Improved electromagnetic tracking for catheter path reconstruction with application in high-dose-rate brachytherapy.

PURPOSE: Electromagnetic (EM) catheter tracking has recently been introduced in order to enable prompt and uncomplicated reconstruction of catheter paths in various clinical interventions. However, EM tracking is prone to measurement errors which can compromise the outcome of the procedure. Minimizing catheter tracking errors is therefore paramount to improve the path reconstruction accuracy. METHODS: An extended Kalman filter (EKF) was employed to combine the nonlinear kinematic model of an EM sensor inside the catheter, with both its position and orientation measurements. The formulation of the kinematic model was based on the nonholonomic motion constraints of the EM sensor inside the catheter. Experimental verification was carried out in a clinical HDR suite. Ten catheters were inserted with mean curvatures varying from 0 to [Formula: see text] in a phantom. A miniaturized Ascension (Burlington, Vermont, USA) trakSTAR EM sensor (model 55) was threaded within each catheter at various speeds ranging from 7.4 to [Formula: see text]. The nonholonomic EKF was applied on the tracking data in order to statistically improve the EM tracking accuracy. A sample reconstruction error was defined at each point as the Euclidean distance between the estimated EM measurement and its corresponding ground truth. A path reconstruction accuracy was defined as the root mean square of the sample reconstruction errors, while the path reconstruction precision was defined as the standard deviation of these sample reconstruction errors. The impacts of sensor velocity and path curvature on the nonholonomic EKF method were determined. Finally, the nonholonomic EKF catheter path reconstructions were compared with the reconstructions provided by the manufacturer's filters under default settings, namely the AC wide notch and the DC adaptive filter. RESULTS: With a path reconstruction accuracy of 1.9 mm, the nonholonomic EKF surpassed the performance of the manufacturer's filters (2.4 mm) by 21% and the raw EM measurements (3.5 mm) by 46%. Similarly, with a path reconstruction precision of 0.8 mm, the nonholonomic EKF surpassed the performance of the manufacturer's filters (1.0 mm) by 20% and the raw EM measurements (1.7 mm) by 53%. Path reconstruction accuracies did not follow an apparent trend when varying the path curvature and sensor velocity; instead, reconstruction accuracies were predominantly impacted by the position of the EM field transmitter ([Formula: see text]). CONCLUSION: The advanced nonholonomic EKF is effective in reducing EM measurement errors when reconstructing catheter paths, is robust to path curvature and sensor speed, and runs in real time. Our approach is promising for a plurality of clinical procedures requiring catheter reconstructions, such as cardiovascular interventions, pulmonary applications (Bender et al. in medical image computing and computer-assisted intervention-MICCAI 99. Springer, Berlin, pp 981-989, 1999), and brachytherapy.

Simultaneous Electromagnetic Tracking and Calibration for Dynamic Field Distortion Compensation.

Electromagnetic (EM) tracking systems are highly susceptible to field distortion. The interference can cause measurement errors up to a few centimeters in clinical environments, which limits the reliability of these systems. Unless corrected for, this measurement error imperils the success of clinical procedures. It is therefore fundamental to dynamically calibrate EM tracking systems and compensate for measurement error caused by field distorting objects commonly present in clinical environments. We propose to combine a motion model with observations of redundant EM sensors and compensate for field distortions in real time. We employ a simultaneous localization and mapping technique to accurately estimate the pose of the tracked instrument while creating the field distortion map. We conducted experiments with six degrees-of-freedom motions in the presence of field distorting objects in research and clinical environments. We applied our approach to improve the EM tracking accuracy and compared our results to a conventional sensor fusion technique. Using our approach, the maximum tracking error was reduced by 67% for position measurements and by 64% for orientation measurements. Currently, clinical applications of EM trackers are hampered by the adverse distortion effects. Our approach introduces a novel method for dynamic field distortion compensation, independent from preoperative calibrations or external tracking devices, and enables reliable EM navigation for potential applications.

Simultaneous localization and calibration for electromagnetic tracking systems.

BACKGROUND: In clinical environments, field distortion can cause significant electromagnetic tracking errors. Therefore, dynamic calibration of electromagnetic tracking systems is essential to compensate for measurement errors. METHODS: It is proposed to integrate the motion model of the tracked instrument with redundant EM sensor observations and to apply a simultaneous localization and mapping algorithm in order to accurately estimate the pose of the instrument and create a map of the field distortion in real-time. Experiments were conducted in the presence of ferromagnetic and electrically-conductive field distorting objects and results compared with those of a conventional sensor fusion approach. RESULTS: The proposed method reduced the tracking error from 3.94±1.61 mm to 1.82±0.62 mm in the presence of steel, and from 0.31±0.22 mm to 0.11±0.14 mm in the presence of aluminum. CONCLUSIONS: With reduced tracking error and independence from external tracking devices or pre-operative calibrations, the approach is promising for reliable EM navigation in various clinical procedures. Copyright © 2015 John Wiley & Sons, Ltd.

Electromagnetic tracking in surgical and interventional environments: usability study.

