Needle insertion into soft tissue: A survey

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Abstract

Needle insertion in soft tissue has attracted considerable attention in recent years due to its application in minimally invasive percutaneous procedures such as biopsies and brachytherapy. This paper presents a survey of the current state of research on needle insertion in soft tissue. It examines the topic from several aspects, e.g. modeling needle insertion forces, modeling tissue deformation and needle deflection during insertion, robot-assisted needle insertion, and the effect of different trajectories on tissue deformation. All studies show that the axial force of a needle during insertion in soft tissue is the summation of different forces distributed along the needle shaft such as stiffness force, frictional force and cutting force. Some studies have modeled these forces. The force data in some procedures is used for identifying tissue layers as the needle is inserted or for path planning. Needle deflection and tissue deformation are major problems for accurate needle insertion and attempts have been made to model them. Using current models several insertion techniques have been developed which are briefly reviewed in this paper.

Introduction

Many modern clinical practices involve percutaneous (“through the skin”) diagnosis and local therapies. In these procedures, thin tubular devices (needles, catheters, tissue ablation probes, etc.) have to be inserted deep into soft, inhomogeneous tissue to reach a target. There are several applications for percutaneous needle insertion such as biopsies [1], regional anesthesia, blood sampling [2], neurosurgery [3] and brachytherapy [4]. The effectiveness of a treatment and the success or precision of a diagnosis is highly dependent on the accuracy of percutaneous insertion [5]. There is not a defined tolerance for the accuracy of needle insertion in clinical practice and in general, insertions with less needle misplacement result in more effective treatment [6] or increase the precision of diagnosis. The desired performance depends on the application. In procedures such as biopsy (for prostate, kidney, breast and liver), brachytherapy and anesthetic, placement accuracy of millimeters is required while in brain, fetus, eye and ear procedures placement accuracy of micro-millimeter is desirable. Clinical studies have revealed that targeting error (needle misplacement) may be due to imaging limitations, image misalignments, target uncertainty, human errors, target movement due to tissue deformation and needle deflection [7], [8], [9], [10], [11], [12]. In Ref. [8], for imaging misalignment of ±5 mm in only one view, a targeting error of about 10 mm was calculated. In Ref. [11], post-implantation measurements showed that the prostate gland volume which was receiving the least prescribed dose was 20–30% due to tissue deformation and gland motion, i.e., an average misplacement of 6.5 mm where the prostate volume was in the range of 25–55 cm3.

Furthermore, in a percutaneous procedure, the target might be in the millimeter neighborhood of another organ, vessel or nerve. Therefore, extra caution is required to avoid any damage or spread of a disease which in turn may lead to subsequent complications (e.g., seed migration) that may even be fatal [13]. Some of the desired accuracies may be achieved using current tools and models but accurate needle placement for many other applications requires much more research and development. Real-time visualization and high precision imaging techniques can increase the performance of the surgeon in navigating the tool and tracking the target [14]. Advanced mechanical tools that consider the constraints imposed by anatomy using haptic feedback and those that reduce or remove human errors due to fatigue, hand tremor and problems in hand/eye coordination can contribute to reduction in targeting error [7]. Medical simulators that can accurately model a clinical procedure are of great advantage for training medical residents, predicting the outcome of complex procedures and practicing new procedures. These simulators would reduce the need for animals, cadavers and anatomical phantoms as training objects and medical residents would have unlimited opportunity to practice before performing an operation on a patient.

Percutaneous therapies are constrained procedures where target visibility, target access and tool maneuverability in addition to physiological changes to the target are key issues. During some conventional needle insertion procedures, the surgeon relies on kinesthetic feedback from the tool (needle or catheter) and his or her mental 3D visualization of the anatomical structure [15]. Real-time imaging techniques that are used in some procedures can improve target visibility. However, human errors [13], imaging limitations, target uncertainty [8], tissue deformation and needle deflection [9] are a few known problems that contribute to needle misplacement in percutaneous procedures. Human errors may be related to poor techniques and insufficient skills of a physician. Target uncertainty may be caused by patient motion, physiological or geometry related problems [8]. Despite the availability of different imaging modalities to improve visualization; there are several factors such as high cost, poor resolution, probe availability, X-ray exposure, material compatibility, reliable real-time image processing techniques, etc. that may limit application of imaging in some clinical and research studies. A few examples of these limitations are: working with robots where MRI is the imaging modality (magnetic interference), using artificial phantoms when the imaging modality is ultrasound (acoustic properties), and using camera when performing ex vivo experiments (nonvisibility of target). Needle deflection is generally due to the bevel tip and diameter of the needle [16], [17]. The tissue, into which the needle is inserted, may also contribute to needle deflection. The factors that affect tissue deformation include mechanical properties of soft tissue, needle tip contact force, and frictional forces between the tissue and the needle shaft [11].

