The combination of a dexterous continuum robot and magnetic resonance imaging can potentially improve surgical precision and minimize brain manipulation in a minimally invasive neurosurgical procedure. In this work, a seven degree-of-freedom (DoF) continuum neurosurgical robot was developed. The main innovation lies in the design of a safe and robust switching mechanism and gear-based quick-connect mechanism that, respectively, allow selective actuation of the 6-DoF end effector using only three motors and highly efficient end effector exchange. Its performance has been validated in experiments involving multi-segment dexterous motion. We also evaluated the robotic system on a human cadaver head in a clinical 3-Tesla MRI. The entire workflow of robotic system set-up was implemented, confirming its clinical feasibility. The signal-to-noise ratio (SNR) drop was consistently less than 6% throughout various stages of end effector motion.

Brain tumor, be it primary or metastatic, is usually life threatening for a person of any age. Primary surgical resection which is one of the most effective ways of treating brain tumors can have tremendously increased success rate if the appropriate imaging modality is used for complete tumor resection. Magnetic resonance imaging (MRI) is the imaging modality of choice for brain tumor imaging because of its excellent soft-tissue contrast. MRI combined with continuum soft robotics has immense potential to be the next major technological breakthrough in the field of brain cancer diagnosis and therapy. In this work, we present the design, kinematic, and force analysis of a flexible spring-based minimally invasive neurosurgical intracranial robot (MINIR-II). It is comprised of an inter-connected inner spring and an outer spring and is connected to actively cooled shape memory alloy spring actuators via tendon driven mechanism. Our robot has three serially connected 2-DoF segments which can be independently controlled due to the central tendon routing configuration. The kinematic and force analysis of the robot and the independent segment control were verified by experiments. Robot motion under forced cooling of SMA springs was evaluated as well as the MRI compatibility of the robot and its motion capability in brainlike gelatin environment.

This paper presents the design, modeling, and control of a meso-scale torsion actuator based on shape memory alloy (SMA) for image-guided surgical procedures. Developing a miniature torsion actuator is challenging, but it opens the possibility of significantly enhancing the robot agility and maneuverability. The proposed torsion actuator is bi-directionally actuated by a pair of antagonistic SMA torsion springs through alternate Joule heating and natural cooling. The torsion actuator is integrated into a surgical robot prototype to demonstrate its working performance in the humid environment under C-Arm CT image guidance.

Diffusion kurtosis imaging (DKI) is gaining rapid adoption in the medical imaging community due to its ability to measure the non-Gaussian property of water diffusion in biological tissues. Compared to traditional diffusion tensor imaging (DTI), DKI can provide additional details about the underlying microstructural characteristics of the neural tissues. It has shown promising results in studies on changes in gray matter and mild traumatic brain injury where DTI is often found to be inadequate. The DKI dataset, which has high-fidelity spatio-angular fields, is difficult to visualize. Glyph-based visualization techniques are commonly used for visualization of DTI datasets; however, due to the rapid changes in orientation, lighting, and occlusion, visually analyzing the much more higher fidelity DKI data is a challenge. In this paper, we provide a systematic way to manage, analyze, and visualize high-fidelity spatio-angular fields from DKI datasets, by using spherical harmonics lighting functions to facilitate insights into the brain microstructure.

Measuring the 3D motion of muscular tissues, e.g., the heart or the tongue, using magnetic resonance (MR) tagging is typically carried out by interpolating the 2D motion information measured on orthogonal stacks of images. The incompressibility of muscle tissue is an important constraint on the reconstructed motion field and can significantly help to counter the sparsity and incompleteness of the available motion information. Previous methods utilizing this fact produced incompressible motions with limited accuracy. In this paper, we present an incompressible deformation estimation algorithm (IDEA) that reconstructs a dense representation of the 3D displacement field from tagged MR images and the estimated motion field is incompressible to high precision. At each imaged time frame, the tagged images are first processed to determine components of the displacement vector at each pixel relative to the reference time. IDEA then applies a smoothing, divergence-free, vector spline to interpolate velocity fields at intermediate discrete times such that the collection of velocity fields integrate over time to match the observed displacement components. Through this process, IDEA yields a dense estimate of a 3D displacement field that matches our observations and also corresponds to an incompressible motion. The method was validated with both numerical simulation and in vivo human experiments on the heart and the tongue.

Brain tumors are the most feared complications of cancer. Their treatment is challenging due to the lack of good imaging modality and the inability to remove the complete tumor. Facilitating tumor removal by accessing regions outside the "line-of-sight" will require a highly dexterous and MRI compatible robot. We present our work towards the development of a MRI-compatible neurosurgical robot. We used two antagonistic shape memory alloy (SMA) wires as actuators for each joint. Due to the size limitation of the device, we rely on temperature feedback to control the joint motion of the robot. We have developed a theoretical model based on Tanaka's model to characterize the joint motion with the change in SMA wire temperature. The results demonstrated that the SMA wire temperature can be used reliably to predict the motion of the robot. We then used a PWM scheme and switching circuit to control the temperature of multiple SMA wires. Experimental results showed that we can actuate the robot reliably and observe joint motion in a gelatin medium. MR images also showed that the robot is fully MRI-compatible and creates no significant image distortion.

