High-precision evaluation of electromagnetic tracking

Kügler, David; Krumb, Henry; Bredemann, Judith; Stenin, Igor; Kristin, Julia; Klenzner, Thomas; Schipper, Jörg; Schmitt, Robert; Sakas, Georgios; Mukhopadhyay, Anirban

doi:10.1007/s11548-019-01959-5

Cited by 15 publications

(23 citation statements)

References 17 publications

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“…Positional tracking experiments are performed with an Ascension trakSTAR 3D Guidance system (Northern Digital Inc.) under the use of a 1.8 mm sensor. Positional EMT measurement data are collected on a calibrated Lego measurement phantom (repeatability 20 µm) similar to the one proposed by us earlier [14]. EMT measurements are performed in laboratory and near a Ziehm Vision 3D c-arm fluoroscopy unit.…”

Section: Methodsmentioning

confidence: 99%

“…Training on relative displacements between positions ensures that the exact distance of phantom to the field generator center does not need to be measured [14]. Hence, this approach circumvents the need for a second measurement standard to capture absolute positions, which would contribute to overall measurement uncertainty.…”

Section: Error Compensating Neural Networkmentioning

confidence: 99%

“…We obtain raw quality values by setting the user parameters to S = 1, b = 0, m = 0. For comparison, we implement mixed-term polynomial regression models as proposed by Kügler et al [14]. Both compensation models are trained on pairs of EMT sensor positions and corresponding ground truth distances on the Lego phantom.…”

Section: Error Compensating Neural Networkmentioning

confidence: 99%

“…ANNs with and without quality indicator (Q) are compared to polynomials. Percentages denote error reduction evaluate the polynomial regression model for comparison to the compensation approach proposed by Kügler et al [14].…”

Section: Compensation Of Unseen Distortionsmentioning

confidence: 99%

“…x + σ 2 y for each point (x, y) in the planar full-baseboard dataset. Distributions of model predictions cannot be assumed to be Gaussian (see section 4.1 in [14]), so that the 68-95-99.7 rule for confidence interval approximation does not apply here.…”

Section: Model Uncertainty Evaluationmentioning

confidence: 99%

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Leveraging spatial uncertainty for online error compensation in EMT

Krumb

Hofmann

Kügler³

et al. 2020

Int J CARS

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Purpose Electromagnetic tracking (EMT) can potentially complement fluoroscopic navigation, reducing radiation exposure in a hybrid setting. Due to the susceptibility to external distortions, systematic error in EMT needs to be compensated algorithmically. Compensation algorithms for EMT in guidewire procedures are only practical in an online setting. Methods We collect positional data and train a symmetric artificial neural network (ANN) architecture for compensating navigation error. The results are evaluated in both online and offline scenarios and are compared to polynomial fits. We assess spatial uncertainty of the compensation proposed by the ANN. Simulations based on real data show how this uncertainty measure can be utilized to improve accuracy and limit radiation exposure in hybrid navigation. Results ANNs compensate unseen distortions by more than 70%, outperforming polynomial regression. Working on known distortions, ANNs outperform polynomials as well. We empirically demonstrate a linear relationship between tracking accuracy and model uncertainty. The effectiveness of hybrid tracking is shown in a simulation experiment. Conclusion ANNs are suitable for EMT error compensation and can generalize across unseen distortions. Model uncertainty needs to be assessed when spatial error compensation algorithms are developed, so that training data collection can be optimized. Finally, we find that error compensation in EMT reduces the need for X-ray images in hybrid navigation.

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Section: Methodsmentioning

confidence: 99%

Section: Error Compensating Neural Networkmentioning

confidence: 99%

Section: Error Compensating Neural Networkmentioning

confidence: 99%

Section: Compensation Of Unseen Distortionsmentioning

confidence: 99%

Section: Model Uncertainty Evaluationmentioning

confidence: 99%

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Leveraging spatial uncertainty for online error compensation in EMT

Krumb

Hofmann

Kügler³

et al. 2020

Int J CARS

Self Cite

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A comprehensive quality assurance protocol for electromagnetic tracking in brachytherapy

Dürrbeck,

Gomez‐Sarmiento,

Androulakis

et al. 2024

Medical Physics

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BackgroundElectromagnetic tracking (EMT) systems have proven to be a valuable source of information regarding the location and geometry of applicators in patients undergoing brachytherapy (BT). As an important element of an enhanced and individualized pre‐treatment verification, EMT can play a pivotal role in detecting treatment errors and uncertainties to increase patient safety.PurposeThe purpose of this study is two‐fold: to design, develop and test a dedicated measurement protocol for the use of EMT‐enabled afterloaders in BT and to collect and compare the data acquired from three different radiation oncology centers in different clinical environments.MethodsA novel quality assurance (QA) phantom composed of a scaffold with supports to fix the field generator, different BT applicators, and reference sensors (sensor verification tools) was used to assess the precision (jitter error) and accuracy (relative distance errors and target registration error) of the EMT sensor integrated into an afterloader prototype. Measurements were repeated in different environments where EMT measurements are likely to be performed, namely an electromagnetically clean laboratory, a BT suite, an operating room, and, if available, a CT suite and an MRI suite dedicated to BT.ResultsThe mean positional jitter was consistently under 0.1 mm across all measurement points, with a slight trend of increased jitter at greater distances from the field generator. The mean variability of sensor positioning in the tested tandem and ring gynecological applicator was also below 0.1 mm. The tracking accuracy close to the center of the measurement volume was higher than at its edges. The relative distance error at the center was 0.2‐0.3 mm with maximum values reaching 1.2‐1.8 mm, but up to 5.5 mm for measurement points close to the edges. In general, similar accuracy results were obtained in the clinical environments and in all investigated institutions (median distance error 0.1‐0.4 mm, maximum error 1.0‐2.0 mm), however, errors were found to be larger in the CT suite (median distance error up to 1.0 mm, maximum error up to 3.6 mm).ConclusionThe presented quality assessment protocol for EMT systems in BT has demonstrated that EMT offers a high‐accuracy determination of the applicator/implant geometry even in clinical environments. In addition to that, it has provided valuable insights into the performance of EMT‐enabled afterloaders across different radiation oncology centers.

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A New Electromagnetic-Video Endoscope Tracking Method via Anatomical Constraints and Historically Observed Differential Evolution

Luo

2020

Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

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High-precision evaluation of electromagnetic tracking

Cited by 15 publications

References 17 publications

Leveraging spatial uncertainty for online error compensation in EMT

Leveraging spatial uncertainty for online error compensation in EMT

A comprehensive quality assurance protocol for electromagnetic tracking in brachytherapy

A New Electromagnetic-Video Endoscope Tracking Method via Anatomical Constraints and Historically Observed Differential Evolution

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