Magnetic resonance and ultrasound image-guided navigation system using a needle manipulator

PURPOSE

Image guidance is crucial for percutaneous tumor ablations, enabling accurate needle-like applicator placement into target tumors while avoiding tissues that are sensitive to injury and/or correcting needle deflection. Although ultrasound (US) is widely used for image guidance, magnetic resonance (MR) is preferable due to its superior soft tissue contrast. The objective of this study was to develop and evaluate an MR and US multi-modal image-guided navigation system with a needle manipulator to enable US-guided applicator placement during MR imaging (MRI)-guided percutaneous tumor ablation.

METHODS

The MRI-compatible needle manipulator with US probe was installed adjacent to a 3 Tesla MRI scanner patient table. Coordinate systems for the MR image, patient table, manipulator, and US probe were all registered using an optical tracking sensor. The patient was initially scanned in the MRI scanner bore for planning and then moved outside the bore for treatment. Needle insertion was guided by real-time US imaging fused with the reformatted static MR image to enhance soft tissue contrast. Feasibility, targeting accuracy, and MR compatibility of the system were evaluated using a bovine liver and agar phantoms.

RESULTS

Targeting error for 50 needle insertions was 1.6 ± 0.6 mm (mean ± standard deviation). The experiment confirmed that fused MR and US images provided real-time needle localization against static MR images with soft tissue contrast.

CONCLUSIONS

The proposed MR and US multi-modal image-guided navigation system using a needle manipulator enabled accurate needle insertion by taking advantage of static MR and real-time US images simultaneously. Real-time visualization helped determine needle depth, tissue monitoring surrounding the needle path, target organ shifts, and needle deviation from the path.

CT and MRI compatibility of flexible 3D-printed materials for soft actuators and robots used in image-guided interventions

PURPOSE

Three-dimensional (3D) printing allows for the fabrication of medical devices with complex geometries, such as soft actuators and robots that can be used in image-guided interventions. This study investigates flexible and rigid 3D-printing materials in terms of their impact on multimodal medical imaging.

METHODS

The generation of artifacts in clinical computer tomography (CT) and magnetic resonance (MR) imaging was evaluated for six flexible and three rigid materials, each with a cubical and a cylindrical geometry, and for one exemplary flexible fluidic actuator. Additionally, CT Hounsfield units (HU) were quantified for various parameter sets iterating peak voltage, x-ray tube current, slice thickness, and convolution kernel.

RESULTS

We found the image artifacts caused by the materials to be negligible in both CT and MR images. The HU values mainly depended on the elemental composition of the materials and applied peak voltage was ranging between 80 and 140 kVp. Flexible, nonsilicone-based materials were ranged between 51 and 114 HU. The voltage dependency was less than 29 HU. Flexible, silicone-based materials were ranged between 60 and 365 HU. The voltage-dependent influence was as large as 172 HU. Rigid materials ranged between -69 and 132 HU. The voltage-dependent influence was <33 HU.

CONCLUSIONS

All tested materials may be employed for devices placed in the field of view during CT and MR imaging as no significant artifacts were measured. Moreover, the material selection in CT could be based on the desired visibility of the material depending on the application. Given the wide availability of the tested materials, we expect our results to have a positive impact on the development of devices and robots for image-guided interventions.

Compensating Uncertainties in Force Sensing for Robotic-Assisted Palpation

Force sensing in robotic-assisted minimally invasive surgery (RMIS) is crucial for performing dedicated surgical procedures, such as bilateral teleoperation and palpation. Due to the bio-compatibility and sterilization requirements, a specially designed surgical tool/shaft is normally attached to the sensor while contacting the organ targets. Through this design, the measured force from the sensor usually contains uncertainties, such as noise, inertial force etc., and thus cannot reflect the actual interaction force with the tissue environment. Motivated to provide the authentic contact force between a robotic tool and soft tissue, we proposed a data-driven force compensation scheme without intricate modeling to reduce the effects of force measurement uncertainties. In this paper, a neural-network-based approach is utilized to automatically model the inertial force subject to noise during the robotic palpation procedure, then the exact contact force can be obtained through the force compensation method which cancels the noise and inertial force. Following this approach, the genuine interaction force during the palpation task can be achieved furthermore to improve the appraisal of the tumor surrounded by the soft tissue. Experiments are conducted with robotic-assisted palpation tasks on a silicone-based soft tissue phantom and the results verify the effectiveness of the suggested method.

A Magnetic Resonance Compatible Soft Wearable Robotic Glove for Hand Rehabilitation and Brain Imaging

In this paper, we present the design, fabrication and evaluation of a soft wearable robotic glove, which can be used with functional Magnetic Resonance imaging (fMRI) during the hand rehabilitation and task specific training. The soft wearable robotic glove, called MR-Glove, consists of two major components: a) a set of soft pneumatic actuators and b) a glove. The soft pneumatic actuators, which are made of silicone elastomers, generate bending motion and actuate finger joints upon pressurization. The device is MR-compatible as it contains no ferromagnetic materials and operates pneumatically. Our results show that the device did not cause artifacts to fMRI images during hand rehabilitation and task-specific exercises. This study demonstrated the possibility of using fMRI and MR-compatible soft wearable robotic device to study brain activities and motor performances during hand rehabilitation, and to unravel the functional effects of rehabilitation robotics on brain stimulation.

Design, Additive Manufacture, and Control of a Pneumatic, MR-Compatible Needle Driver

This paper reports the design, modeling, and control of an MR-compatible actuation unit comprising pneumatic stepper mechanisms. One helix-shaped bellows and one toroid-shaped bellows were designed to actuate in pure rotation and pure translation, respectively. The actuation unit is a two degree- of-freedom needle driver that translates and rotates the base of one tube of a steerable needle like a concentric tube robot. For safety, mechanical stops limit needle motion to maximum unplanned step sizes of 0.5 mm and 0.5 degrees. Additively manufactured by selective laser sintering, the flexible fluidic actuating (FFA) mechanism achieves two degree-of-freedom motion as a monolithic, compact, and hermetically-sealed device. A second novel contribution is sub-step control for precise translations and rotations less than full step increments; steady- state errors of 0.013 mm and 0.018 degrees were achieved. The linear FFA produced peak forces of 33 N and -26.5 N for needle insertion and retraction, respectively. The rotary FFA produced bidirectional peak torques of 68 N-mm. With the FFA's in full motion in a 3T scanner, no loss in signal-to-noise ratio of MR images observed.