Ultrasound-guided prostate percutaneous intervention robot system and calibration by informative particle swarm optimization

Abstract

Applying a robot system in ultrasound-guided percutaneous intervention is an effective approach for prostate cancer diagnosis and treatment. The limited space for robot manipulation restricts structure volume and motion. In this paper, an 8-degree-of-freedom robot system is proposed for ultrasound probe manipulation, needle positioning, and needle insertion. A novel parallel structure is employed in the robot system for space saving, structural rigidity, and collision avoidance. The particle swarm optimization method based on informative value is proposed for kinematic parameter identification to calibrate the parallel structure accurately. The method identifies parameters in the modified kinematic model stepwise according to parameter discernibility. Verification experiments prove that the robot system can realize motions needed in targeting. By applying the calibration method, a reasonable, reliable forward kinematic model is built, and the average errors can be limited to 0.963 and 1.846 mm for insertion point and target point, respectively.

Graphical abstract

Keywords

ultrasound image guidance / prostate percutaneous intervention / parallel robot / kinematics identification / particle swarm optimization / informative value

References

[1] SiegelR L, MillerK D, JemalA. Cancer statistics. CA: A Cancer Journal for Clinicians, 2020, 70( 1): 7– 30

[2] CarterH B. American Urological Association (AUA) Guideline on prostate cancer detection: process and rationale. BJU International, 2013, 112( 5): 543– 547

[3] JiangS, YangY P, YangZ Y, ZhangZ, LiuS. Design and experiments of ultrasound image-guided multi-DOF robot system for brachytherapy. Transactions of Tianjin University, 2017, 23( 5): 479– 487

[4] ThomasT L, VenkiteswaranV K, AnanthasureshG K, MisraS. Surgical applications of compliant mechanisms: a review. Journal of Mechanisms and Robotics, 2021, 13( 2): 020801–

[5] UkimuraO, HiraharaN, FujiharaA, YamadaT, IwataT, KamoiK, OkiharaK, ItoH, NishimuraT, MikiT. Technique for a hybrid system of real-time transrectal ultrasound with preoperative magnetic resonance imaging in the guidance of targeted prostate biopsy. International Journal of Urology, 2010, 17( 10): 890– 893

[6] SinghA K, KrueckerJ, XuS, GlossopN, GuionP, UllmanK, ChoykeP L, WoodB J. Initial clinical experience with real-time transrectal ultrasonography-magnetic resonance imaging fusion-guided prostate biopsy. BJU International, 2008, 101( 7): 841– 845

[7] PoquetC, MozerP, VitraniM A, MorelG. An endorectal ultrasound probe comanipulator with hybrid actuation combining brakes and motors. IEEE/ASME Transactions on Mechatronics, 2015, 20( 1): 186– 196

[8] LimS, JunC, ChangD, PetrisorD, HanM, StoianoviciD. Robotic transrectal ultrasound guided prostate biopsy. IEEE Transactions on Biomedical Engineering, 2019, 66( 9): 2527– 2537

[9] SchlüterM, FürwegerC, SchlaeferA. Optimizing robot motion for robotic ultrasound-guided radiation therapy. Physics in Medicine and Biology, 2019, 64( 19): 195012–

[10] YuX B, HeW, LiH Y, SunJ. Adaptive fuzzy full-state and output-feedback control for uncertain robots with output constraint. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, 51( 11): 6994– 7007

[11] KongL Y, ChenG L, WangH, HuangG Y, ZhangD. Kinematic calibration of a 3-PRRU parallel manipulator based on the complete, minimal and continuous error model. Robotics and Computer-Integrated Manufacturing, 2021, 71 : 102158–

[12] LiZ B, LiS, LuoX. An overview of calibration technology of industrial robots. IEEE/CAA Journal of Automatica Sinica, 2021, 8( 1): 23– 36

[13] QuinteroH F, MejiaL A, Diaz-RodriguezM. End-effector positioning due to joint clearances: a comparison among three planar 2-DOF parallel manipulators. Journal of Mechanical Science and Technology, 2019, 33( 7): 3497– 3507

[14] JiangZ H, ZhouW G, LiH, MoY, NiW C, HuangQ. A new kind of accurate calibration method for robotic kinematic parameters based on the extended Kalman and particle filter algorithm. IEEE Transactions on Industrial Electronics, 2018, 65( 4): 3337– 3345

