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Low-cost 3D vision-based triangulation system for ultrasonic probe positioning

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Introduction: In ultrasonic imaging, such as echocardiography, accurately positioning the probe in relation to the patient's body or an external coordinate system is typically done manually. However, when developing speckle-tracking methods for echocardiology, ensuring consistency in probe positioning is essential for reliable data interpretation. To address this challenge, we present a vision-based system and method for probe positioning in this study. Materials and Methods: Our system comprises two cameras, a calibration frame with eight markers of known coordinates in the frames' local coordinate system, and a probe holder with four markers. The calibration process involves image segmentation via region growing and extraction of the camera projection matrices. Subsequently, our positioning method also utilises marker segmentation, followed by estimating the markers' positions using triangulation. Results: To evaluate the system's performance, we conducted tests using a validation plate with five coplanar circular markers. The distances between each pair of points were calculated, and their errors compared to the true distances were found to be within a maximum of 0.7 mm. This level of accuracy is comparable to ultrasonic imaging resolution and thus deemed sufficient for the intended purpose. Conclusion: For those interested in replicating or modifying our methods, the supplementary material includes the complete design of the calibration frame and the Matlab code.
Rocznik
Strony
249--257
Opis fizyczny
Bibliogr. 22 poz., rys., tab.
Twórcy
autor
  • Warsaw University of Technology, Institute of Metrology and Biomedical Engineering, Warsaw, Poland
  • Warsaw University of Technology, Institute of Metrology and Biomedical Engineering, Warsaw, Poland
autor
  • Warsaw University of Technology, Institute of Metrology and Biomedical Engineering, Warsaw, Poland
  • Warsaw University of Technology, Institute of Metrology and Biomedical Engineering, Warsaw, Poland
Bibliografia
  • 1. Chetboul V, Athanassiadis N, Concordet D, et al. Observer‐dependent variability of quantitative clinical endpoints: the example of canine echocardiography. Vet Pharm & Therapeutics. 2004;27(1):49-56. https://doi.org//10.1046/j.0140-7783.2003.00543.x
  • 2. Kunz P, Kiesl S, Groß S, Kauczor HU, Schmidmaier G, Fischer C. Intra-observer and Device-Dependent Inter-observer Reliability of Contrast-Enhanced Ultrasound for Muscle Perfusion Quantification. Ultrasound in Medicine & Biology. 2020;46(2):275-285. https://doi.org//10.1016/j.ultrasmedbio.2019.10.007
  • 3. Serago CF, Chungbin SJ, Buskirk SJ, Ezzell GA, Collie AC, Vora SA. Initial experience with ultrasound localisation for positioning prostate cancer patients for external beam radiotherapy. International Journal of Radiation Oncology*Biology*Physics. 2002;53(5):1130-1138. https://doi.org//10.1016/s0360-3016(02)02826-2
  • 4. Toporek G, Wang H, Balicki M, Xie H. Autonomous image-based ultrasound probe positioning via deep learning. EasyChair Preprints. Published online May 8, 2018. https://doi.org//10.29007/dj33
  • 5. Peng C, Cai Q, Chen M, Jiang X. Recent Advances in Tracking Devices for Biomedical Ultrasound Imaging Applications. Micromachines. 2022;13(11):1855. https://doi.org//10.3390/mi13111855
  • 6. Ali A, Logeswaran R. A visual probe localisation and calibration system for cost-effective computer-aided 3D ultrasound. Computers in Biology and Medicine. 2007;37(8):1141-1147. https://doi.org//10.1016/j.compbiomed.2006.10.003
  • 7. De Lorenzo D, Vaccarella A, Khreis G, Moennich H, Ferrigno G, De Momi E. Accurate calibration method for 3D freehand ultrasound probe using virtual plane. Medical Physics. 2011;38(12):6710-6720. https://doi.org//10.1118/1.3663674
