DEVELOPMENT OF HYBRID FORCE-POSITION CONTROLLER FOR ULTRASOUND-GUIDED BREAST BIOPSY ROBOTIC SYSTEM
MOHAMMAD AFIF BIN AYOB
A thesis submitted in
fulfillment of the requirement for the award of the Doctor of Philosophy of Electrical Engineering
Faculty of Electrical and Electronics Engineering Universiti Tun Hussein Onn Malaysia
MARCH 2018
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For my beloved parents & my dearest partner, Nurasyeera.
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ACKNOWLEDGEMENT
Alhamdulillah. Praise be to Allah.
I would like to take this opportunity to express my sincere appreciation to my supervisor, Dr Wan Nurshazwani Binti Wan Zakaria for without her enthusiasm, encouragement, support and continuous optimism this thesis would hardly have been completed. I also would like to thank my co supervisor, Dr Jamaludin Bin Jalani for his advice and direction of this work.
This thesis would not have been possible without the inspiration and support of a number of wonderful individuals – many thanks and appreciation to all of them for being part of this journey and making this thesis possible. Acknowledgement also goes to Coldplay for constantly being inspiring and motivating during my ups and downs in this research.
And finally, last but by no means least, special thanks to my family members for their motivation, unparalleled love and emotional supports.
Thanks for all your encouragement!
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ABSTRACT
Conventional ultrasound-guided breast biopsy (UGBB) procedure is commonly performed to assess abnormal masses within the breast. It requires a radiologist to handle multiple devices at once, which could reduce the abilities in performing such procedure resulting in radiologist’s fatigue, compromised breast tissue due to multiple insertions and susceptibilities to pneumothorax complication for the patient. Previous studies have reported that many of the restrictions associated with handheld minimally invasive methods were tackled when physician assist instruments were used. Therefore, the purpose of this research is to assist radiologist in conventional UGBB procedure by introducing a semi-automated robotic system to maintain desired contact force between the ultrasound transducer and the breast. For that reason, a hybrid force/position controlled UGBB robotic system has been developed in simulation environment. The UGBB robotic system involves a 5 degree of freedom (DOF) articulated robot arm to control the transducer movement, a force/torque (F/T) sensor system to measure the contact force, an ultrasound machine to view the inside structure of the breast tissue and a computer-based control system. As such, the RV-2AJ robotic arm has been modelled with its positional accuracy of almost 100%. A breast model based on a medical grade breast phantom has been established with a mean error of 0.69% by using black-box modelling approach. Motion disturbance from human respiration has been explored as well since it plays a significant element that would affect the stability of the system to constantly maintain low contact force on the breast. Finally, intelligent Fuzzy-PID hybrid force/position controller has been successfully established to maintain low contact force on identified breast stiffness characteristics. The overall hardware-based simulation shows promising outcomes with almost no overshoot, fast rise time, high robustness and stability on different environment condition. In conclusion, the success of this work serves as significant foundations for long-term related research, especially in the development of UGBB robotic system and approaches of force control mainly for human-robot interaction.