[15] GanY H, DuanJ J, DaiX Z. A calibration method of robot kinematic parameters by drawstring displacement sensor. International Journal of Advanced Robotic Systems, 2019, 16( 5): 1– 9

[16] LiJ, YuL D, SunJ Q, XiaH J. A kinematic model for parallel-joint coordinate measuring machine. Journal of Mechanisms and Robotics, 2013, 5( 4): 044501–

[17] HuangT, LiuH T, ChetwyndD G. Generalized Jacobian analysis of lower mobility manipulators. Mechanism and Machine Theory, 2011, 46( 6): 831– 844

[18] TianW J, ShenZ Q, LvD P, YinF W. A systematic approach for accuracy design of lower-mobility parallel mechanism. Robotica, 2020, 38( 12): 2173– 2188

[19] ZhangZ Y. A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22( 11): 1330– 1334

[20] LiaoS H, ZengQ, EhmannK F, CaoJ. Parameter identification and nonparametric calibration of the tri-pyramid robot. IEEE/ASME Transactions on Mechatronics, 2020, 25( 5): 2309– 2317

[21] DaneyD, PapegayY, MadelineB. Choosing measurement poses for robot calibration with the local convergence method and tabu search. The International Journal of Robotics Research, 2005, 24( 6): 501– 518

[22] MaoC T, ChenZ W, LiS, ZhangX. Separable nonlinear least squares algorithm for robust kinematic calibration of serial robots. Journal of Intelligent & Robotic Systems, 2021, 101( 1): 2–

[23] XuW Y, XuH D, LiuF K, TangY Y, WuZ, WangX J, WangJ, FengJ Q. Millimeter wave power monitoring in EAST ECRH system. IEEE Access: Practical Innovations, Open Solutions, 2016, 4 : 5809– 5817

[24] KennedyJ, EberhartR. Particle swarm optimization. In: Proceedings of ICNN'95-International Conference on Neural Networks. Perth: IEEE, 1995, 1942– 1948

[25] QiY, SunT, SongY M. Multi-objective optimization of parallel tracking mechanism considering parameter uncertainty. Journal of Mechanisms and Robotics, 2018, 10( 4): 041006–

[26] FlockerF W, BravoR H. On global convergence in design optimization using the particle swarm optimization technique. Journal of Mechanical Design, 2016, 138( 8): 081402–

[27] Gao G B, Liu F, San H J, Wu X, Wang W. Hybrid optimal kinematic parameter identification for an industrial robot based on BPNN-PSO. Complexity, 2018, 4258676

[28] ZhaoQ, YueY H, GuanQ. A PSO-based ball-plate calibration for laser scanner. In: Proceedings of 2009 International Conference on Measuring Technology and Mechatronics Automation. Zhangjiajie: IEEE, 2009, 2 : 479– 481

[29] ZhengY X, LiaoY. Parameter identification of nonlinear dynamic systems using an improved particle swarm optimization. Optik (Stuttgart), 2016, 127( 19): 7865– 7874

[30] Shankar GaneshS, Koteswara RaoA B. Error analysis and optimization of a 3-degree of freedom translational parallel kinematic machine. Frontiers of Mechanical Engineering, 2014, 9( 2): 120– 129

[31] QiuN, ParkC, GaoY K, FangJ G, SunG Y, KimN H. Sensitivity-based parameter calibration and model validation under model error. Journal of Mechanical Design, 2018, 140( 1): 011403–

[32] DrigneiD, MourelatosZ P, PandeyV, KokkolarasM. Concurrent design optimization and calibration-based validation using local domains sized by bootstrapping. Journal of Mechanical Design, 2012, 134( 10): 100910–

[33] VernerM, XiF F, MechefskeC. Optimal calibration of parallel kinematic machines. Journal of Mechanical Design, 2005, 127( 1): 62– 69

[34] XuS, KrueckerJ, TurkbeyB, GlossopN, SinghA K, ChoykeP, PintoP, WoodB J. Real-time MRI-TRUS fusion for guidance of targeted prostate biopsies. Computer Aided Surgery, 2008, 13( 5): 255– 264

RIGHTS & PERMISSIONS

The Author(s) 2022. This article is published with open access at link.springer.com and journal.hep.com.cn.