Student: Ali Uneri, Johns Hopkins University – “Known-Component 3D-2D Registration for Surgical Guidance and Quality Assurance”

When:
November 9, 2016 @ 3:00 pm – 5:00 pm
2016-11-09T15:00:00+00:00
2016-11-09T17:00:00+00:00

Location

Malone 228

Abstract

Intraoperative 2D and 3D imaging using mobile C-arms combined with advanced image registration algorithms could overcome many of the limitations of conventional surgical navigation, streamline workflow, and enable novel applications in image-guided surgery.

This talk focuses on one particular premise in my PhD dissertation – demonstrating how to extend the utility of fluoroscopic intraoperative imaging systems (conventionally limited to providing visual feedback to the surgeon) to accurately guide and assess the delivery of various surgical devices. The solution involves a 3D-2D registration algorithm that leverages prior knowledge of the patient and surgical components to obtain quantitative assessment of 3D shape and pose from a small number of 2D radiographs obtained during surgery.

The presented system is evaluated in application to pedicle screw placement, where it can (1) provide guidance of surgical device analogous to an external tracking system; and (2) provide intraoperative quality assurance of the surgical product, potentially reducing postoperative morbidity and the rate of revision surgery. Key aspects that affect the performance of the proposed system will be discussed, including optimal selection of radiographic views, minimization of radiation dose, as well as parametric modeling of the surgical components to handle limited shape and composition information, and modeling of component deformation.

Bio

Ali Uneri is a Ph.D. candidate in Computer Science at Johns Hopkins University. His doctoral research was carried out at the I-STAR Lab in Biomedical Engineering under supervision of Jeffrey H. Siewerdsen and Russell H. Taylor. His Ph.D. dissertation includes work encompassing: (1) an extensible software platform for integrating navigational tools with cone-beam CT, including fast registration algorithms using parallel computation on general purpose GPU; (2) a 3D-2D registration approach that leverages knowledge of interventional devices for surgical guidance and quality assurance; and (3) a hybrid 3D deformable registration approach using image intensity and feature characteristics to resolve gross deformation in cone-beam CT guidance of thoracic surgery. Prior to joining Johns Hopkins University, he obtained an M.Sc. in Bioengineering from Imperial College London and worked at the Acrobot Company on the development of a surgical robot designed to assist hip and knee replacement procedures.

Back to top