ABSTRACT This article presents a fluoroscopy image‐based registration method along with a comprehensive protocol for robotic needle insertion in radiofrequency ablation (RFA) to treat liver cancer. The proposed method uses real‐time fluoroscopic images acquired from a C‐ARM system and integrates an inverse finite element (FE) simulation to compute robotic commands for accurate and adaptive needle steering. The registration procedure is fully automated and involves the injection of multiple radiopaque markers into the liver, enabling precise anatomical registration and targeted tumor localization. A key challenge addressed in this work is the integration of this image‐based registration with the inverse biomechanical simulation used to guide the robot during insertion. We describe how registration constraints can be mapped onto the surface of the biomechanical model to ensure consistent alignment between image data and robotic actuation. Designed to be adaptable to varying levels of radiologist expertise and applicable across a wide range of tumor locations, this method provides a robust and versatile solution for improving the accuracy and safety of minimally invasive liver cancer treatments.

We present a method for estimating, in real time, a 3D displacement field from a single fluoroscopic image. Our approach uses a fully convolutional network architecture to solve the associated inverse problem. Supervised learning is performed on synthetic data, using Digitally Reconstructed Radiographs as input and displacement fields as output. We use randomized Gaussian kernels to produce a synthetic training dataset with displacement fields that are smooth and diffeomorphic. In contrast to other 2D-3D registration methods, our novel data generation approach does not rely on a statistical motion model. This enables our model to accurately predict deformations unrelated to breathing or other predetermined motion patterns. As an example of clinical application, we show that our model is able to predict deformations related to percutaneous needle insertions accurately, potentially removing the need for contrast agent injection.

In breast cancer patients, some nodules are only visible on MRI, thus, requiring MRI-guidance to perform the biopsy. MRI interventions are cumbersome due to the magnetic field and the constrained working space. An MR safe robotic system actuated by pneumatic stepper motors may enable these procedures, improving both accuracy and image-guided navigation. A compact multipurpose pneumatic stepper motor has been designed with outer dimensions$(45 \times 40\times 15)\mathbf{mm}^{\mathbf{3}}$. This is configurable as a linear, rotational or curved stepper motor with a customizable step size and radius of curvature. Five copies of these motors actuate the Sunram 7 biopsy robot, of which the moving part (without protruding racks and tubes) measures$(130 \times 65\times 55)\mathbf{mm}^{\mathbf{3}}$. After manually choosing the target location and angle of approach, the needle is robotically inserted into the breast and the integrated pneumatic biopsy gun is fired to sample tissue from the lesion. The maximum torque of the presented motor is 0.61 N m at 6 bar which can be achieved using 13-teeth polycarbonate gears. Using 17-teeth gears for higher accuracy and a more convenient working pressure of 2 bar the maximum torque is 0.28 N m. The accuracy in free air of the Sunram 7 robot is 1.69mm and 1.72mm in X and Z-direction respectively, with a resulting 2-D error of 2.54 mm. The workspace volume is 4.1 L. When targeting 10 mm-sized lesions in phantoms under MRI guidance, Sunram 7 achieved a success rate of 68%. The minimum interval between two successive biopsies was 5:47 minutes. The presented multipurpose stepper motor has distinct advantages over previous designs in terms of robustness, customizability, printability and ease of integration in MR safe robotics. The Sunram 7 is able to perform accurate MRI-guided biopsies in a large workspace volume while reducing the intervention time when compared to the gold standard (i.e., MRI-guided free-hand biopsy).