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ABSTRAK
Prosedur konvensional biopsi payudara secara ultrasound (UGBB) dijalankan secara meluas untuk menilai ketumbuhan yang tidak normal di dalam payudara. Ianya memerlukan pakar bedah untuk mengendalikan pelbagai alat pada satu-satu masa, yang mana boleh mengurangkan keupayaan pakar bedah dalam melakukan prosedur tersebut, mengakibatkan keletihan kepada pakar bedah, dan menjejaskan integriti struktur tisu payudara akibat kekerapan suntikan jarum biopsi. Kajian terdahulu telah melaporkan bahawa banyak sekatan yang berkaitan dengan kaedah pandu tangan invasif secara minimum dapat ditangani dengan menggunakan alat bantuan kepada doktor. Oleh itu, tujuan penyelidikan ini adalah untuk membantu ahli radiologi dalam prosedur konvensional UGBB dengan memperkenalkan sistem robotik separa automatik untuk mengekalkan daya sentuhan yang dikehendaki antara transduser ultrasound dan payudara. Atas sebab itu, satu sistem robot hibrid daya/posisi UGBB telah dibangunkan dalam persekitaran simulasi. Sistem robot UGBB tersebut melibatkan 5 darjah kebebasan (DOF) untuk mengawal pergerakan transduser, sistem deria daya/kilas (F/T) untuk mengukur daya sentuhan, mesin ultrasound untuk melihat struktur dalaman tisu payudara, dan sistem kawalan berasaskan komputer. Dengan itu, sistem robot RV-2AJ telah dimodelkan dengan ketepatan posisinya menghampiri 100%. Model payudara berdasarkan payudara silikon gred perubatan juga telah ditubuhkan dengan kesilapan min 0.69% dengan menggunakan pendekatan permodelan kotak hitam. Gangguan pergerakan dari pernafasan manusia juga telah dibangunkan memandangkan ianya memainkan peranan penting dalam memberi kesan kepada kestabilan sistem untuk mengekalkan tekanan yang rendah terhadap payudara. Akhir sekali, pengawal hibrid daya/posisi telah berjaya ditubuhkan untuk mengekalkan daya sentuhan rendah terhadap kekerasan payudara silikon yang dikenal pasti. Keseluruhan simulasi berasaskan perkakasan menunjukkan hasil yang memberangsangkan dengan hampir tiada lebihan respon, masa kenaikan yang pantas, kekukuhan dan kestabilan yang tinggi dalam keadaan persekitaran yang berbeza.
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Kejayaan kajian ini merupakan landasan penting bagi penyelidikan jangka panjang, terutamanya dalam pembangunan sistem robot UGBB dan pendekatan kawalan daya untuk interaksi antara robot dan manusia.
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CONTENTS TITLE i DECLARATION ii DEDICATION iii ACKNOWLEDGEMENT iv ABSTRACT v ABSTRAK vi CONTENTS viii LIST OF TABLES xi
LIST OF FIGURES xiii
LIST OF SYMBOLS AND ABBREVIATIONS xviii
LIST OF APPENDICES xxi
CHAPTER 1 INTRODUCTION 1
1.1 Problem Statement 4
1.2 Aim and Objectives 5
1.3 Scopes 5
1.4 Contributions 6
1.5 Publications 7
1.6 Thesis Outline 7
CHAPTER 2 LITERATURE REVIEW 9
2.1 Introduction to UGBB Procedure 9
2.2 Existing UGBB Robotic Systems 11
2.3 Hybrid Force/Position Control in Robotic Systems 18
2.4 Hybrid Force/Position Control Law 25
2.4.1 Position Control Law 25
2.4.2 Force/Position Control Law 26
2.5 Summary 29
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CHAPTER 3 RESEARCH APPROACH 30
3.1 Research Methodology 31
3.2 Feasibility Studies 33
3.3 Design of UGBB Robotic System 39
3.4 Hardware System 42
3.4.1 5-DOF Industrial Robot 42
3.4.2 Ultrasound Machine 43
3.4.3 6-Axis F/T Sensor 45
3.4.4 F/T Controller 46
3.5 Software System 47
3.5.1 Robot Controller Communication to Host PC 47 3.5.2 F/T Controller Communication to Host PC 50
3.6 Summary 51
CHAPTER 4 UGBB ROBOTIC SYSTEM MODELLING AND
SIMULATION 53
4.1 Modelling of RV-2AJ Robot 54
4.1.1 CAD Modelling 54
4.1.2 SimMechanics Modelling 56
4.2 Robot Kinematics Analysis 59
4.2.1 Forward Kinematics Study 60
4.2.2 Inverse Kinematics Study 68
4.3 Simulation of RV-2AJ in SimMechanics 80
4.4 Summary 82
CHAPTER 5 CONTACT FORCE MODELLING 83
5.1 Nonlinear ARX Soft Tissue Modelling by Wavenet
and Sigmoid Estimators 84
5.1.1 Experimental Setup 85
5.1.2 Simulation Results and Analysis 91
5.1.3 Development of Motion Disturbance 98
5.2 Summary 101
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CHAPTER 6 DEVELOPMENT OF FORCE/POSITION
CONTROL SYSTEM 103
6.1 UGBB Control System 103
6.2 PID Force Controller 105
6.2.1 Proportional Force Controller 106
6.2.2 PI Force Controller 107
