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Published in: International Journal of Computer Assisted Radiology and Surgery 4/2018

01-04-2018 | Original Article

Kalman filter-based EM-optical sensor fusion for needle deflection estimation

Authors: Baichuan Jiang, Wenpeng Gao, Daniel Kacher, Erez Nevo, Barry Fetics, Thomas C. Lee, Jagadeesan Jayender

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 4/2018

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Abstract

Purpose

In many clinical procedures such as cryoablation that involves needle insertion, accurate placement of the needle’s tip at the desired target is the major issue for optimizing the treatment and minimizing damage to the neighboring anatomy. However, due to the interaction force between the needle and tissue, considerable error in intraoperative tracking of the needle tip can be observed as needle deflects.

Methods

In this paper, measurements data from an optical sensor at the needle base and a magnetic resonance (MR) gradient field-driven electromagnetic (EM) sensor placed 10 cm from the needle tip are used within a model-integrated Kalman filter-based sensor fusion scheme. Bending model-based estimations and EM-based direct estimation are used as the measurement vectors in the Kalman filter, thus establishing an online estimation approach.

Results

Static tip bending experiments show that the fusion method can reduce the mean error of the tip position estimation from 29.23 mm of the optical sensor-based approach to 3.15 mm of the fusion-based approach and from 39.96 to 6.90 mm, at the MRI isocenter and the MRI entrance, respectively.

