[ieee 2009 5th international symposium on applied computational intelligence and informatics (saci)...

133978-1-4244-4478-6/09/$25.00 ©2009 IEEE

5th International Symposium on Applied Computational Intelligence and Informatics • May 28–29, 2009 – Timişoara, Romania

Spatial Accuracy of Surgical Robots

Tamás Haidegger, Levente Kovács, Balázs Benyó, Zoltán Benyó

Spatial Accuracy of Surgical Robots

T. Haidegger, L. Kovács, B. Benyó and Z. Benyó

Budapest University of Technology and Economics, Dept. of Control Engineering and Information Technology

(BME - IIT) – Biomedical Engineering Laboratory, Budapest, Hungary

E-mail: [email protected], [email protected], bbenyo@ iit.bme.hu, benyo@ iit.bme.hu

Abstract—Robots have been introduced to the operating

room primarily to provide higher accuracy and dexterity.

Mechatronic devices can support surgeons with advanced

targeting, visualization and task execution with a precision

beyond the human skills. To evaluate a system, accuracy

tests are required, and proper methodology should be

applied to describe its properties. It is crucial in

interventional medicine to test the application accuracy of a

system, showing the overall task execution error. This may

be a non-linear function of the intrinsic- and registration

accuracies, as described in the paper. Different methods are

presented to provide system characteristics. Also, the most

important surgical robot systems are introduced in details

along with their published accuracy measures. Despite the

fact that the added value of a surgical robot is usually

consistent with the precision it can achieve, the importance

of overall safety, predictability and transparency many

times overcomes the need for spatial precision.

I. INTRODUCTION

Computer-Integrated Surgery (CIS) refers to the entire field of interventional medicine, theory and technology from image processing and augmented reality applications to automated tissue ablation. CIS means the combination of innovative algorithms, robotic devices, imaging systems, sensors and human-machine interfaces to work cooperatively with physicians in the planning and execution of surgical procedures [1]. A subfield of it is called Image Guided Surgery (IGS), where the digital system does not necessarily take part in the physical part of the operation, but improves the quality of surgery by better visualization or guidance. IGS means the real-time registration (correlation and mapping) of the operative field to a preoperative (MR, CT) imaging or intraoperative (ultrasound, fluoroscopy) data set of the patient, providing free-hand navigation, positioning accuracy of equipment, or guidance for mechatronic systems. IGS has been successfully used in neurosurgery, and has also had a major impact on pediatrics, orthopedics and other fields.

Robotic surgery is defined by the SAGES-MIRA Robotic Consensus Group [2] as “A surgical procedure or technology that adds a computer-technology-enhanced device to the interaction between the surgeon and the patient during a surgical operation, and assumes some degree of freedom of control heretofore completely reserved for the surgeon. This definition encompasses micro-manipulators, remotely controlled endoscopes and console-manipulator devices. The key elements are enhancement of the surgeon’s abilities—by the vision, tissue manipulation, or tissue sensing—and alteration of the traditional direct local contact between surgeon and patient.” This incorporates smart tools and intelligent

devices from hand-held biopsy needles to remote telepresence systems.

From the clinical aspect, most of the robots are intended for Minimally Invasive Surgery (MIS), reducing the patient trauma and therefore shortening the recovery time. MIS originally referred to the laparoscopic procedures (keyhole surgery), where the abdominal cavity is accessed through 3–5 small incisions (0.5–3 cm). This technique has been applied and improved by the complete teleoperated surgical robots where the laparoscope and surgical instruments are moved by the manipulators. The main advantages of surgical robots based on [3] are:

• Superior 3D spatial accuracy, predictable motion

and stability of the instruments

• Additional degrees of freedom compensating for

spatial limitations of MIS

• Ergonomics features including the option of

integrated 3D vision system, motion scaling

• Reduced patient trauma and hospitalization

• Standardization, planning and reproduction of

the operation

• Autonomous functions such as tissue-stitching,

motion compensation

• Feasibility of teleoperation

II. DIFFERENT ACCURACIES IN CIS

Spatial precision of robotic systems can be represented by the accuracy and repeatability of the device to characterize the overall effect of the precision of the encoders, the compliance of the hardware elements (e.g. the servos) and the rigidity of the structure. Generally, the absolute positioning accuracy shows the error of the robot when reaching for a prescribed position, while repeatability expresses the variability of the positioning error acquired through multiple trials. Typically, repeatability is smaller for manipulators.