Other causes of inaccuracy in percutaneous therapies are physiological changes in the organ between the planning and treatment phases, glandular swelling during the operation, difference in tissue types involved in each procedure, differences in mechanical properties of healthy and diseased tissue, changes of mechanical properties when tissue is damaged and variability of soft tissue properties for the same organ in different patients.

To date, a number of researchers have explored ways to improve the process of needle insertion in soft tissue using robotics and medical imaging [18], [19], [20], [21], [22] in order to improve the accuracy of the procedure. Fig. 1 shows a schematic diagram of an image-guided robot-assisted percutaneous procedure.

Medical imaging (CT-scan, ultrasound, fluoroscopy, MRI) plays an important role in image-guided procedures and these procedures rely on powerful computer systems for navigating, planning, tracking and modeling. From several studies and clinical practices, it is known that off-line medical imaging does not provide enough accuracy for precise procedures. Therefore, real-time imaging techniques have been developed [23], [24], [25]. Image registration, image segmentation and augmented reality are the major topics in image-guided surgery. In Ref. [26], Peters reviewed different methods of imaging used in image-guided surgery including virtual reality. It should be mentioned that in the absence of real-time imaging, force feedback [27] and an accurate mechanical model which identifies tissue deformation and target movement would be valuable. Such models could be updated with real-time force feedback during needle insertion.

Simulation and modeling of needle insertion have been studied in 2D and 3D environments for general [15], [28] or particular [24], [29] applications. Boundary conditions, tissue geometry and biomechanical tissue properties are of major importance in simulation and modeling applications because these factors affect the amount of tissue deformation, needle deflection and interaction forces. Needle insertion has also been studied in the context of haptic rendering for simulation [30] and virtual reality systems for medical procedures [31], [32]. Needle insertion with sensorless planning, respiratory motion simulation [33] and automatic targeting [34] are also topics that have attracted considerable research.

Robotic devices for percutaneous procedures have been discussed in reviews of medical robotics such as [35], [36]. While a review of robotic devices or image-guided interventions for percutaneous procedures is not the aim of this paper, it is worth mentioning the work of a few groups in this area to show that this is also a fertile area of research into needle insertion. Krieger et al. [37] developed MR image-guided prostate interventions using an MR compatible manipulator with 3 degrees of freedom (DOF) and Susil et al. [38] demonstrated the feasibility and accuracy of needle placement, intra-prostatic injections and fiducial marker placements using the manipulator for operations on anesthetized canines. Fichtinger et al. [39] developed a robotically assisted needle insertion system for prostate biopsy and therapy with intraoperative CT guidance. Schneider et al. [40] presented a robotic device for transrectal needle insertion into the prostate with integrated ultrasound. Ebrahimi et al. [41] introduced a hand-held steerable device which incorporates a pre-bent stylet inside a straight cannula, and Maurin et al. [42] presented a parallel robotic system for percutaneous procedures under CT guidance. Hong et al. [43] built an ultrasound-guided needle insertion robot. Their robot has a 5-DOF passive arm for positioning the needle at the skin entry point and 2-DOF for insertion. They developed a real-time image servo system to compensate for tissue deformation and organ movement.

Fig. 2 shows different stages during the needle insertion procedure as reviewed in this paper. Studies that are reviewed have in general been undertaken to improve both manual and robotic percutaneous procedures. When a needle is inserted percutaneously, visual and haptic feedback are required to enhance the clinical operation. Visual and haptic data provide knowledge about tissue deformation and needle deflection during the needle insertion procedure and are useful in modeling. Visual data can also be processed and used in image-guided procedures. The models obtained from visual and haptic data can be used for intraoperative path planning and trajectory generation and they can also be used for better off-line planning and simulation for medical training. For deep needle insertion, knowledge of anatomical structures is also a requirement. In manual procedures, the knowledge and expertise can be integrated with visual feedback from an imaging modality to guide the needle with a limited accuracy. In robotic procedures, imaging data is incorporated with precise robotic motion and accurate prediction of needle deflection and tissue deformation to increase the accuracy of the overall procedure.

