Albert Hsiao | Searcharxiv

arXiv2025

Dexterous Control of an 11-DOF Redundant Robot for CT-Guided Needle Insertion With Task-Oriented Weighted Policies

Computed tomography (CT)-guided needle biopsies are critical for diagnosing a range of conditions, including lung cancer, but present challenges such as limited in-bore space, prolonged procedure times, and radiation exposure. Robotic assistance offers a promising solution by improving needle trajectory accuracy, reducing radiation exposure, and enabling real-time adjustments. In our previous work, we introduced a redundant robotic platform designed for dexterous needle insertion within the confined CT bore. However, its limited base mobility restricts flexible deployment in clinical settings. In this study, we present an improved 11-degree-of-freedom (DOF) robotic system that integrates a 6-DOF robotic base with a 5-DOF cable-driven end-effector, significantly enhancing workspace flexibility and precision. With the hyper-redundant degrees of freedom, we introduce a weighted inverse kinematics controller with a two-stage priority scheme for large-scale movement and fine in-bore adjustments, along with a null-space control strategy to optimize dexterity. We validate our system through both simulation and real-world experiments, demonstrating superior tracking accuracy and enhanced manipulability in CT-guided procedures. The study provides a strong case for hyper-redundancy and null-space control formulations for robot-assisted needle biopsy scenarios.

cs.RO↗

arXiv2017

Computationally efficient cardiac views projection using 3D Convolutional Neural Networks

4D Flow is an MRI sequence which allows acquisition of 3D images of the heart. The data is typically acquired volumetrically, so it must be reformatted to generate cardiac long axis and short axis views for diagnostic interpretation. These views may be generated by placing 6 landmarks: the left and right ventricle apex, and the aortic, mitral, pulmonary, and tricuspid valves. In this paper, we propose an automatic method to localize landmarks in order to compute the cardiac views. Our approach consists of first calculating a bounding box that tightly crops the heart, followed by a landmark localization step within this bounded region. Both steps are based on a 3D extension of the recently introduced ENet. We demonstrate that the long and short axis projections computed with our automated method are of equivalent quality to projections created with landmarks placed by an experienced cardiac radiologist, based on a blinded test administered to a different cardiac radiologist.

cs.CV↗