In indirect image guided surgery, 3–5 mm accuracy is considered acceptable, whereas 2 mm is recommended for IG neurosurgery. However, in image guided robotics (direct IGS), sub-millimeter accuracy is recommended. Localization error can originate from the imperfection of different software and hardware elements. There are three different types of accuracies that can be separated for integrated interventional CIS systems [4], [5]:

• Intrinsic (technical) accuracy (typically 0.1–0.6 mm)

• Registration accuracy (typ. 0.2–3 mm )

• Application accuracy (typ. 0.6–10 mm )

Intrinsic accuracy applies to certain elements, such as the robot or the localizer. It describes the average error of the component in operational use. Random errors (e.g.

Tamás Haidegger, Levente Kovács, Balázs Benyó, Zoltán Benyó • Spatial Accuracy of Surgical Robots

134

Figure 1. Definition of FRE and TRE. The black and white circles

represent corresponding point pairs in the two different spaces.

FRE is the residual error of the points used to derive the Ttraf

transformation, while TRE is the mapping the error of a (set of)

independent points.

mechanical compliance, friction, loose hardware), resolu- tion of the imaging device, inadequate control and noise can all result in low intrinsic accuracy. On the user interface side, discretized input and modeling errors may further decrease the precision. All registration methods involve some kind of errors, as it is only possible compute a normalized (e.g. least squares) solution for a mathematical fitting problem. In IGS, a major source of error can be the markers (different types, forms and materials), displacement of the fiducials and determination of the center of the fiducials. Application accuracy refers to the overall targeting error of the integrated system while used in a clinical or clinical-like setup. This measures most realistically the effectiveness of a system and is used for validation. The application accuracy is dependent on all other sources of errors in a complex, non-linear way, therefore typically phantom, cadaver and clinical trials are required to determine it.

In the case of a CIS system, application accuracy can also be affected by the many changing factors in the operating room (OR). There are several people in the OR, constantly in motion among the numerous medical devices surrounding the patient. IGS is based on the principle that the real-world setup does not change unpredictably over time and its registration to the still image space thus remains valid. IGS is sensitive to spatial changes, e.g., when the patient is unintentionally moved relative to the marker that tracks its motion.

Current research projects are trying to increase the utility of the surgical equipment along different strategies [6]. Three areas of improvement are mainly in focus:

• augmenting the overall accuracy and/or efficacy of classic stereotactic systems

• increasing the added-value of robotic procedures

• further enhancing the capabilities of the human surgeon by providing smarter tools.

A common strategy is to keep the human in the control loop at all times through real-time sensory feedback. Most effective is the visual feedback that can be achieved e.g. through endoscopic cameras. This ensures that the operator can compensate for any visible error, improving the effectiveness and the overall application accuracy of the system. However, the intrinsic accuracy of the components can still be significant. To objectively evaluate the performance of a robot-assisted system, it is crucial to understand and apply consistent measurement methods.

III. ACCURACY MEASURES

In industrial robotics, accuracy measures and tests have been widely used, and some got straight applied to CIS. Most common, a tracked device is guided (directed) to different positions and orientations along a precisely known set of landmarks (fiducials). The positions are recorded with an independent localizer, typically an optical tracking system.

To evaluate the different tests, certain measures are used. Specific to the tracker and the setup, the Fiducial Localization Error (FLE) includes the intrinsic and extrinsic sources of error, representing the accuracy of a position tracker to localize a point, the centroid of the cluster of measured points [7]. FLE can be defined as the expected value of the error (E[]) of all samples.

( , )ε

⋅ trial fiducial

1FLE = trial fiducial

trial fiducial (1)

where is the RMS error of a single measurement at a given fiducial.

One of the most precise optical tracker currently available is the Optotrak from Northern Digital Inc. (NDI, Waterloo, Ontario, Canada). It has a 0.1–0.15 mm root mean square (RMS) FLE according to the specifications, and capable of tracking up to 512 markers with 1.5 KHz update rate. Typical surgical navigation systems provide less accurate measurements that have a 0.2–1 mm RMS error.

Fiducial Registration Error (FRE) is the mean square distance between homologous fiducial points, the residual error of the paired-point registration between the given subset of the known and recorded fiducial coordinates during an accuracy test.

