Inverse C-arm positioning for interventional procedures using real-time body part detection

14Citations
Citations of this article
18Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

The automation and speedup of interventional therapy and diagnostic workflows is a crucial issue. One way to improve these workflows is to accelerate the image acquisition procedures by fully automating the patient setup. This paper describes a system that performs this task without the use of markers or other prior assumptions. It returns metric coordinates of the 3-D body shape in real-time for inverse positioning. This is achieved by the application of an emerging technology, called Time-of-Flight (ToF) sensor. A ToF sensor is a cost-efficient, off-the-shelf camera which provides more than 40,000 3-D points in real-time. The first contribution of this paper is the incorporation of this novel imaging technology (ToF) in interventional imaging. The second contribution is the ability of a C-arm system to position itself with respect to the patient prior to the acquisition. We are using the 3-D surface information of the patient to partition the body into anatomical sections. This is achieved by a fast two-stage classification process. The system computes the ISO-center for each detected region. To verify our system we performed several tests on the ISO-center of the head. Firstly, the reproducibility of the head ISO-center computation was evaluated. We achieved an accuracy of (x: 1.73±1.11 mm/y: 1.87±1.31 mm/z: 2.91±2.62 mm). Secondly, a C-arm head scan of a body phantom was setup. Our system automatically aligned the ISO-center of the head with the C-arm ISO-center. Here we achieved an accuracy of ± 1 cm, which is within the accuracy of the patient table control. © 2009 Springer-Verlag.

Cite

CITATION STYLE

APA

Schaller, C., Rohkohl, C., Penne, J., Stürmer, M., & Hornegger, J. (2009). Inverse C-arm positioning for interventional procedures using real-time body part detection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5761 LNCS, pp. 549–556). https://doi.org/10.1007/978-3-642-04268-3_68

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free