6.3 Development of Intelligent Force Controller 108
6.3.1 PI-Fuzzy Force Controller 109
6.3.2 Fuzzy-PID Force Controller 112
6.4 Force Controller Performance Evaluation 116 6.5 Comparison of Force and Position Responses on
Different Environment Condition 121
6.5.1 Position Control System without Force Controller 121 6.5.2 Force and Position Control System without
Disturbance 123
6.5.3 Force and Position Control System with
Respiration Disturbance 124
6.5.4 Force and Position Control System with Extreme
Disturbance 126
6.6 Variations of Respiration Disturbance 127 6.7 Variations of Desired Contact Force 131
6.8 Summary 136
CHAPTER 7 CONCLUSION AND RECOMMENDATIONS FOR
FUTURE WORK 137
7.1 Conclusion 137
7.2 Recommendations for Future Work 138
REFERENCES 140
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LIST OF TABLES
2.1 Summary of existing UGBB robotic system 18
2.2 Comparative study of existing hybrid force/position control
in robotic systems 24
2.3 Summary of position control law in existing robotic system 26 2.4 Summary of force/position control law in existing robotic
system 28
3.1 Ultrasound images of targeted lump movement at different
contact force 37
4.1 RV-2AJ robot arm parameters 60
4.2 Comparison between simulation and experiment results for
home position 64
4.3 Comparison between simulation and experiment results for
second position 65
4.4 Comparison between simulation and experiment results for
third position 66
4.5 Comparison between simulation and experiment results for
fourth position 67
4.6 Comparison between simulation and experiment results for
fifth position 68
4.7 Comparison between simulation and experimental results for
determining θ1 74
4.8 Comparison between simulation and experimental results for
first configuration 75
4.9 Comparison between simulation and experimental results for
second configuration 77
4.10 Comparison between simulation and experimental results for
third configuration 79
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4.11 Results comparison between geometrical approach and
inverse matrix multiplication method 80
4.12 Comparison between experiment and simulation results for
both forward and inverse kinematics of RV-2AJ 81 5.1 Model fit and correlation test summary for nonlinear models
using wavenet and sigmoid estimators 91
5.2 Measured and simulation output force at fixed input distance for (a) generic tissue phantom model and (b) breast phantom
model 96
6.1 PI-Fuzzy force controller membership functions 110 6.2 Time-domain characteristic for different types of force
controller for a step input of 1N 118
6.3 Time-domain characteristic for different types of force
controller for a step input of 7N 119
6.4 Performance index values for different types of force
controller 120
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LIST OF FIGURES
1.1 Ultrasound-guided core needle biopsy (Mayo Clinic, 2014) 3 2.1 Patient undergoing UGBB procedure involving (a) one
radiologist handling multiple devices and (b) two radiologists
performing each tasks individually 10
2.2 The steerable needle device: (a) External device view; (b) Stylet extension drive train; and (c) Stylet rotation drive train
(Okazawa et al., 2005) 11
2.3 (a) Schematic diagram and (b) Real hardware of the
experimental setup (Liang et al., 2010) 12
2.4 Experiment of robotic system for lump manipulation and
UGBB (V. Mallapragada et al., 2008b) 13
2.5 Ultrasound-guided breast intervention system: (a) Overall mechanism; and (b) Needle guidance system (Mallapragada
et al., 2011) 14
2.6 Needle insertion manipulator (Kobayashi et al., 2009) 15 2.7 (a) Overview of in vitro experiment and (b) Manipulator for
in vivo experiments (Hatano et al., 2011) 16 2.8 Breast test object and biopsy system attached on the robot
arm (Nelson et al., 2010) 17
2.9 Block diagram of the external hybrid force/position control
during the guiding task (Rabenorosoa et al., 2010) 19 2.10 Structure of hybrid force/position controller (Jiang et al.,
2010) 20
2.11 Block diagram of the hybrid force/position controller (Cho et
al., 2013) 20
2.12 Skincare robot (Tsumaki et al., 2008) 21
2.13 Ultrasound diagnosis robot (Masuda et al., 2011) 22
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2.14 Experimental scenario (Perez-del-Pulgar et al., 2013) 23 2.15 Device compensates for up to 5cm between the radiologist