N

22i i

i

1FRE = T(p ) - q

N (2)

where N is the number of fiducials used during the registration and qi is the position of the ith fiducial in one space (e.g. the robot), pi is the same point in the other (e.g. CNC) space and T is the computed homogenous transformation connecting the two spaces [8]. In ideal case the FRE equals zero. FRE is connected to FLE through

2 22FRE = 1- FLE

N. (3)

Target Registration Error (TRE) is the deviation between definite points in the reference and the other (registered) coordinate system. For example, TRE is the average error of locating markers (that were not used for registration) in robot coordinates (Fig. 1). TRE is typically used for the characterization of schematic point-based registrations. Usually a given subset of the points is used for registration and the rest for TRE computation. TRE is related to FLE through

3 2

2

1

1 1( )

3=

= + 2 2 i

ii

dTRE r FLE

N f (4)


135

Figure 1. Definition of FRE and TRE. The black and white circles

represent corresponding point pairs in the two different spaces.

FRE is the residual error of the points used to derive the Ttraf

transformation, while TRE is the mapping the error of a (set of)

independent points.

mechanical compliance, friction, loose hardware), resolu- tion of the imaging device, inadequate control and noise can all result in low intrinsic accuracy. On the user interface side, discretized input and modeling errors may further decrease the precision. All registration methods involve some kind of errors, as it is only possible compute a normalized (e.g. least squares) solution for a mathematical fitting problem. In IGS, a major source of error can be the markers (different types, forms and materials), displacement of the fiducials and determination of the center of the fiducials. Application accuracy refers to the overall targeting error of the integrated system while used in a clinical or clinical-like setup. This measures most realistically the effectiveness of a system and is used for validation. The application accuracy is dependent on all other sources of errors in a complex, non-linear way, therefore typically phantom, cadaver and clinical trials are required to determine it.

In the case of a CIS system, application accuracy can also be affected by the many changing factors in the operating room (OR). There are several people in the OR, constantly in motion among the numerous medical devices surrounding the patient. IGS is based on the principle that the real-world setup does not change unpredictably over time and its registration to the still image space thus remains valid. IGS is sensitive to spatial changes, e.g., when the patient is unintentionally moved relative to the marker that tracks its motion.

Current research projects are trying to increase the utility of the surgical equipment along different strategies [6]. Three areas of improvement are mainly in focus:

• augmenting the overall accuracy and/or efficacy of classic stereotactic systems

• increasing the added-value of robotic procedures

• further enhancing the capabilities of the human surgeon by providing smarter tools.

A common strategy is to keep the human in the control loop at all times through real-time sensory feedback. Most effective is the visual feedback that can be achieved e.g. through endoscopic cameras. This ensures that the operator can compensate for any visible error, improving the effectiveness and the overall application accuracy of the system. However, the intrinsic accuracy of the components can still be significant. To objectively evaluate the performance of a robot-assisted system, it is crucial to understand and apply consistent measurement methods.

III. ACCURACY MEASURES

In industrial robotics, accuracy measures and tests have been widely used, and some got straight applied to CIS. Most common, a tracked device is guided (directed) to different positions and orientations along a precisely known set of landmarks (fiducials). The positions are recorded with an independent localizer, typically an optical tracking system.

To evaluate the different tests, certain measures are used. Specific to the tracker and the setup, the Fiducial Localization Error (FLE) includes the intrinsic and extrinsic sources of error, representing the accuracy of a position tracker to localize a point, the centroid of the cluster of measured points [7]. FLE can be defined as the expected value of the error (E[]) of all samples.

( , )ε

⋅ trial fiducial

1FLE = trial fiducial

trial fiducial (1)

where is the RMS error of a single measurement at a given fiducial.

One of the most precise optical tracker currently available is the Optotrak from Northern Digital Inc. (NDI, Waterloo, Ontario, Canada). It has a 0.1–0.15 mm root mean square (RMS) FLE according to the specifications, and capable of tracking up to 512 markers with 1.5 KHz update rate. Typical surgical navigation systems provide less accurate measurements that have a 0.2–1 mm RMS error.

Fiducial Registration Error (FRE) is the mean square distance between homologous fiducial points, the residual error of the paired-point registration between the given subset of the known and recorded fiducial coordinates during an accuracy test.

N

22i i

i

1FRE = T(p ) - q

N (2)

where N is the number of fiducials used during the registration and qi is the position of the ith fiducial in one space (e.g. the robot), pi is the same point in the other (e.g. CNC) space and T is the computed homogenous transformation connecting the two spaces [8]. In ideal case the FRE equals zero. FRE is connected to FLE through

2 22FRE = 1- FLE

N. (3)

Target Registration Error (TRE) is the deviation between definite points in the reference and the other (registered) coordinate system. For example, TRE is the average error of locating markers (that were not used for registration) in robot coordinates (Fig. 1). TRE is typically used for the characterization of schematic point-based registrations. Usually a given subset of the points is used for registration and the rest for TRE computation. TRE is related to FLE through

3 2

2

1

1 1( )

3=

= + 2 2 i

ii

dTRE r FLE

N f (4)

Figure 2. The ROBODOC orthopedic system for bone milling in

hip and knee replacement surgeries. Credit: ISS

Figure 2. The new design of the NeuroMate stereotactic robot.