We present our work towards the development of a MR image-guided SMA-actuated neurosurgical robot. We used two antagonistic SMA wires as actuators for each joint in the robot, so that each joint can be actuated independently. We also modeled and tested the force behaviour of SMA wires in the bent configuration, which can be used as a guideline for SMA actuator selection. Due to the size scale of the robot, it is impossible to have individual position sensors at each joint and hence we rely primarily on vision feedback to control the joint motion of the robot. The images used to control the robot in this paper were obtained from a camera with the goal of eventually using MR images to control the end-effector motion. We then developed a control strategy and a switching circuit to control multiple links simultaneously and independently using only one power supply. Experimental results from our current prototype of a 3-DOF robot showed that we can actuate the robot and hence observe joint motion in a gelatin slab. We also did a series of experiments inside the MRI to show that the robot is fully MRI-compatible and creates no significant image distortion in the MR images.

Magnetic Resonance Imaging (MRI) is superior to other imaging modalities such as Ultrasound and Computed Tomography and is used for both diagnostic and therapeutic procedures. However, current breast biopsy procedures based on MR images obtained apriori, use a blind targeting approach, which can be long and painful. Current approaches, due to possible patient motion, can lead to tool tip positioning errors thereby affecting diagnostic accuracy and causing significant patient discomfort, if repeated procedures are required. Hence, it is desired to develop a MRI-compatible robot for breast biopsy procedures without removing the patient from the MRI bore. This approach could potentially avoid multiple biopsy needle insertions and minimize sampling errors. Due to the working principle of MRI, material, actuation, and sensing techniques are limited as the MR images must not be affected significantly during the procedure. In addition, the limited space of the MRI bore requires the robot to be compact. This paper presents a four degrees of freedom robot with a compact parallel mechanism of which three degrees of freedom are pneumatically actuated while the needle driver mechanism is actuated by a piezo motor. Fiber-optic force sensor is also designed, developed, and mounted on the top mobile platform of the parallel mechanism to sense the needle and tissue interaction forces. Position control of the pneumatic cylinders is implemented using PI control with a modified integration term to achieve a slow and smooth motion.

Magnetic resonance imaging (MRI) has been gaining popularity over standard imaging modalities like ultrasound and CT because of its ability to provide excellent soft-tissue contrast. However, due to the working principle of MRI, a number of conventional force sensors are not compatible. One popular solution is to develop a fiber-optic force sensor. However, the measurements along the principal axes of a number of these force sensors are highly cross-coupled. One of the objectives of this paper is to minimize this coupling effect. In addition, this paper describes the design of elastic frame structures that are obtained systematically using topology optimization techniques for maximizing sensor resolution and sensor bandwidth. Through the topology optimization approach, we ensure that the frames are linked from the input to output. The elastic frame structures are then fabricated using polymers materials, such as ABS and Delrin(®), as they are ideal materials for use in MRI environment. However, the hysteresis effect seen in the displacement-load graph of plastic materials is known to affect the accuracy. Hence, this paper also proposes modeling and addressing this hysteretic effect using Prandtl-Ishlinskii play operators. Finally, experiments are conducted to evaluate the sensor's performance, as well as its compatibility in MRI under continuous imaging.

Magnetic resonance imaging (MRI) has been gaining popularity over standard imaging modalities like ultrasound and CT because of its ability to provide excellent soft-tissue contrast. However, due to the working principle of MRI, a number of conventional force sensors are not compatible. One popular solution is to develop a fiber-optic force sensor. However, the measurements along the principal axes of a number of these force sensors are highly cross-coupled. One of the objectives of this paper is to minimize this coupling effect. In addition, this paper describes the design of an elastic frame structure that is obtained systematically by an algorithm and not purely based on design intuition. We used a topology optimization technique, which has two major advantages: 1) aids engineers in design when given a constrained boundary, and 2) optimize the displacement amplification, which will in turn increase stiffness, bandwidth, and improve sensing resolution. To ensure that the frames are linked from the input to output, a solution for topology optimization is proposed. The sensor is then fabricated using plastic material (ABS) as it is one of the ideal material for MRI environment. However, the hysteresis effect seen in the displacement-load graph of plastic materials is known to affect the accuracy. Hence, this paper also proposes modeling and addressing this hysteretic effect using Prandtl-Ishlinskii play operators. Finally, experiments are conducted to evaluate the sensor's performance, as well as its compatibility in MRI under continuous imaging.

This paper presents an initial design and feasibility study for a 1-DOF Magnetic Resonance Imaging (MRI) compatible needle driver robot for radiofrequency ablation (RFA). This initial design and study is necessary to further understand how to improve on many of the shortcomings in the standard RFA procedure. Combining needle driving with advanced image tracking techniques could provide improved solutions to these clinical limitations. In this paper, we present a hydraulically-actuated 1-DOF needle driver robot that is capable of advancing a radiofrequency (RF) probe into tissue at controllable velocities and positions within an MRI scanner, while collecting force feedback data and maintaining all standards of MRI-compatible design. We also present a method of interfacing the robot with a PHANToM haptic feedback device controlled from outside the MRI scanning room. Experiments demonstrating the PHANToM’s ability to receive force feedback and guide the RFA tool to a tumor nodule within a phantom breast model while continuously imaging within MRI have been presented. Our haptic feedback system enabled us to detect normal vs. tumor phantom tissue in the preliminary experiments. Our experimental results demonstrate the compatibility of the entire system for operation during continuous MRI imaging.

This paper will present three-dimensional tongue “volumes,” reconstructed from three sagittal slices (left, mid, right) made using tagged cine MRI. The volumes will be animated to show CV movement from the consonants /k/ and /s/ to the vowels /i/, /a/, and /u/.