  • 8. Rocchi M, Ingram M, Claus P, D'hooge J, Meyns B, Fresiello L. Use of 3D anatomical models in mock circulatory loops for cardiac medical device testing. Artificial Organs. 2022;47(2):260-272. https://doi.org//10.1111/aor.14433
  • 9. Okrasinski SJ, Ramachandran B, Konofagou EE. Assessment of myocardial elastography performance in phantoms under combined physiologic motion configurations with preliminaryin vivofeasibility. Phys Med Biol. 2012;57(17):5633-5650. https://doi.org//10.1088/0031-9155/57/17/5633
  • 10. Heyde B, Cygan S, Hon Fai Choi, et al. Regional cardiac motion and strain estimation in three-dimensional echocardiography: a validation study in thick-walled univentricular phantoms. IEEE Trans Ultrason, Ferroelect, Freq Contr. 2012;59(4):668-682. https://doi.org//10.1109/tuffc.2012.2245
  • 11. Hjertaas JJ, Fosså H, Dybdahl GL, Grüner R, Lunde P, Matre K. Accuracy of Real-Time Single- and Multi-Beat 3-D Speckle Tracking Echocardiography In Vitro. Ultrasound in Medicine &vBiology. 2013;39(6):1006-1014. https://doi.org//10.1016/j.ultrasmedbio.2013.01.010
  • 12. Lesniak-Plewinska B, Cygan S, Kaluzynski K, et al. A Dual-Chamber, Thick-Walled Cardiac Phantom for Use in Cardiac Motion and Deformation Imaging by Ultrasound. Ultrasound in Medicine & Biology. 2010;36(7):1145-1156. https://doi.org//10.1016/j.ultrasmedbio.2010.04.008
  • 13. Cygan S, Werys K, Błaszczyk Ł, Kubik T, Kałużyński K. Left ventricle phantom and experimental setup for MRI and echocardiography – Preliminary results of data acquisitions. Biocybernetics and Biomedical Engineering. 2014;34(1):19-24. https://doi.org//10.1016/j.bbe.2013.12.002
  • 14. Cygan S, Kumor M, Żmigrodzki J, Leśniak-Plewińska B, Kowalski M, Kałużyński K. Left ventricular phantoms with inclusions simulating transmural and non-transmural infarctions: FEM and EchoPAC study. Duric N, Heyde B, eds. SPIE Proceedings. 2017;10139:1013918. https://doi.org//10.1117/12.2254350
  • 15. Kroon DJ. Region Growing (https://www.mathworks.com/matlabcentral/fileexchange/19084-region-growing). MATLAB Central File Exchange. Retrieved January 11, 2023.
  • 16. Kolahi A, Hoviattalab M, Rezaeian T, Alizadeh M, Bostan M, Mokhtarzadeh H. Design of a marker-based human motion tracking system. Biomedical Signal Processing and Control. 2007;2(1):59-67. https://doi.org//10.1016/j.bspc.2007.02.001
  • 17. Abdel-Aziz YI, Karara HM. Direct Linear Transformation from Comparator Coordinates into Object Space Coordinates in Close-Range Photogrammetry. photogramm eng remote sensing. 2015;81(2):103-107. https://doi.org//10.14358/pers.81.2.103
  • 18. Rahimian P, Kearney JK. Optimal Camera Placement for Motion Capture Systems. IEEE Trans Visual Comput Graphics. 2017;23(3):1209-1221. https://doi.org//10.1109/tvcg.2016.2637334
  • 19. Żmigrodzki J, Cygan S, Leśniak-Plewińska B, Kowalski M, Kałużyński K. Effect of Transmural Extent of the Simulated Infarction in a Left Ventricular Model on Displacement and Strain Distribution Estimated from Synthetic Ultrasonic Data. Ultrasound in Medicine& Biology. 2017;43(1):206-217. https://doi.org//10.1016/j.ultrasmedbio.2016.08.017
  • 20. Das K, de Paula Oliveira T, Newell J. Comparison of markerless and marker-based motion capture systems using 95% functional limits of agreement in a linear mixed-effects modelling framework. Sci Rep. 2023;13(1). https://doi.org//10.1038/s41598-023-49360-2
  • 21. Shen J, Zemiti N, Dillenseger JL, Poignet P. Fast And Simple Automatic 3D Ultrasound Probe Calibration Based On 3D Printed Phantom And An Untracked Marker. 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Published online July 2018:878-882. https://doi.org//10.1109/embc.2018.8512406
  • 22. Ghorbani A, Ouyang D, Abid A, et al. Deep learning interpretation of echocardiograms. npj Digit Med. 2020;3(1). https://doi.org//10.1038/s41746-019-0216-8
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-fddbd551-b509-4031-8d07-58ada049dd19
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