Credit: Renishaw plc.

where r is the target point, N the number of fiducials, di the distance of the target from the principle axis i of the fiducial points and fi is the RMS distance of all the fiducial points from that same axis [9].

IV. PRECISION OPERATIONS WITH ROBOTS

In the case of robotic surgery, there is a great need for high precision, and CIS offers various possibilities to improve and augment human dexterity. The history of robot assisted interventions dates back to the mid 1980s. The first robot used on a human patient was a Unimate Puma 200, manipulating a biopsy cannulae using a Brown-Roberts-Wells stereotactic frame. The operation took place in the Memorial Medical Center (Long Beach, CA, USA) in 1985. The repeatability of the robot was 0.05 mm with an overall application accuracy of 2 mm [10]. Since then, the need for high precision MIS operations has greatly increased, and many systems have been developed explicitly for medical interventions. Major surgical robotic systems addressing the problem of precision in different ways are presented in the following section.

A. ROBODOC

Integrated Surgical Systems Inc. (ISS – Sacramento, CA, USA) was one of the earliest companies in the field, founded in 1990. ISS had developed two image-directed, semiautonomous robotic products for neurological and orthopedic surgical applications.

The ROBODOC Surgical Assistant System was developed together with IBM T. J. Watson Center and U.C. Davis [11]. A 5 degree of freedom (DOF) IBM SCARA robot was custom designed for total hip arthroplasty (surgical shaping or alteration of the joint). The 3D planning station (called Orthodoc) together with the ROBODOC use pre-surgical images and software to first design the surgical procedure (Fig. 2). Surgeon can precisely define the desired cavity in the bone, size and position the prosthesis before the real surgery. The first ever robotic human trial was performed in 1992.

ROBODOC drills the bone without direct human control of the tool during the procedure; therefore the application accuracy of the system is critical. The robot has a 0.5 mm intrinsic accuracy, while the application accuracy was 1.2 mm in average, ranging from 1.0 to 3.5 mm in cadaver trials [12]. The later version of the device had around 0.1 mm dimensional error and 1.0 mm placement error, providing over 95% implant-bone contact [13]. More than 80 units were sold worldwide (in Europe and Asia), being used for both hip and knee surgery. The

system got approved by US Food and Drug Administration (FDA) in August 2008. ISS did not make it to the end of the clearance procedure, the system is now selling under ROBODOC, a Curexo Technology Corporation.

B. NeuroMate

In 1997 ISS licensed the 5 DOF NeuroMate stereotactic neurosurgery system from Innovative Medical Machines International (Lyon, France). The robot was used for surgical assistance with biopsy and tumor removal. Combined with pre-operative images, the NeuroMate system provided real-time visualization to give surgeon precise location of a tumor. This was the first neurosurgical robotic device to get CE mark (Conformité Européene) in Europe, and then FDA approval in 1997.

The intrinsic accuracy was reported to be 0.75 mm with a repeatability of 0.15 mm [14]. In a human stereotactic surgical setup, conducted in 2002, the application accuracy was 0.95 ± 0.44 mm (mean ± standard deviation) [15]. In a more recent study the intrinsic accuracy of the robot was 0.36 mm FRE and 0.36 ± 0.17 mm TRE [16].

In the first couple of years of operation, the company has installed around 25 NeuroMate systems in the United States, Europe and Japan. The NeuroMate technology was acquired by Schaerer Mayfield NeuroMate AG (Lyon, France), and after a more recent redesign, it might reappear on the market soon in Renishaw plc’s product line (Fig. 3) [17].

C. The da Vinci surgical system

Unquestionably, the most successful surgical robot is the da Vinci (Fig. 4) from Intuitive Surgical Inc. (Sunnyvale, CA, USA). The robot is the only complete teleoperation surgical robot currently available, created with a 500M USD investment. It is capable of performing complex surgical procedures with laparoscopic technique, guided remotely by a skilled surgeon. Intuitive Surgical was founded in 1995, licensing many promising technologies, and by 1997 the first prototype (Lenny) was ready for animal trials. Prototype Mona performed the first human trials in Belgium in 1997, and the first da Vinci was created within a year [18]. Eventually, the


136

Figure 5. The da Vinci S complete teleoperation system for MIS.

