Int J Comput Assist Radiol Surg - Catheter tip tracking for MR-guided interventions using discrete Kalman filter and mean shift localization.

Tópicos

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Resumo

RPOSE: This paper presents and evaluates stochastic computer algorithms used to automatically detect and track marked catheter tip during MR-guided catheterization. The algorithms developed employ extraction and matching of regional features of the catheter tip to perform the localization.METHOD: To perform the tracking, a probability map that indicates the possible locations of the catheter tip in the MR images is first generated. This map is generated from the similarity to a given marker template. The method to assess the similarity between the marker template image and the different positions on each MR frame is based on speeded-up robust features extracted from the gradient image. The probability map is then used in two different stochastic localization frameworks mean shift (MS) localization and Kalman filter (KF) to track the position of the catheter using pairs of orthogonal projection of 2D MR images. The algorithm developed was tested on catheter tip marked with LC resonant circuit (of size 2 mm x 2 cm) tuned to the Larmor frequency of the MRI scanner and for different image resolutions (1, 3, 5 and 7 mm squared pixel).RESULTS: The tracking performance was very robust for the two algorithms MS and KF with image resolution as low as 3 mm where the localization error was about 1 mm for KF and 0.9 mm for MS. For the 5-mm resolution images, the error was 2.2 mm for both KF and MS, and for the 7-mm resolution images, the error was 3.6 and 3.7 mm for KF and MS, respectively.CONCLUSION: Both KF and MS gave comparable results when it comes to accuracy for the different image resolutions. The results showed that the two tracking algorithms tracked the catheter tip with high robustness for image resolution of 3 mm and with acceptable reliability for image resolution as poor as 5 mm with the resonant marker configuration used.

Resumo Limpo

rpose paper present evalu stochast comput algorithm use automat detect track mark cathet tip mrguid catheter algorithm develop employ extract match region featur cathet tip perform localizationmethod perform track probabl map indic possibl locat cathet tip mr imag first generat map generat similar given marker templat method assess similar marker templat imag differ posit mr frame base speededup robust featur extract gradient imag probabl map use two differ stochast local framework mean shift ms local kalman filter kf track posit cathet use pair orthogon project d mr imag algorithm develop test cathet tip mark lc reson circuit size mm x cm tune larmor frequenc mri scanner differ imag resolut mm squar pixelresult track perform robust two algorithm ms kf imag resolut low mm local error mm kf mm ms mm resolut imag error mm kf ms mm resolut imag error mm kf ms respectivelyconclus kf ms gave compar result come accuraci differ imag resolut result show two track algorithm track cathet tip high robust imag resolut mm accept reliabl imag resolut poor mm reson marker configur use

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