This paper is intended to give an overview of recent nonclinical work in the field of needle insertion in soft tissue with a focus on the effect of force measurements for modeling the needle–tissue interaction and on guiding robots to improve the precision of needle insertion (shaded area in Fig. 2). Knowing interaction forces and developing appropriate needle deflection and tissue deformation models during needle insertion are the key issues for accurate insertion. This paper reviews those studies that implicitly improve the needle insertion procedure for both manual and robot-assisted, image-guided procedures as well as simulation systems for medical training. However, a review of robotic systems, imaging techniques and simulation systems developed for percutaneous procedures is not included in this paper. The performance of each study can be evaluated based on the verification of the results with an imaging system or supporting the experimental results with an analytical model. It is clear that those results and models that are based on experiments with an artificial phantom should be validated with ex vivo and in vivo experiments.

This paper is organized as follows. First an overview of interaction forces during needle insertion is presented. Second, modeling and simulation of tissue deformation are discussed. It should be noted that the requirements for tissue modeling vary slightly depending on applications. Third, the effect of needle deflection on accurate insertion is reviewed. Fourth, some publications on trajectory planning and insertion techniques for needle insertion in manual and robot-assisted insertion are discussed. In this section, we also look into force measurement for precise control of needle insertion. Finally, challenges in this field of research as well as concluding remarks are presented. Fig. 3, Fig. 4 illustrate some of the terms used in this paper.

Section snippets

Modeling needle insertion forces

Knowledge of interactive forces during needle insertion plays an important role in precise needle insertion. This knowledge can help to identify and model different tissue types and it can also provide feedback for precise control of robot-assisted insertion while reducing tissue deformation and needle deflection. In general, an accurate model for insertion forces should be able to identify features such as the force peak, latency in the force changes, and separation of different forces such as

Modeling tissue deformation during needle insertion

Tissue deformation and tissue modeling are complex because of the inhomogeneous, nonlinear, anisotropic, elastic and viscous behavior of soft tissue. Real-time and accurate calculation of such behavior could significantly improve robotic percutaneous therapy, surgical planning and simulation systems for medical training. Hence, tissue modeling is the subject of much research.

Physical and mathematical tools are being developed to accurately model soft tissue. For modeling soft tissue, it is

Modeling needle deflection

The problem of needle deflection is one of the reasons for inaccuracy in the needle insertion procedure. During insertion of the needle, the tissue around the needle tip is compressed and the unbalanced resistance force against the compression due to the needle geometry can deflect the needle [16]. Moreover, the thin needle has to pass through layers of tissues with different properties, which would affect the net amount of needle deflection.

Okamura et al. [49] studied the effect of needle

Force sensory data

One of the requirements for precise control of robotic needle insertion is the capability to identify tissue types and their deformation in real time. Brett et al. studied the use of force sensory data for stapedotomy in Ref. [83] and epidural puncture in Ref. [84]. In stapedotomy (a part of the stapedectomy procedure to the middle ear), force sensory data was useful to identify the state of the procedure [83]. In epidural puncture, laser-based spectroscopy and force sensory data were used to

Challenges

The aim of most of the studies related to needle insertion into soft tissue is to increase the accuracy of insertion which may help to improve the effectiveness of the corresponding therapy or the precision of the diagnosis. While these studies can be carried out independently of the applications, the results obtained have a broader impact in the general area of percutaneous intervention or therapy. For example, while modeling soft tissue using accurate mechanical properties is required for

Concluding remarks

In this paper, we have conducted a detailed survey of the research to date concerning needle insertion in soft tissue. In particular, we have examined the role played by tissue deformation, needle deflection and tool–tissue interaction forces. Several techniques for needle guidance and steering were described, and challenges and areas for future research were discussed.

All recent studies show that the axial force of a needle during insertion in soft tissue is the summation of different forces

Acknowledgements

This work was supported by funds from the Natural Sciences and Engineering Research Council (NSERC) of Canada under a Collaborative Health Research Projects Grant #262583-2003, the Ontario Research and Development Challenge fund under Grant 00-May-0709, and by infrastructure grants from the Canada Foundation for Innovation awarded to the University of Western Ontario and Canadian Surgical Technologies & Advanced Robotics (CSTAR). Financial support for Ms. Abolhassani was provided by an NSERC

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