Percutaneous Robotics in Interventional Radiology.

The accuracy of the robotic device not only relies on a reproducible needle advancement, but also on the possibility to correct target movement at chosen checkpoints and to deviate from a linear to a nonlinear trajectory. We report our experience in using the robotic device for the insertion of trocar needles in CT guided procedures. The majority of procedures were targeted organ biopsies in the chest abdomen or pelvis.

Feasibility and Accuracy of a Novel Hands-Free Robotic System for Percutaneous Needle Insertion and Steering.

Purpose: To assess the performance and accuracy of CT-guided needle insertion for clinical biopsies using a novel, hands-free robotic system that balances accuracy with the duration of the procedure and radiation dose.

Materials And Methods: A prospective, multi-center study was conducted on 60 clinically indicated biopsies of abdominal lesions at two centers (Center 1, n=26; Center 2, n=34). CT datasets were obtained for planning and controlled placement of 17g and 18g needles using a patient-mounted, CT-guided robotic system with 5 degrees of freedom.

Clinical evaluation of a robotic system for precise CT-guided percutaneous procedures.

Purpose: To assess accuracy and compare protocols for CT-guided needle insertion for clinical biopsies using a hands-free robotic system, balancing system accuracy with duration of procedure and radiation dose.

Methods: Thirty-two percutaneous abdominal and pelvic biopsies were performed and analyzed at two centers (Center 1 n = 11; Center 2 n = 21) as part of an ongoing prospective, multi-center study. CT datasets were obtained for planning and controlled placement of 17 g needles using a patient-mounted, CT-guided robotic system.

Towards radiologist-level cancer risk assessment in CT lung screening using deep learning.

Purpose: Lung cancer is the leading cause of cancer mortality in the US, responsible for more deaths than breast, prostate, colon and pancreas cancer combined and large population studies have indicated that low-dose computed tomography (CT) screening of the chest can significantly reduce this death rate. Recently, the usefulness of Deep Learning (DL) models for lung cancer risk assessment has been demonstrated. However, in many cases model performances are evaluated on small/medium size test sets, thus not providing strong model generalization and stability guarantees which are necessary for clinical adoption.

Identifying Areas for Operational Improvement and Growth in IR Workflow Using Workflow Modeling, Simulation, and Optimization Techniques.

Identifying areas for workflow improvement and growth is essential for an interventional radiology (IR) department to stay competitive. Deployment of traditional methods such as Lean and Six Sigma helped in reducing the waste in workflows at a strategic level. However, achieving efficient workflow needs both strategic and tactical approaches.