Abstract-This paper addresses the use of robotic tissue manipulation in medical needle insertion procedures to improve targeting accuracy and to help avoid damaging sensitive tissues. To control these multiple, potentially competing objectives, we present a phased controller that operates one manipulator at a time using closed-loop imaging feedback. We present an automated procedure planning technique that uses tissue geometry to select the needle insertion location, manipulation locations, and controller parameters. The planner uses a stochastic optimization of a cost function that includes tissue stress and robustness to disturbances. We demonstrate the system on 2D tissues simulated with a mass-spring model, including a simulation of a prostate brachytherapy procedure. It can reduce targeting errors from more than 2cm to less than 1mm, and can also shift obstacles by over 1cm to clear them away from the needle path.

Cryoablation is a percutaneous procedure for treating solid tumors using needle-like instruments. This paper presents an interventional guidance device for faster and more accurate alignment and insertion of multiple probes during cryoablation performed in closed bore magnetic resonance (MR) imaging systems. The device is compact and is intended to be mounted onto a Siemens 110 mm MR loop coil. A cable-driven two-degrees-of-freedom spherical mechanism mimics the wrist motion as it orients the intervention probes about a remote center of motion located 15 mm above the skin. A carriage interfaces with the probes via a thumbscrew-fastened latch to passively release the probes from their tracks, enabling them to be inserted sequentially and freeing them to move with respiration. Small actuator modules containing piezoelectric encoder-based motors are designed to be snap-fit into the device for ease of replacement and sterilization. The robot MRI compatibility was validated with standard cryoablation imaging sequences in 3T MR environment, yielding a maximum of 4% signal to noise ratio during actuator motion. Bench-level device characterization demonstrated a maximum error of 0.78° in the carriage movement. Needle-tip placement experiments for multiple targets in gelatin were performed using our image-guided navigation software, measuring an average targeting error of 2.0 mm.

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Rationale and Objectives Accuracy and speed are essential for the intraprocedural nonrigid MR-to-CT image registration in the assessment of tumor margins during CT-guided liver tumor ablations. While both accuracy and speed can be improved by limiting the registration to a region of interest (ROI), manual contouring of the ROI prolongs the registration process substantially. To achieve accurate and fast registration without the use of an ROI, we combined a nonrigid registration technique based on volume subdivision with hardware acceleration using a graphical processing unit (GPU). We compared the registration accuracy and processing time of GPU-accelerated volume subdivision-based nonrigid registration technique to the conventional nonrigid B-spline registration technique. Materials and Methods Fourteen image data sets of preprocedural MR and intraprocedural CT images for percutaneous CT-guided liver tumor ablations were obtained. Each set of images was registered using the GPU-accelerated volume subdivision technique and the B-spline technique. Manual contouring of ROI was used only for the B-spline technique. Registration accuracies (Dice Similarity Coefficient (DSC) and 95% Hausdorff Distance (HD)), and total processing time including contouring of ROIs and computation were compared using a paired Student’s t-test. Results Accuracy of the GPU-accelerated registrations and B-spline registrations, respectively were 88.3 ± 3.7% vs 89.3 ± 4.9% (p = 0.41) for DSC and 13.1 ± 5.2 mm vs 11.4 ± 6.3 mm (p = 0.15) for HD. Total processing time of the GPU-accelerated registration and B-spline registration techniques was 88 ± 14 s vs 557 ± 116 s (p < 0.000000002), respectively; there was no significant difference in computation time despite the difference in the complexity of the algorithms (p = 0.71). Conclusion The GPU-accelerated volume subdivision technique was as accurate as the B-spline technique and required significantly less processing time. The GPU-accelerated volume subdivision technique may enable the implementation of nonrigid registration into routine clinical practice.