and patient (Gilbertson & Anthony, 2015) 23
3.1 Flowchart of research activities 32
3.2 Steps involved in locating suspected lump in a breast using
ultrasound transducer 33
3.3 Proposed procedures for UGBB robotic system 34 3.4 Experimental setup to obtain lump movement 35 3.5 Targeted lump movement on different contact forces 39 3.6 Overall configuration of UGBB robotic system 40
3.7 Experimental setup of UGBB robotic system 41
3.8 Control architecture of the proposed UGBB robotic system 42 3.9 5-DOF Mitsubishi RV-2AJ industrial robot (Mitsubishi
Electric, 2008) 43
3.10 Toshiba Nemio SSA-550A ultrasound machine (BIMEDIS,
2010a) 44
3.11 Toshiba PLM-805AT ultrasound transducer (BIMEDIS,
2010b) 44
3.12 Mini40 6-axis F/T sensor (ATI Industrial Automation, 2013) 45 3.13 F/T controller (ATI Industrial Automation, 2012) 46 3.14 F/T controller connections (ATI Industrial Automation, 2012) 46
3.15 Software configuration diagram 48
3.16 Communication protocol window 49
3.17 Communication server window: (a) Successful connection
and (b) Connection error 49
3.18 Program operational interface 50
3.19 F/T function software 51
4.1 Overview of position control law for UGBB robotic system 54 4.2 3D modelling of Mitsubishi RV-2AJ robotic arms 55
4.3 CAD translation method 56
4.4 SimMechanics model of RV-2AJ in (a) Simulink
environment and (b) simulation model 57
4.5 Joint actuator input control 58
4.6 Inverse and forward kinematics for RV-2AJ robot controller 58
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4.7 RV-2AJ robot arm joints 59
4.8 Frame assignment of RV-2AJ robot 60
4.9 RV-2AJ robot at home position in (a) simulation and (b)
experiment 63
4.10 RV-2AJ robot at second position in (a) simulation and (b)
experiment 64
4.11 RV-2AJ robot at third position in (a) simulation and (b)
experiment 65
4.12 RV-2AJ robot at fourth position in (a) simulation and (b)
experiment 66
4.13 RV-2AJ robot at fifth position in (a) simulation and (b)
experiment 67
4.14 Waist joint angle, θ1 69
4.15 RV-2AJ robot with wrist pitch at vertical position 70 4.16 Shoulder joint angle, θ2 to wrist joint angle, θ4 71
4.17 RV-2AJ robot at first configuration 75
4.18 RV-2AJ robot at second configuration 77
4.19 RV-2AJ robot at third configuration 78
4.20 Comparison of RV-2AJ robot at several random positions in
experiment and simulation 81
5.1 Black-box modelling approach on soft tissue phantom 85
5.2 General steps in system identification 86
5.3 Schematic diagram for breast phantom data acquisition 87 5.4 Data acquisition setup for (a) generic tissue phantom and (b)
breast phantom 89
5.5 Input signals (robot travel towards phantom) and output signals (acting force towards robot) for (a) generic tissue
phantom and (b) breast phantom 90
5.6 Simulation and actual response of system output for (a)
generic tissue phantom and (b) breast phantom 93 5.7 Autocorrelation and cross correlation analysis for [2 2 0]
nonlinear ARX sigmoid estimator for (a) generic tissue
phantom and (b) breast phantom 94
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5.8 Measured vs simulation of final mean value for (a) generic
tissue phantom and (b) breast phantom 95
5.9 Respiration disturbance in UGBB robotic system 98 5.10 Effect of breast motion during respiration to UGBB robotic
system 99
5.11 Force and position response of respiration disturbance from
breast’s CTV movement 100
5.12 Force and position response for respiration disturbance from
breast movement 101
6.1 Schematic diagram for UGBB robotic system 104 6.2 Contact operation on breast biopsy procedure 105 6.3 Force response for proportional force controller 106 6.4 Force response for PI force controller (a) without disturbance
and (b) with respiration disturbance 108
6.5 Schematic diagram of PI-Fuzzy force controller 109 6.6 Control surface of PI-Fuzzy force controller 110 6.7 Force response for PI-Fuzzy force controller (a) without
disturbance and (b) with disturbance 112
6.8 Schematic diagram of F-PID force controller 113 6.9 Control surface of F-PID force controller 114 6.10 Force response for F-PID force controller (a) without
respiration and (b) with respiration disturbance 115 6.11 Unit step response of control system (Ogata, 2009) 117 6.12 Step response of (a) PI force controller and (b) F-PID force