The patient chart consists of four slave manipulators guided by the surgeon at the master controller. Credit: Intuitive Surgical Inc.

Figure 4. The B-ROB II developed for percutaneous interventions.

The linear positioning stage allows for submillimeter positioning [5].

FDA approved the system for general laparoscopic surgery (gallbladder, gastroesophageal reflux and gynecologic surgery) in July 2000, followed by many other approvals. The original da Vinci consists of two 6 DOF slave manipulators, a 6 DOF stereo endoscopic camera holder arm, and a separated master controller consol that records the surgeons hand motions with special 6 DOF interfaces, and provides high quality 3D video feedback.

To compensate for any application errors (and to avoid safety hazards), the da Vinci robot only follows the movements of the surgeon, applying tremor filtering and motion scaling at the most. The intrinsic accuracy of the robot was measured to be 1.35 mm TRE (with the points not used in registration), with FLE 1.02 ± 0.58 mm [9].

Once the da Vinci was released, Intuitive continued perfecting the system, and the second generation, the da Vinci S, got ready in 2005 (Fig. 4). FLE for da Vinci S was measured to be 1.05 ± 0.24 mm [19]. The latest version, the da Vinci Si was released in April 2009 with improved hardware and software capabilities.

According to Intuitive [20], up to December 2008, there have been 1,111 da Vinci units sold, 825 in the US, 194 in Europe and 92 in the rest of the world, and the number of procedures performed is well over 300,000. The most successful application of the robot became prostatectomy, approximately 70% of all radical prostate removal procedures were performed with the da Vinci in 2008 in the USA.

D. B-Rob system

A robotic system for CT and ultrasound-guided biopsies was developed by the robotics laboratory of ARC Seibersdorf Research in Austria. The first B-Rob I prototype was a 7 DOF robot integrated on a mobile rack. A 4 DOF positioning stage was used to direct the needle to the desired skin entry point. The complete system was thoroughly tested on needle-penetrable phantoms, where its application accuracy was 1.48 ± 0.62 mm, which is better than the traditional free-hand technique [21].

The development of the second prototype (Fig. 5) was motivated by the aim to provide a modular setup for a broad variety of clinical applications, easy integration with other systems, reduction of technical complexity and costs. The robot was equipped with a ‘‘Needle Positioning Unit’’ (NPU) for fine orientation. The first gel phantom tests of the B-Rob II showed 0.66 ± 0.27 mm application

accuracy in IG positioning. In-vitro trials with ultrasound guided biopsy specimen harvests followed, where the mean deviation of the needle tip from the center of the target was 1.1 ± 0.8 mm.

E. CyberKnife

One of the most successful robotic applications is the CyberKnife from Accuray Inc. (Sunnyvale, CA, USA). This stereotactic radiosurgery system integrates IGS with robotic positioning. The 6 MeV LINAC relatively light-weight photon device is mounted on a KUKA 6 DOF industrial manipulator (Fig. 6). Its primary deployment is the irradiation of brain and spine tumors. X-ray cameras are used to track the spatial displacement of the patient and compensate for motion caused by e.g. breathing. The overall accuracy of the system is 0.42 ± 0.4 mm, while patient skin motions are detected with a 0.35 mm precision [22]. The CyberKnife moves the radiation beam by physically repositioning the radiation source. It uses intra-corpuscular markers and Polaris (NDI Inc.) infrared cameras to track the patients’ moving body surface.

To improve accuracy, radioopaque fiducial markers are implanted in or near the tumor region several days before CT scanning for treatment planning. The fiducials, which are detectable in X-ray images, are used as reference markers to locate and track tumor location during patient alignment and treatment delivery. The Synchrony Respiratory Tracking System builds a correlation model between the positions of periodically detected fiducials and the real-time locations of optically tracked markers placed on the chest to track tumor location. It uses 4D-CT (imaging through time) to measure respiratory tissue motion and deformation and to account for the effect of displacement and deformation through the irradiation [23].

V. THE REAL NEED FOR ACCURACY

Surgical robots are involved in interventional robotics primarily to provide patient benefits through increased precision and minimally invasiveness. Even more, robotic devices should allow for procedures that are not possible to perform by humans. The above presented robot systems all have an application accuracy between 1–2 mm. This is less precise than robotic setups in industrial applications, as the accumulation of different errors in IGS prevents real sub-millimeter accuracy as claimed in the case of many systems. Beyond spatial accuracy, many other factors determine the success of a surgical robot once released to the market. Assuming clinical benefits and adequate safety measures, a surgical robotic system


137

Figure 5. The da Vinci S complete teleoperation system for MIS.

