In this paper we introduce a novel and enabling MRI-compatible needle guidance toolkit intended to streamline arthrography procedures, eliminating the need for ionizing radiation exposure during this diagnostic procedure. We developed a flexible 2D grid template with unique patterns in which each point on the grid can be uniquely represented and denoted with respect to surrounding patterns. This MRI-visible non-repeating grid template sits on top of the patient’s skin in the region of interest and allows the radiologist to visualize the skin surface in the MR images and correlates each point in MR image with the corresponding point on the grid. In this manner, the radiologist can intuitively find the entry point on the skin according to their plan. An MRI-compatible handheld positioning device consisting of a needle guide, two baseline bubble inclinometers, and an MRI-compatible stabilizer arm allows the radiologist to adjust the orientation of the needle in two directions. The radiologist selects an entry point on MR images, identifies a grid location through which the needle would be projected to pass on the image, and then reproduces this needle position and angulation using the MRI-compatible handheld device and the physical grid. To evaluate the accuracy of needle targeting with the MRI-compatible needle guidance toolkit, we used the kit to target 10 locations in a phantom in a Philips Achieva 1.5T MRI. The average targeting error was 2.2±0.7 mm. Average targeting procedure time was around 20 minutes for each target.
Here we report on a phantom targeting study for accuracy evaluation of our body-mounted robot for Magnetic Resonance Imaging (MRI) guided arthrography. We use a standardized method developed in a multi-institute effort with the aim of providing an objective method for accuracy and signal-to-noise Ratio (SNR) evaluation of MRI-compatible robots. The medical definition of arthrography is the radiographic visualization of a joint (as the hip or shoulder) after the injection of a radiopaque substance. That procedure provides an evaluation of the joints using two medical imaging modalities, fluoroscopic x-ray imaging and MRI. Conventional arthrography is done in two stages: first the contrast dye injected into the joint (fluoroscopic procedure) and then an MRI to evaluate the joint space. Our MRI-guided compatible robot is intended to enable needle placement in the MRI environment, streamlining the procedure. The targeting study was conducted using the quality assessment mockup phantom and associated software called QARAI that was developed by the URobotics Laboratory at Johns Hopkins and colleagues. The mockup contains four embedded fiducials and an 8 by 8 grid which is used to automatically identify the targeting points with high accuracy. The study was conducted on a Philips Achieva 1.5T MRI system and 10 points were targeted. All targets were reached with an average error of 2.71mm. The targeting algorithm, as well as the control of the robot, were completed using robot control modules developed with the open source software 3D Slicer.
In this paper we report development of an integrated RF coil for our body-mounted arthrography robot called Arthrobot. Arthrography is the evaluation of joint conditions using imaging modalities such as computed tomography (CT) and magnetic resonance imaging (MRI). Current arthrography practice requires two separate stages; an intra-articular contrast injection guided by fluoroscopy or ultrasound followed by MR imaging. Our body-mounted robot is intended to enable needle placement in the MRI environment, streamlining the procedure. To improve imaging with our robot, a single loop coil was created and embedded into the mounting adaptor of the robot. This coil provides enough spatial coverage and sensitivity to localize anatomical points of interest and registration fiducials on the robot frame. In this paper we report the results of a SNR and heating study using our custom-made RF coil in four different scenarios using T1 and T2 weighted MR images: 1) no robot present, 2) robot off, 3) robot powered on, and 4) robot running. We also report an end-to-end robotic-assisted targeting study in a Philips MRI scanner suite using Arthrobot and our custommade RF coil for image acquisition. The SNR results and targeting results were promising. SNR dropped 32% for T1 weighted images compared to baseline (no robot present) images. For T2 weighted images the SNR drop was 42%. The average targeting error was 2.91 mm with a standard deviation (SD) of 1.82 mm. In future work we plan to replace the passive fiducials embedded in the base of Arthrobot with active fiducials that are tracked by the MRI system. These active fiducials will enable real-time tracking of the robot base and could allow breathing motion compensation during robotic procedures.