Conclusion

This work established a novel sensor fusion scheme that incorporates model information, which enables real-time tracking of needle deflection with MRI compatibility, in a free-hand operating setup.
Literature
1.
go back to reference Rossa C, Tavakoli M (2017) Issues in closed-loop needle steering. Control Eng Pract 62:55–69CrossRef Rossa C, Tavakoli M (2017) Issues in closed-loop needle steering. Control Eng Pract 62:55–69CrossRef
2.
go back to reference Abolhassani N, Patel R, Moallem M (2007) Needle insertion into soft tissue: a survey. Med Eng Phys 29(4):413–431CrossRefPubMed Abolhassani N, Patel R, Moallem M (2007) Needle insertion into soft tissue: a survey. Med Eng Phys 29(4):413–431CrossRefPubMed
3.
go back to reference Dupuy DE, Zagoria RJ, Akerley W, Mayo-Smith WW, Kavanagh PV, Safran H (2000) Percutaneous radiofrequency ablation of malignancies in the lung. Am J Roentgenol 174(1):57–59CrossRef Dupuy DE, Zagoria RJ, Akerley W, Mayo-Smith WW, Kavanagh PV, Safran H (2000) Percutaneous radiofrequency ablation of malignancies in the lung. Am J Roentgenol 174(1):57–59CrossRef
4.
go back to reference Park YL, Elayaperumal S, Daniel B, Ryu SC, Shin M, Savall J, Black RJ, Moslehi B, Cutkosky MR (2010) Real-time estimation of 3-D needle shape and deflection for MRI-guided interventions. IEEE/ASME Trans Mechatron 15(6):906–915 Park YL, Elayaperumal S, Daniel B, Ryu SC, Shin M, Savall J, Black RJ, Moslehi B, Cutkosky MR (2010) Real-time estimation of 3-D needle shape and deflection for MRI-guided interventions. IEEE/ASME Trans Mechatron 15(6):906–915
5.
go back to reference Bartynski WS, Grahovac SZ, Rothfus WE (2005) Incorrect needle position during lumbar epidural steroid administration: inaccuracy of loss of air pressure resistance and requirement of fluoroscopy and epidurography during needle insertion. Am J Neuroradiol 26(3):502–505PubMed Bartynski WS, Grahovac SZ, Rothfus WE (2005) Incorrect needle position during lumbar epidural steroid administration: inaccuracy of loss of air pressure resistance and requirement of fluoroscopy and epidurography during needle insertion. Am J Neuroradiol 26(3):502–505PubMed
6.
go back to reference Mala T, Edwin B, Mathisen Ø, Tillung T, Fosse E, Bergan A, SØreide O, Gladhaug I (2004) Cryoablation of colorectal liver metastases: minimally invasive tumour control. Scand J Gastroenterol 39(6):571–578CrossRefPubMed Mala T, Edwin B, Mathisen Ø, Tillung T, Fosse E, Bergan A, SØreide O, Gladhaug I (2004) Cryoablation of colorectal liver metastases: minimally invasive tumour control. Scand J Gastroenterol 39(6):571–578CrossRefPubMed
7.
go back to reference Charboneau JW, Reading CC, Welch TJ (1990) Ct and sonographically guided needle biopsy: current techniques and new innovations. AJR Am J Roentgenol 154(1):1–10CrossRefPubMed Charboneau JW, Reading CC, Welch TJ (1990) Ct and sonographically guided needle biopsy: current techniques and new innovations. AJR Am J Roentgenol 154(1):1–10CrossRefPubMed
8.
go back to reference Tesei M, Saccomandi P, Massaroni C, Quarta R, Carassiti M, Schena E, Setola R (2016) A cost-effective, non-invasive system for pressure monitoring during epidural needle insertion: Design, development and bench tests. In: IEEE 38th annual international conference of the engineering in medicine and biology society (EMBC), IEEE, pp 194–197 Tesei M, Saccomandi P, Massaroni C, Quarta R, Carassiti M, Schena E, Setola R (2016) A cost-effective, non-invasive system for pressure monitoring during epidural needle insertion: Design, development and bench tests. In: IEEE 38th annual international conference of the engineering in medicine and biology society (EMBC), IEEE, pp 194–197
10.
go back to reference Dorileo E, Zemiti N, Poignet P (2015) Needle deflection prediction using adaptive slope model. In: International conference on advanced robotics (ICAR), IEEE, pp 60–65 Dorileo E, Zemiti N, Poignet P (2015) Needle deflection prediction using adaptive slope model. In: International conference on advanced robotics (ICAR), IEEE, pp 60–65
11.
go back to reference Roesthuis RJ, Van Veen YR, Jahya A, Misra S (2011) Mechanics of needle-tissue interaction. In: IEEE/RSJ international conference on intelligent robots and systems (IROS), IEEE, pp 2557–2563 Roesthuis RJ, Van Veen YR, Jahya A, Misra S (2011) Mechanics of needle-tissue interaction. In: IEEE/RSJ international conference on intelligent robots and systems (IROS), IEEE, pp 2557–2563
12.
go back to reference Asadian A, Kermani MR, Patel RV (2011) An analytical model for deflection of flexible needles during needle insertion. In: IEEE/RSJ international conference on intelligent robots and systems (IROS), IEEE, pp 2551–2556 Asadian A, Kermani MR, Patel RV (2011) An analytical model for deflection of flexible needles during needle insertion. In: IEEE/RSJ international conference on intelligent robots and systems (IROS), IEEE, pp 2551–2556
13.
go back to reference Park W, Reed KB, Okamura AM, Chirikjian GS (2010) Estimation of model parameters for steerable needles. In: IEEE International conference on robotics and automation (ICRA), 2010, IEEE, pp 3703–3708 Park W, Reed KB, Okamura AM, Chirikjian GS (2010) Estimation of model parameters for steerable needles. In: IEEE International conference on robotics and automation (ICRA), 2010, IEEE, pp 3703–3708
14.
go back to reference Taffoni F, Formica D, Saccomandi P, Pino GD, Schena E (2013) Optical fiber-based MR-compatible sensors for medical applications: an overview. Sensors 13(10):14105–14120CrossRefPubMedPubMedCentral Taffoni F, Formica D, Saccomandi P, Pino GD, Schena E (2013) Optical fiber-based MR-compatible sensors for medical applications: an overview. Sensors 13(10):14105–14120CrossRefPubMedPubMedCentral
15.
go back to reference Sadjadi H, Hashtrudi-Zaad K, Fichtinger G (2013) Fusion of electromagnetic trackers to improve needle deflection estimation: simulation study. IEEE Trans Biomed Eng 60(10):2706–2715CrossRefPubMed Sadjadi H, Hashtrudi-Zaad K, Fichtinger G (2013) Fusion of electromagnetic trackers to improve needle deflection estimation: simulation study. IEEE Trans Biomed Eng 60(10):2706–2715CrossRefPubMed
