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.

This paper presents an innovative approach for executing robotic needle insertion within deformable living organs. The objective is to maintain the insertion pivot point on the skin, which remains stationary. At the same time, the organs undergo displacement and deformation due to respiration. Therefore, real-time control and precise needle steering are crucial. The proposed method relies on isolated objective constraints to ensure the objectives while steering the needle along a predefined trajectory. The needle insertion process benefits from Finite Element (FE) models to simulate the environment and address the inverse problem to drive the robot’s end effector (EE) by re-evaluating the objective functions in the constraint space for each time step. So, the desired motion of the robot’s EE could be calculated at a small cost for non-linear functions in real-time, resulting in better precision and reducing stress caused to the organs.

Accurate scatter correction is essential for qualitative and quantitative PET imaging. Until now, scatter correction based on Monte Carlo simulation (MCS) has been recognized as the most accurate method of scatter correction for PET. However, the major disadvantage of MCS is its long computational time, which makes it unfeasible for clinical usage. Meanwhile, single scatter simulation (SSS) is the most widely used method for scatter correction. Nevertheless, SSS has the disadvantage of limited robustness for dynamic measurements and for the measurement of large objects. In this work, a newly developed implementation of MCS using graphics processing unit (GPU) acceleration is employed, allowing full MCS-based scatter correction in clinical 3D brain PET imaging. Starting from the generation of annihilation photons to their detection in the simulated PET scanner, all relevant physical interactions and transport phenomena of the photons were simulated on GPUs. This resulted in an expected distribution of scattered events, which was subsequently used to correct the measured emission data. The accuracy of the approach was validated with simulations using GATE (Geant4 Application for Tomography Emission), and its performance was compared to SSS. The comparison of the computation time between a GPU and a single-threaded CPU showed an acceleration factor of 776 for a voxelized brain phantom study. The speedup of the MCS implemented on the GPU represents a major step toward the application of the more accurate MCS-based scatter correction for PET imaging in clinical routine.

We propose a new method for the progressive enhancement of images degraded by noise and blur using shock filters. The originality of our approach lies in the iterative exploitation of nonconservative edge-based force fields normally designed for segmentation to determine both locations and amplitudes of the sharpening action. The proposed nonconservative shock filter (NCSF) produces images with strong discontinuities that can be used as an additional pre-processing step to facilitate higher level tasks such as edge detection or segmentation. NCSF is especially useful when coupled with an anisotropic diffusion equation in noisy configurations. Using the proposed method, edges that are not well defined prior to filtering can be iteratively recovered. We show processing results on various types of images, focusing on medical transrectal ultrasound and brain PET imaging.

We propose to consider linear and nonlinear image diffusion processes as the result of the transport of image values in external advection fields. Linear diffusion is seen as the continuous transport of particles in fields of random orientations, while non-linear behavior is obtained by modulating the magnitude of these fields according to local edge strength. Edge sharpening can be achieved by biasing fields towards image edges using e.g. Gradient Vector Flow. This point of view, closely related to discrete random walks and other stochastic interpretations of diffusion processes, allows for simple, stable and surprisingly efficient filters that can be implemented using a few lines of code only.

Minimally-invasive (MI) procedures are becoming more popular and frequent due to their benefits such as reduced patient trauma and hospitalization time. However, several common types of MI interventions are performed under X-ray guidance, which exposes both patients and staff to harmful ionizing radiation. Radiation exposure has therefore become a major concern for the medical community. Yet, few efforts to actively reduce it by exploiting the robotic capabilities of the devices present in the surgical suite have been performed. The propagation of radiation highly depends on the X-ray source positioning. Hence, we propose an approach to optimize the imaging device's pose in order to reduce the exposure to radiation of both patient and staff, while preserving the visibility of the targeted anatomical structure in the acquired image. Our method is based on the optimization of a cost function, which takes the current context and device parameters into account to compute the overall radiation exposure. It relies on GPU-accelerated Monte Carlo methods to simulate radiation propagation and performs the optimization in quasi real-time. When evaluated on a set of standard imaging configurations, our approach is able to recommend a device's pose in a few seconds, for which the delivered dose is reduced. Such an approach can contribute to lower the probability of appearance and severity of long-term negative effects due to radiation exposure and improve overall radiation safety.

We improve and adjust usual weak calibration techniques to the case of stereo video microscopes: Harris detector using a simplex optimization method for feature points detection, a "cha" window based ZNSSD correlation for points matching. Images of a pattern made with a water drop covered with nickel fillings are used. The result is validated by constructing 3D view of a micromanipulation work field.

The vision system for micromanipulation and microassembly usually includes at least two cameras allowing top and lateral views of the work field. The top view is used to control the xy position of the microgripper. As for the lateral view, it allows the control of the z position. The paper describes how the lateral camera can be replaced by a virtual one using the trifocal transfer. Then, the work field is set free. The novel view synthesis method used requires only a weak calibration to recover the geometry of the scene, then, it is well suited for the vision at the microscale