The patient chart consists of four slave manipulators guided by the surgeon at the master controller. Credit: Intuitive Surgical Inc.

Figure 4. The B-ROB II developed for percutaneous interventions.

The linear positioning stage allows for submillimeter positioning [5].

FDA approved the system for general laparoscopic surgery (gallbladder, gastroesophageal reflux and gynecologic surgery) in July 2000, followed by many other approvals. The original da Vinci consists of two 6 DOF slave manipulators, a 6 DOF stereo endoscopic camera holder arm, and a separated master controller consol that records the surgeons hand motions with special 6 DOF interfaces, and provides high quality 3D video feedback.

To compensate for any application errors (and to avoid safety hazards), the da Vinci robot only follows the movements of the surgeon, applying tremor filtering and motion scaling at the most. The intrinsic accuracy of the robot was measured to be 1.35 mm TRE (with the points not used in registration), with FLE 1.02 ± 0.58 mm [9].

Once the da Vinci was released, Intuitive continued perfecting the system, and the second generation, the da Vinci S, got ready in 2005 (Fig. 4). FLE for da Vinci S was measured to be 1.05 ± 0.24 mm [19]. The latest version, the da Vinci Si was released in April 2009 with improved hardware and software capabilities.

According to Intuitive [20], up to December 2008, there have been 1,111 da Vinci units sold, 825 in the US, 194 in Europe and 92 in the rest of the world, and the number of procedures performed is well over 300,000. The most successful application of the robot became prostatectomy, approximately 70% of all radical prostate removal procedures were performed with the da Vinci in 2008 in the USA.

D. B-Rob system

A robotic system for CT and ultrasound-guided biopsies was developed by the robotics laboratory of ARC Seibersdorf Research in Austria. The first B-Rob I prototype was a 7 DOF robot integrated on a mobile rack. A 4 DOF positioning stage was used to direct the needle to the desired skin entry point. The complete system was thoroughly tested on needle-penetrable phantoms, where its application accuracy was 1.48 ± 0.62 mm, which is better than the traditional free-hand technique [21].

The development of the second prototype (Fig. 5) was motivated by the aim to provide a modular setup for a broad variety of clinical applications, easy integration with other systems, reduction of technical complexity and costs. The robot was equipped with a ‘‘Needle Positioning Unit’’ (NPU) for fine orientation. The first gel phantom tests of the B-Rob II showed 0.66 ± 0.27 mm application

accuracy in IG positioning. In-vitro trials with ultrasound guided biopsy specimen harvests followed, where the mean deviation of the needle tip from the center of the target was 1.1 ± 0.8 mm.

E. CyberKnife

One of the most successful robotic applications is the CyberKnife from Accuray Inc. (Sunnyvale, CA, USA). This stereotactic radiosurgery system integrates IGS with robotic positioning. The 6 MeV LINAC relatively light-weight photon device is mounted on a KUKA 6 DOF industrial manipulator (Fig. 6). Its primary deployment is the irradiation of brain and spine tumors. X-ray cameras are used to track the spatial displacement of the patient and compensate for motion caused by e.g. breathing. The overall accuracy of the system is 0.42 ± 0.4 mm, while patient skin motions are detected with a 0.35 mm precision [22]. The CyberKnife moves the radiation beam by physically repositioning the radiation source. It uses intra-corpuscular markers and Polaris (NDI Inc.) infrared cameras to track the patients’ moving body surface.

To improve accuracy, radioopaque fiducial markers are implanted in or near the tumor region several days before CT scanning for treatment planning. The fiducials, which are detectable in X-ray images, are used as reference markers to locate and track tumor location during patient alignment and treatment delivery. The Synchrony Respiratory Tracking System builds a correlation model between the positions of periodically detected fiducials and the real-time locations of optically tracked markers placed on the chest to track tumor location. It uses 4D-CT (imaging through time) to measure respiratory tissue motion and deformation and to account for the effect of displacement and deformation through the irradiation [23].

V. THE REAL NEED FOR ACCURACY

Surgical robots are involved in interventional robotics primarily to provide patient benefits through increased precision and minimally invasiveness. Even more, robotic devices should allow for procedures that are not possible to perform by humans. The above presented robot systems all have an application accuracy between 1–2 mm. This is less precise than robotic setups in industrial applications, as the accumulation of different errors in IGS prevents real sub-millimeter accuracy as claimed in the case of many systems. Beyond spatial accuracy, many other factors determine the success of a surgical robot once released to the market. Assuming clinical benefits and adequate safety measures, a surgical robotic system

Figure 6. CyberKnife radiosurgery system for high precision

radiation therapy. The system compensates for physiological patient

motion, and the tumors are treated from different angles to minimize tissue exposure. Credit: Accuray Inc.

should still address the question of ergonomics, setup time and complexity of operation, overall and recurrent costs of usage, supporting devices and procedures. A robot must be intuitive and require minimal maintenance and engineering skills to operate [24]. The user acceptance of a system will eventually determine the value of the device, therefore one of the mayor directives of development is to minimize the change to the present clinical workflow.