PURPOSE: Electromagnetic (EM) tracking of instruments within a clinical setting is notorious for fluctuating measurement performance. Position location measurement uncertainty of an EM system was characterized in various environments, including control, clinical, cone beam computed tomography (CBCT), and CT scanner environments. Static and dynamic effects of CBCT and CT scanning on EM tracking were evaluated. METHODS: Two guidance devices were designed to solely translate or rotate the sensor in a non-interfering fit to decouple pose-dependent tracking uncertainties. These devices were mounted on a base to allow consistent and repeatable tests when changing environments. Using this method, position and orientation measurement accuracies, precision, and 95 % confidence intervals were assessed. RESULTS: The tracking performance varied significantly as a function of the environment-especially within the CBCT and CT scanners-and sensor pose. In fact, at a fixed sensor position in the clinical environment, the measurement error varied from 0.2 to 2.2 mm depending on sensor orientations. Improved accuracies were observed along the vertical axis of the field generator. Calibration of the measurements improved tracking performance in the CT environment by 50-85 %. CONCLUSION: EM tracking can provide effective assistance to surgeons or interventional radiologists during procedures performed in a clinical or CBCT environment. Applications in the CT scanner demand precalibration to provide acceptable performance.

Needle deflection estimation: prostate brachytherapy phantom experiments.

PURPOSE: The performance of a fusion-based needle deflection estimation method was experimentally evaluated using prostate brachytherapy phantoms. The accuracy of the needle deflection estimation was determined. The robustness of the approach with variations in needle insertion speed and soft tissue biomechanical properties was investigated. METHODS: A needle deflection estimation method was developed to determine the amount of needle bending during insertion into deformable tissue by combining a kinematic deflection model with measurements taken from two electromagnetic trackers placed at the tip and the base of the needle. Experimental verification of this method for use in prostate brachytherapy needle insertion procedures was performed. A total of 21 beveled tip, 18 ga, 200 mm needles were manually inserted at various speeds through a template and toward different targets distributed within 3 soft tissue mimicking polyvinyl chloride prostate phantoms of varying stiffness. The tracked positions of both the needle tip and base were recorded, and Kalman filters were applied to fuse the sensory information. The estimation results were validated using ground truth obtained from fluoroscopy images. RESULTS: The manual insertion speed ranged from 8 to 34 mm/s, needle deflection ranged from 5 to 8 mm at an insertion depth of 76 mm, and the elastic modulus of the soft tissue ranged from 50 to 150 kPa. The accuracy and robustness of the estimation method were verified within these ranges. When compared to purely model-based estimation, we observed a reduction in needle tip position estimation error by [Formula: see text] % (mean [Formula: see text] SD) and the cumulative deflection error by [Formula: see text] %. CONCLUSIONS: Fusion of electromagnetic sensors demonstrated significant improvement in estimating needle deflection compared to model-based methods. The method has potential clinical applicability in the guidance of needle placement medical interventions, particularly prostate brachytherapy.

Electromagnetic tracking performance analysis and optimization.

PURPOSE: The purpose of this study is to evaluate the uncertainties of an electromagnetic (EM) tracking system and to improve both the trueness and the precision of the EM tracker. METHODS: For evaluating errors, we introduce an optical (OP) tracking system and consider its measurement as "ground truth". In the experiment, static data sets and dynamic profiles are collected in both relatively less-metallic environments. Static data sets are for error modeling, and dynamic ones are for testing. To improve the trueness and precision of the EM tracker, tracker calibration based on polynomial fitting and smooth filters, such as the Kalman filter, the moving average filter and the local regression filter, are deployed. RESULTS: From the experimental data analysis, as the distance between the transmitter and the sensor of the EM tracking system increases, the trueness and precision tend to decrease. The system's trueness and jitter errors can be modeled as the 3(rd) order polynomial error equations. After minimizing the positional error and applying smoothing filters, the mean value of error reduction is 36.9%. CONCLUSION: Our method can effectively reduce both positional systematic error and jitter error caused by EM field distortion. The method is successfully applied to calibrate an EM tracked surgical cautery tool.

Fusion of electromagnetic trackers to improve needle deflection estimation: simulation study.

We present a needle deflection estimation method to anticipate needle bending during insertion into deformable tissue. Using limited additional sensory information, our approach reduces the estimation error caused by uncertainties inherent in the conventional needle deflection estimation methods. We use Kalman filters to combine a kinematic needle deflection model with the position measurements of the base and the tip of the needle taken by electromagnetic (EM) trackers. One EM tracker is installed on the needle base and estimates the needle tip position indirectly using the kinematic needle deflection model. Another EM tracker is installed on the needle tip and estimates the needle tip position through direct, but noisy measurements. Kalman filters are then employed to fuse these two estimates in real time and provide a reliable estimate of the needle tip position, with reduced variance in the estimation error. We implemented this method to compensate for needle deflection during simulated needle insertions and performed sensitivity analysis for various conditions. At an insertion depth of 150 mm, we observed needle tip estimation error reductions in the range of 28% (from 1.8 to 1.3 mm) to 74% (from 4.8 to 1.2 mm), which demonstrates the effectiveness of our method, offering a clinically practical solution.