controller for a step input of 1N 118
6.13 Step response of (a) PI force controller and (b) F-PID force
controller for a step input of 7N 119
6.14 (a) Force and (b) position response of position control system
without force controller 122
6.15 (a) Force and (b) position response of force and position
control system without disturbance 124
6.16 (a) Force and (b) position response of force and position
control system with respiration disturbance 125
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6.17 (a) Force and (b) position response of force and position
control system with extreme disturbance 126
6.18 (a) Force and (b) position response with double amplitude of
respiration disturbance 128
6.19 (a) Force and (b) position response with double frequency of
respiration disturbance 129
6.20 (a) Force and (b) position response with double amplitude
and double frequency of respiration disturbance 130 6.21 (a) Force and (b) position response for Fd = 1N 132
6.22 (a) Force and (b) position response for Fd = 3N 133
6.23 (a) Force and (b) position response for Fd = 5N 134
6.24 (a) Force and (b) position response for Fd = 7N 135
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LIST OF SYMBOLS AND ABBREVIATIONS ° - Degree 𝜃 - Theta % - Percentage 2D - Two-Dimensional 3D - Three-Dimensional
ACF - Autocorrelation Function ARX - Auto Regression Exogenous ASR - Age-Standardized Rate
CAD - Computer-Aided Design
CCF - Cross Correlation Function CTV - Clinical Target Volume
D-H - Denavit-Hartenberg
DC - Direct Current
DCBF - Digital Continuous Beam Former
DLL - Dynamic-Link Library
DMS - Dead Man’s Switch
DOF - Degree of Freedom
F-PID - Fuzzy PID
FL - Fuzzy Logic
F/T - Force/Torque
FES - Functional Electrical Stimulation
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FIS - Fuzzy Inference System
FNAC - Fine Needle Aspiration Cytology
FOV - Field of View
g - Gram
GUI - Graphical User Interface
Hz - Hertz
IAE - Integral of Absolute Error
IARC - International Agency for Research on Cancer ILC - Iterative Learning Control
ITAE - Integral of Time Absolute Error
kg - Kilogram
LAD - Left Anterior Descending
LED - Light-Emitting Diode
mm - Millimeter
MRC - Modified Rate Control
ms - Milliseconds
N - Newton
N-mm - Newton-millimeter
NARX - Nonlinear ARX
NCR - National Cancer Registry NNARX - Neural Network with ARX
PC - Personal Computer
PFC - Predictive Functional Control PI - Proportional Integral
PID - Proportional, Integral and Derivative
PM - Pneumatic Muscles
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RLS - Recursive Least Square RSS - Robot Simulation Software
s - Seconds
SD - Standard Deviation
UGBB - Ultrasound-Guided Breast Biopsy
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LIST OF APPENDICES
APPENDIX TITLE PAGE
A Mitsubishi RV-2AJ Robot 151
B1 ATI Mini40 F/T Sensor 154
B2 Detailed Certificate of Calibration for ATI Mini40 F/T Sensor
158
C1 Arm Parameters of Forward Kinematics 162
C2 Transformation Matrices of Forward Kinematics 164 D Simplified Schematic Diagram for UGBB Robotic
System in MATLAB
165
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CHAPTER 1
INTRODUCTION
The International Agency for Research on Cancer (IARC) reported in 2012 that breast cancer is the second most common cancer in the world and the most frequent cancer among women (Ferlay et al., 2015). It was estimated that 1.67 millions new breast cancer cases were diagnosed and was ranked as the fifth cause of death (522,000 deaths) from overall cancers. In Malaysia, the IARC estimated an Age-Standardized Rate (ASR) of breast cancer as 38.7 per 100,000 with 5410 new cases in 2012.
According to the 5-year Malaysian National Cancer Registry (NCR) report for new cancers diagnosed from 2007 to 2011, breast cancer in Malaysia accounted to the most common cancer for all residents by 17.7% and the most frequent cancer among women by 32.1% (Ministry of Health, 2016). Overall, Malaysian women have poor survival from breast cancer due to poor understanding of the risk factors, symptoms and methods for early detection of breast cancer (Yip et al., 2014). Otherwise, it is approximated that half of the deaths due to breast cancer could be prevented, considering that survival rates essentially depend on the timely discovery of the disease, which is at the early stage of the unregulated cell growth.