This is also represented in the medical device regulations. The procedure both in Europe and in the United States is focusing on the safety and transparency of the system [25].

In the European Union, the CE mark (Conformité Européene – European Conformity) must be obtained that means the product complies with the essential requirements of the relevant European health, safety and environmental protection legislation. The procedure is managed by independent Notified Bodies (NB). There are approximately 100 international, non-governmental NBs for medical devices. International Standards Organization’s ISO 9000 Quality Standards family is applied to verify the production management of a company. ISO 9001:2000 combines three previous standards (9001, 9002 and 9003) affecting design and development procedures under the title “Quality management systems – Requirement”. It is possible for ISO 9001 complied companies to self-certify (CE mark) their products within certain limitations, and the Notified Bodies would periodically audits their system.

In the United States, only the federal Food and Drug Administration can approve medical systems. This can be done either in the form of a Pre-Market Approval (PMA) procedure, which is for new devices, requiring extensive clinical trials, and huge amount of documentation. This needs a hospital’s Institutional Review Board (IRB) approval. On the other hand, 510(K) procedure is for devices that can prove to be “substantially equivalent” to an existing device, already approved by FDA. The validation may still include clinical trials, but less extensive than the PMA. All systems must comply with FDA Quality System Regulations (QSR).

Interestingly, all the FDA approved surgical robots (e.g. da Vinci, NeuroMate, CyberKnife, ROBODOC) went down the 510(K) procedure, proved to be substantially equivalent to existing technologies. The basic idea behind these regulations is to prevent failures and safety issues originating from bad design. The clinical use and patient outcome might not even be proved during the validation. At the most, the system should show the capability to perform a procedure with the same effectiveness as an existing (manual) technique. They rely on the selectivity of the market, which should only allow for the presence of well-sustained systems with significant added value to the surgical procedure.

VI. CONCLUSION

CIS provides many possibilities to improve the quality

of surgery and patient care. Medical imaging and image

guided surgery are definitely the flagships of modern

medicine, and recent technological development allows

for improved quality in pre- and intra-operative care.

The devices introduced here represent the first

generation of interventional robots. Further development

is still required in patient safety and precision of

treatment delivery. New systems should focus on the

improvement of application accuracy and to better

integrate the different system components. Through

advanced technological solutions, it is possible to

improve the quality of future healthcare and justify the

higher investment costs of IG interventions. Systems

currently under development will soon deliver great

clinical advantages providing advanced procedures that

benefit both the patient and the surgeon.

ACKNOWLEDGMENT

The research has been supported by Hungarian National Scientific Research Foundation – OTKA T69055 Grant.

REFERENCES

[1] R. A. Faust (editor), “Robotics in Surgery: History, Current and Future Applications”. Nova Science Publishers, New York, 2007

[2] D. M. Herron, M. Marohn, the SAGES-MIRA Robotic Surgery Consensus Group, “A consensus document on robotic surgery”. Surgical Endoscopy; Vol.22, pp.313-325; 2008

[3] T. Ortmaier, B. Dem, B. Kübler, G. Passig, D. Reintsema, U. Seibold, “Robot Assisted Force Feedback Surgery”. Chapter 21 in Advances in Telerobotics, M. Ferre et al. (editors), Springer, Berlin, pp.361–379; 2007

[4] P. Grunert, K. Darabi, J. Espinosa, R. Filippi, “Computer-aided navigation in neurosurgery”. Neurosurgery Review; Vol.26, pp.73-99; 2003

[5] G. Kronreif, “Medical Robotic—General Aspects and Applications”. 12th Summer School in Image Processing (SSIP2004); Graz, Austria; 2004

[6] T. Haidegger, L. Kovacs, G. Fordos, Z. Benyo, P. Kazanzides, “Future Trends in Robotic Neurosurgery”. Proc. of 14th Nordic-Baltic Conf. on Biomedical Engineering and Medical Physics; Riga, pp.229-233; 2008

