Share Email Print

Proceedings Paper

High frequency ultrasound in-plane registration of deformable finger vessels
Author(s): Chengqian Che; Jihang Wang; Vijay S. Gorantla; John Galeotti
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Ultrasound imaging is widely used in clinical imaging because it is non-invasive, real-time, and inexpensive. Due to the freehand nature of clinical ultrasound, analysis of an image sequence often requires registration between the images. Of the previously developed mono-modality ultrasound registration frameworks, only few were designed to register small anatomical structures. Monitoring of small finger vessels, in particular, is essential for the treatment of vascular diseases such as Raynaud’s Disease. High frequency ultrasound (HFUS) can now image smaller anatomic details down to 30 microns within the vessels, but no work has been done to date on such small-scale ultrasound registration. Due to the complex internal finger structure and increased noise of HFUS, it is difficult to register 2D images of finger vascular tissue, especially under deformation. We studied a variety of similarity measurements with different pre-processing techniques to find which registration similarity metrics were best suited for HFUS vessel tracking. The overall best performance was obtained with a normalized correlation metric coupled with HFUS downsampling and a one-plus-one evolutionary optimizer, yielding a mean registration error of 0.05 mm. We also used HFUS to study how finger tissue deforms under an ultrasound transducer, comparing internal motion vs. transducer motion. Improving HFUS registration and tissue modeling may lead to new research and improved treatments for peripheral vascular disorders.

Paper Details

Date Published: 24 February 2017
PDF: 7 pages
Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101332L (24 February 2017); doi: 10.1117/12.2254708
Show Author Affiliations
Chengqian Che, Carnegie Mellon Univ. (United States)
Jihang Wang, Univ. of Pittsburgh (United States)
Vijay S. Gorantla, Univ. of Pittsburgh Medical Ctr. (United States)
John Galeotti, Robotics Institute, Carnegie Mellon Univ. (United States)

Published in SPIE Proceedings Vol. 10133:
Medical Imaging 2017: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

© SPIE. Terms of Use
Back to Top