16.
go back to reference Jiang B, Gao W, Kacher DF, Lee TC, Jayender J (2016) Kalman filter based data fusion for needle deflection estimation using optical-em sensor. In: International conference on medical image computing and computer-assisted intervention, Springer, pp 457–464 Jiang B, Gao W, Kacher DF, Lee TC, Jayender J (2016) Kalman filter based data fusion for needle deflection estimation using optical-em sensor. In: International conference on medical image computing and computer-assisted intervention, Springer, pp 457–464
17.
go back to reference Roth A, Nevo E (2015) Method and apparatus to estimate location and orientation of objects during magnetic resonance imaging. US Patent 9,037,213 Roth A, Nevo E (2015) Method and apparatus to estimate location and orientation of objects during magnetic resonance imaging. US Patent 9,037,213
18.
go back to reference Goksel O, Dehghan E, Salcudean SE (2009) Modeling and simulation of flexible needles. Med Eng Phys 31(9):1069–1078CrossRefPubMed Goksel O, Dehghan E, Salcudean SE (2009) Modeling and simulation of flexible needles. Med Eng Phys 31(9):1069–1078CrossRefPubMed
19.
go back to reference Wan G, Wei Z, Gardi L, Downey DB, Fenster A (2005) Brachytherapy needle deflection evaluation and correction. Med Phys 32(4):902–909CrossRefPubMed Wan G, Wei Z, Gardi L, Downey DB, Fenster A (2005) Brachytherapy needle deflection evaluation and correction. Med Phys 32(4):902–909CrossRefPubMed
20.
go back to reference Chui CK, Chen G (2008) Kalman filtering: with real-time applications. Springer, Berlin Chui CK, Chen G (2008) Kalman filtering: with real-time applications. Springer, Berlin
21.
go back to reference Hall DL, Llinas J (1997) An introduction to multisensor data fusion. Proc IEEE 85(1):6–23CrossRef Hall DL, Llinas J (1997) An introduction to multisensor data fusion. Proc IEEE 85(1):6–23CrossRef
22.
go back to reference Yan KG, Podder T, Xiao D, Yu Y, Liu TI, Ling KV, Ng WS (2006) Online parameter estimation for surgical needle steering model. In: Medical image computing and computer-assisted intervention–MICCAI 2006, Springer, pp 321–329 Yan KG, Podder T, Xiao D, Yu Y, Liu TI, Ling KV, Ng WS (2006) Online parameter estimation for surgical needle steering model. In: Medical image computing and computer-assisted intervention–MICCAI 2006, Springer, pp 321–329
24.
go back to reference Nevo E (2003) Method and apparatus to estimate location and orientation of objects during magnetic resonance imaging. US Patent 6,516,213 Nevo E (2003) Method and apparatus to estimate location and orientation of objects during magnetic resonance imaging. US Patent 6,516,213
25.
go back to reference Yaniv Z (2015) Which pivot calibration? In: SPIE medical imaging, international society for optics and photonics, pp 941527–941527 Yaniv Z (2015) Which pivot calibration? In: SPIE medical imaging, international society for optics and photonics, pp 941527–941527
26.
go back to reference Crouch JR, Schneider CM, Wainer J, Okamura AM (2005) A velocity-dependent model for needle insertion in soft tissue. In: Medical image computing and computer-assisted intervention–Miccai 2005, Springer, pp 624–632 Crouch JR, Schneider CM, Wainer J, Okamura AM (2005) A velocity-dependent model for needle insertion in soft tissue. In: Medical image computing and computer-assisted intervention–Miccai 2005, Springer, pp 624–632
27.
go back to reference Bar-Shalom Y, Li XR, Kirubarajan T (2004) Estimation with applications to tracking and navigation: theory algorithms and software. Wiley, Hoboken Bar-Shalom Y, Li XR, Kirubarajan T (2004) Estimation with applications to tracking and navigation: theory algorithms and software. Wiley, Hoboken
28.
go back to reference Cerveri P, Pedotti A, Ferrigno G (2003) Robust recovery of human motion from video using kalman filters and virtual humans. Hum Mov Sci 22(3):377–404CrossRefPubMed Cerveri P, Pedotti A, Ferrigno G (2003) Robust recovery of human motion from video using kalman filters and virtual humans. Hum Mov Sci 22(3):377–404CrossRefPubMed
29.
go back to reference Lagarias JC, Reeds JA, Wright MH, Wright PE (1998) Convergence properties of the nelder-mead simplex method in low dimensions. SIAM J Optim 9(1):112–147CrossRef Lagarias JC, Reeds JA, Wright MH, Wright PE (1998) Convergence properties of the nelder-mead simplex method in low dimensions. SIAM J Optim 9(1):112–147CrossRef
30.
go back to reference Khadem M, Rossa C, Sloboda RS, Usmani N, Tavakoli M (2016) Ultrasound-guided model predictive control of needle steering in biological tissue. J Med Robot Res 1(01):1640007CrossRef Khadem M, Rossa C, Sloboda RS, Usmani N, Tavakoli M (2016) Ultrasound-guided model predictive control of needle steering in biological tissue. J Med Robot Res 1(01):1640007CrossRef
31.
go back to reference Coleman TF, Li Y (1996) An interior trust region approach for nonlinear minimization subject to bounds. SIAM J Optim 6(2):418–445CrossRef Coleman TF, Li Y (1996) An interior trust region approach for nonlinear minimization subject to bounds. SIAM J Optim 6(2):418–445CrossRef
32.
go back to reference Birkfellner W, Watzinger F, Wanschitz F, Ewers R, Bergmann H (1998) Calibration of tracking systems in a surgical environment. IEEE Trans Med Imaging 17(5):737–742CrossRefPubMed Birkfellner W, Watzinger F, Wanschitz F, Ewers R, Bergmann H (1998) Calibration of tracking systems in a surgical environment. IEEE Trans Med Imaging 17(5):737–742CrossRefPubMed
Metadata
Title
Kalman filter-based EM-optical sensor fusion for needle deflection estimation
Authors
Baichuan Jiang
Wenpeng Gao
Daniel Kacher
Erez Nevo
Barry Fetics
Thomas C. Lee
Jagadeesan Jayender
Publication date
01-04-2018
Publisher
Springer International Publishing
Published in
International Journal of Computer Assisted Radiology and Surgery / Issue 4/2018
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI
https://doi.org/10.1007/s11548-018-1708-8

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