[7] C. R. Maurer, J. M. Fitzpatrick, M. Y. Wang, R. L. Galloway, R. J. Maciunas, G. S. Allen, “Registration of head volume images using implantable fiducial markers”. IEEE Trans. Medical Imaging; Vol.16, Issue:4, pp.447-62; Aug. 1997

[8] J. V. Hajnal, D. J. Hawkes, D. L. G. Hill (editors), “Medical Image Registration”. CRC Press, USA; 2001


138

[9] D. M. Kwartowitz, · D. Herrell, R. L. Galloway, "Toward image-guided robotic surgery: determining intrinsic accuracy of the daVinci robot". Int. J. of Computer Assisted Radiology and Surgery; Vol.1. No 3. pp.157-165; Nov. 2006

[10] Y.S. Kwoh, J. Hou, E.A. Jonckheere, S. Hayati, “A Robot with Improved Absolute Positioning Accuracy for CT Guided Stereotactic Brain Surgery”; IEEE Transactions on Biomedical Engineering, Vol.35, No.2; 1988

[11] W. L. Bargar, “Robots in Orthopaedic Surgery— Past, Present, and Future”. Clinical Orthopaedics and Related Research, No. 463, pp.31-36; 2007

[12] H. A. Paul, B. Mittlestadt,W. L. Bargar, B. Musits, R. H. Taylor, P. Kazanzides , J. Zuhars, B. Williimson, W. Hanson, “Surgical Robot for Total Hip Replacement Surgery”. Proc. of the IEEE Int. Conference on Robotics and Automation; San Diego, CA; 1994

[13] R. H. Taylor. “Robotic Joint Replacement Surgery”. Course materials, Johns Hopkins University, 2001

[14] Varna: T. R. K. Varma, P. Eldridge, “Use of the NeuroMate stereotactic robot in a frameless mode for functional neurosurgery”. Int. Journal of Medical Robotics and Computer Assisted Surgery, Vol.2, Issue:2, pp.107–113; 2003

[15] Q. H. Li, L. Zamorano, A. Pandya, R. Perez, J. Gond, F. Diaz, “The Application Accuracy of the NeuroMate Robot— A Quantitative Comparison with Frameless and Frame-Based Surgical Localization Systems”. Computer Aided Surgery; Vol.7, Issue:2, pp.90–98; 2002

[16] T. Haidegger, T. Xia, P. Kazanzides, “Accuracy Improvement of a Neurosurgical Robot System”. 2nd IEEE/RAS-EMBS Int. Conf. on

Biomedical Robotics and Biomechatronics (BioRob2008); Scottsdale, AZ; 2008

[17] Renishaw plc press release, 20.11.2008. Avalable online: <http://www.renishaw.com/en/10457.aspx>

[18] G. H. Ballantyne, J. Marescaux, P. C. Giulianotti (editors): Primer of Robotic & Telerobotic Surgery; Lippincott Williams & Wilkins; 2004

[19] D. M. Kwartowitz · D. Herrell, R. L. Galloway, "Update: Determining intrinsic accuracy of the daVinci-S robot ". Int. J. of

Computer Assisted Radiology and Surgery; Vol.1. No.5. pp.301–304; Feb. 2007

[20] Intuitive Surgical Inc., Available from: <http://investor. .intuitivesurgical.com/phoenix.zhtml?c=122359&p=irol-faq>

[21] K. Cleary, A. Melzer, V. Watson, G. Kronreif, D. Stoianovici, “Interventional robotic systems: Applications and technology state-of-the-art”. Minimally Invasive Therapy; Vol.15. Issue: 2; pp.101–113; 2006

[22] S. Dieterich, J. Tang, J. Rodgers, K. Cleary, “Skin Respiratory Motion Tracking for Stereotactic Radiosurgery using the CyberKnife”. Proc. of 17th CARS, Computer Assisted Radiology and Surgery; Vol.1256, pp.130-136; 2003

[23] H. C. Urschel (ed.), “Robotic Radiosurgery. Treating Tumors that Move with Respiration”. Springer; 2007

[24] P. Kazanzides, G. Fichtinger, G. D. Hager, A. M. Okamura, L. L. Whitcomb, R. H. Taylor, “Surgical and Interventional Robotics; Core Concepts, Technology, and Design”. IEEE Robotics and Automation Magazine; Vol.15, Issue: 2, pp.122-130; 2008

[25] R. H. Taylor, P, Kazanzides, “Medical Robotics and Computer-Integrated Interventional Medicine”. Chapter 18 in Biomedical Information Technology; D. D. Feng (ed.), Academic Press; 2007

[ieee 2009 5th international symposium on applied computational intelligence and informatics (saci)...

Documents