i3PosNet: Instrument Pose Estimation from X-Ray in temporal bone surgery

Purpose: Accurate estimation of the position and orientation (pose) of surgical instruments is crucial for delicate minimally invasive temporal bone surgery. Current techniques lack in accuracy and/or line-of-sight constraints (conventional tracking systems) or expose the patient to prohibitive ionizing radiation (intra-operative CT). A possible solution is to capture the instrument with a c-arm at irregular intervals and recover the pose from the image. Methods: i3PosNet infers the position and orientation of instruments from images using a pose estimation network. Said framework considers localized patches and outputs pseudo-landmarks. The pose is reconstructed from pseudo-landmarks by geometric considerations. Results: We show i3PosNet reaches errors less than 0.05mm. It outperforms conventional image registration-based approaches reducing average and maximum errors by at least two thirds. i3PosNet trained on synthetic images generalizes to real x-rays without any further adaptation. Conclusion: The translation of Deep Learning based methods to surgical applications is difficult, because large representative datasets for training and testing are not available. This work empirically shows sub-millimeter pose estimation trained solely based on synthetic training data.Comment: Accepted at International journal of computer assisted radiology and surgery pending publicatio

Motion and Metal Artifact Correction for Enhancing Plaque Visualization in Coronary Computed Tomography Angiography

Atherosclerosis detection remains challenging in coronary CT angiography due to motion and metal artifacts. Motion artifacts arising from rapid coronary artery displacement occurred over the acquisition window may lead to intensity reduction and feature doubling or distortion, severely hindering the visualization of a plaque of interest. Similarly, for patients with cardiac implants, pacing electrodes or implant lead components can create substantial blooming and streak artifacts in the heart region, obscuring the background anatomy adjacent to the component. In this work we presented an image-based compensation framework exploiting a rigid and linear motion model for correcting motion artifacts, and a novel reconstruction method incorporating a deformable model for metal leads to eliminate metal artifacts to improve plaque visualization. The feasibility of both correction methods is validated with simulation and experimental studies. We found a dramatic improvement in the ability to visualize fine details in the coronary artery plaque after the application of the proposed motion compensation method. Similarly, anatomy visualization even near the boundary of the component has greatly improved after reconstruction with the deformable known-component model. Both proposed methods have the potential to improve plaque visualization and characterization in coronary CT angiography