As stated by Bruening et al. (2009), women diagnosed with initial phases of the breast cancer have a 5-year survival rate of making a full recovery. The standard diagnostic procedure is denoted as triple-assessment, which basically comprises of three medical inspections to attain great assurance in the diagnosis. They are palpation (self-examination or by a specialist), breast imaging (a combination of mammography and ultrasound) followed by fine needle biopsy if necessary.
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Two notable approaches for breast biopsy include needle biopsy and open excisional biopsy (Mayo Clinic, 2014). Image-guided needle biopsy is more appealing because it is less traumatic, produces little or no scar (Fine & Staren, 2006; Killebrew & Oneson, 2006; Koskela et al., 2006), allows quicker recovery, and substantially low cost than open surgical biopsy (Zuiani et al., 2005). The procedure is an invasive assessment that incorporates in acquiring the sample cells from the suspicious lump. The obtained cells are then examined under a microscope by a pathologist responsible for the diagnosis to confirm the presence of cancer cells. Since the biopsy work requires comprehensive knowledge and technical proficiency, computer-aided diagnosis can assist radiologist and help in getting the results.
Initial clinical experiment has shown that medical robot has greater precision, faster execution and less risks of infection compared to handheld approaches in conducting breast biopsy (Rovetta, 2000). Some other possible advantages include less risk, pain, recovery time, cost-effective, reduce radiologist’s fatigue and fewer needle placements to correctly sample the lump (V. Mallapragada et al., 2011; Nelson et al., 2010). Thus, an ideal case of medical assist equipment is to use robotic instruments with the knowledge and experience of the radiologist to pilot the robot. Clinical matters to be tackled involve positioning feedback to the operator via force feedback and image verification or a combination of sensor systems for sampling device insertion (Nelson et al., 2012). Nonetheless, concerns regarding large learning curves, lesser radiologist tactile feedback and reasonably high implementation cost will need to be confronted before widespread clinical approval is possible (Gerhardus, 2003).
Small incision during Fine Needle Aspiration Cytology (FNAC) of the breast is a minimally invasive procedure that resulted in a very limited Field of View (FOV) to the naked eyes of the radiologist. Hence, one of the most crucial instruments is an image guidance device such as an ultrasound transducer that can provide analytical information as to the nature of a breast lesion and the positional tip of the biopsy needle. At present, the radiologist has to hold both transducer and biopsy needle simultaneously as shown in Figure 1.1 to effectively conduct the Ultrasound-Guided Breast Biopsy (UGBB) procedure. However, even though handheld approaches can provide accurate sampling of breast lesions, accuracy differs from one radiologist to another (Salem et al., 2009). Besides, a precise localization gradually becomes further challenging for deeply located and smaller sized breast lesions even with exceptional training and high level of experience (Nelson et al., 2012).
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Figure 1.1: Ultrasound-guided core needle biopsy (Mayo Clinic, 2014)
During the UGBB procedure, the needle approach to the lesion has to be as parallel to the chest wall as possible in order to avoid the worst case scenario of a pneumothorax complication (collapsed lung) where it can be a serious life-threatening event (Apesteguía & Pina, 2011). Likewise, the orientation of the ultrasound transducer should be parallel to the needle as well to easily visualize the needle. Formerly, an Italian study of more than 200 000 breast biopsy procedures described that pneumothorax life-threatening complication occurred in 1 in 10 000 cases (0.01%) (Catania et al., 1993). However, the authors admitted that this figure might be underrated due to unrecognized and asymptomatic cases of pneumothoraxes.
Similar incident was first reported in 2005 to a woman having a mild form of Poland’s syndrome (absence of chest muscle) (Salhab et al., 2005). Patients with deeply located central lesions and patients with thin bodies and small sized breasts are also at greater risk for developing this complication. As such, the handling of multiple devices simultaneously is believed to be one of the contributing factors that deteriorated radiologist skills in performing the biopsy procedure, thus leading to the pneumothorax complication.
