5 publications

References 17 publications

An ICP variant with anisotropic weighting to accommodate measurement errors in A-Mode ultrasound-based registration

An ICP variant with anisotropic weighting to accommodate measurement errors in A-Mode ultrasound-based registration

System architecture for intraoperative ultrasound registration in image-based medical navigation

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

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