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Fast and accurate localization of multiple RF markers for tracking in MRI-guided interventions.

Galassi F, Brujic D, Rea M, Lambert N, Desouza N, Ristic M - MAGMA (2014)

Bottom Line: Computational complexity was significantly reduced by avoiding cluster analysis, while higher accuracy was achieved by using optimal projections and by applying Gaussian interpolation in peak detection.The computational time for 6 markers was better than 2 ms, an improvement of up to 100 times, compared to the method by Flask et al. (J Magn Reson Imaging 14(5):617-627, 2001).The proposed method is particularly suitable in systems requiring any of the following: high frame rate, tracking of three or more markers, data filtering or interleaving.

View Article: PubMed Central - PubMed

Affiliation: Mechanical Engineering Department, Imperial College London, London, UK, f.galassi09@imperial.ac.uk.

ABSTRACT

Object: A new method for 3D localization of N fiducial markers from 1D projections is presented and analysed. It applies to semi-active markers and active markers using a single receiver channel.

Materials and methods: The novel algorithm computes candidate points using peaks in three optimally selected projections and removes fictitious points by verifying detected peaks in additional projections. Computational complexity was significantly reduced by avoiding cluster analysis, while higher accuracy was achieved by using optimal projections and by applying Gaussian interpolation in peak detection. Computational time, accuracy and robustness were analysed through Monte Carlo simulations and experiments. The method was employed in a prototype MRI guided prostate biopsy system and used in preclinical experiments.

Results: The computational time for 6 markers was better than 2 ms, an improvement of up to 100 times, compared to the method by Flask et al. (J Magn Reson Imaging 14(5):617-627, 2001). Experimental maximum localization error was lower than 0.3 mm; standard deviation was 0.06 mm. Targeting error was about 1 mm. Tracking update rate was about 10 Hz.

Conclusion: The proposed method is particularly suitable in systems requiring any of the following: high frame rate, tracking of three or more markers, data filtering or interleaving.

Show MeSH
Maximum error as a function of the number of projections,  simulations
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Fig11: Maximum error as a function of the number of projections, simulations

Mentions: However, the results in Table 1 should be considered in relation to the accuracy of results in Fig. 11, showing the variation of the maximum error as a function of the number of projections.Fig. 11


Fast and accurate localization of multiple RF markers for tracking in MRI-guided interventions.

Galassi F, Brujic D, Rea M, Lambert N, Desouza N, Ristic M - MAGMA (2014)

Maximum error as a function of the number of projections,  simulations
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4315881&req=5

Fig11: Maximum error as a function of the number of projections, simulations
Mentions: However, the results in Table 1 should be considered in relation to the accuracy of results in Fig. 11, showing the variation of the maximum error as a function of the number of projections.Fig. 11

Bottom Line: Computational complexity was significantly reduced by avoiding cluster analysis, while higher accuracy was achieved by using optimal projections and by applying Gaussian interpolation in peak detection.The computational time for 6 markers was better than 2 ms, an improvement of up to 100 times, compared to the method by Flask et al. (J Magn Reson Imaging 14(5):617-627, 2001).The proposed method is particularly suitable in systems requiring any of the following: high frame rate, tracking of three or more markers, data filtering or interleaving.

View Article: PubMed Central - PubMed

Affiliation: Mechanical Engineering Department, Imperial College London, London, UK, f.galassi09@imperial.ac.uk.

ABSTRACT

Object: A new method for 3D localization of N fiducial markers from 1D projections is presented and analysed. It applies to semi-active markers and active markers using a single receiver channel.

Materials and methods: The novel algorithm computes candidate points using peaks in three optimally selected projections and removes fictitious points by verifying detected peaks in additional projections. Computational complexity was significantly reduced by avoiding cluster analysis, while higher accuracy was achieved by using optimal projections and by applying Gaussian interpolation in peak detection. Computational time, accuracy and robustness were analysed through Monte Carlo simulations and experiments. The method was employed in a prototype MRI guided prostate biopsy system and used in preclinical experiments.

Results: The computational time for 6 markers was better than 2 ms, an improvement of up to 100 times, compared to the method by Flask et al. (J Magn Reson Imaging 14(5):617-627, 2001). Experimental maximum localization error was lower than 0.3 mm; standard deviation was 0.06 mm. Targeting error was about 1 mm. Tracking update rate was about 10 Hz.

Conclusion: The proposed method is particularly suitable in systems requiring any of the following: high frame rate, tracking of three or more markers, data filtering or interleaving.

Show MeSH