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Additionally, inconsistent contact forces from the ultrasound transducer to the breast can lead to repeatability problem for similar acquisition state and images of the same tissue region (Gilbertson & Anthony, 2015). The uncontrollable forces along with hand tremors from the radiologist encourage the suspected lump to move away from its original position. Even though the radiologist continuously tries to maintain constant forces, the slippery condition of the ultrasound gel makes it possible for the transducer to lose contact with the breast subsequently making the lump disappear from the ultrasound-imaging FOV. Furthermore, due to the dynamic structures of the breast tissue, large deformations occurred inside the breast whenever the biopsy needle is inserted (DiMaio & Salcudean, 2002; V. Mallapragada et al., 2008a). Ultimately, multiple insertions at the same biopsy region might be necessary to successfully sample the target which in return further compromising the integrity of the breast tissue.
Therefore, a robust hybrid force/position control of a UGBB robotic system for manoeuvring the ultrasound transducer is proposed in order to maintain low, safe and consistent contact force on the breast. By implementing this method, expected advantages include enabling full control on needle handling and higher concentration to the radiologist during lump sampling. Furthermore, other anticipated benefits include compensation for external disturbances to the UGBB robotic system, which offers the ability to indirectly track the position of the suspected lump and reducing the number of unnecessary needle insertion.
1.1 Problem Statement
Current researches and developments of UGBB robotic system are more focused on the enhancement of needle position and insertion, but rather neglect to address force and position control of the ultrasound transducer itself as a significant element (Hatano et al., 2011; Kobayashi et al., 2012; Liang et al., 2010; V. Mallapragada et al., 2011; Nelson et al., 2012; Okazawa et al., 2005). Radiologist has to handle multiple devices at once which could reduce the abilities in performing such procedure resulting in radiologist’s fatigue, compromising the structural integrity of the breast tissue due to multiple insertions and susceptibilities to pneumothorax complication for the patient.
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REFERENCES
Apesteguía, L., & Pina, L. J. (2011). Ultrasound-guided core-needle biopsy of breast lesions. Insights into Imaging, 2(4), 493–500. http://doi.org/10.1007/s13244-011-0090-7
Atashzar, S. F., Khalaji, I., Shahbazi, M., Talasaz, A., Patel, R. V., & Naish, M. D. (2013). Robot-assisted lung motion compensation during needle insertion. 2013 IEEE International Conference on Robotics and Automation, 1682–1687. http://doi.org/10.1109/ICRA.2013.6630796
ATI Industrial Automation. (2012). Six-Axis Force/Torque Sensor System.
ATI Industrial Automation. (2013). F/T System Interfaces. Retrieved from http://www.ati-ia.com/products/ft/ft_SystemInterfaces.aspx
Batra, S., Teo, K., Vennarini, S., Lin, L. L., & Freedman, G. M. (2013). An Analysis of 3-Dimensional Breast Motion During Normal Respiration: What CTV to PTV Margin Is Needed? International Journal of Radiation Oncology*Biology*Physics, 87(2), S197. http://doi.org/10.1016/j.ijrobp.2013.06.509
BIMEDIS. (2010a). Toshiba Nemio SSA-550A. Retrieved July 13, 2013, from https://bimedis.com/a-item/ultrasound-machines-toshiba-nemio-10-ssa-550a-10-696421 BIMEDIS. (2010b). Toshiba PLM-805AT. Retrieved July 13, 2013, from
https://bimedis.com/a-item/linear-probes-toshiba-plm-805at-732593/732593/linear-probes-toshiba-plm-805at
Bruening, W., Schoelles, K., Treadwell, J., Launders, J., Fontanarosa, J., & Tipton, K. (2009). Comparative Effectiveness of Core-Needle and Open Surgical Biopsy for the Diagnosis of Breast Lesions. Retrieved from www.effectivehealthcare.ahrq.gov/reports/final.cfm Buitrago, J. a., Giraldo, F. D., & Lamprea, J. a. (2011). Remote access lab for
Mitsubishi RV-2AJ robot. In IX Latin American Robotics Symposium and IEEE Colombian Conference on Automatic Control, 2011 IEEE (pp. 1–7). IEEE. http://doi.org/10.1109/LARC.2011.6086802
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