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Proceedings Paper

Respiration induced fiducial motion tracking in ultrasound using an extended SFA approach
Author(s): Kunlin Cao; Bryan Bednarz; L. Scott Smith; Thomas K. F. Foo; Kedar A. Patwardhan
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Paper Abstract

Radiation therapy (RT) plays an essential role in the management of cancers. The precision of the treatment delivery process in chest and abdominal cancers is often impeded by respiration induced tumor positional variations, which are accounted for by using larger therapeutic margins around the tumor volume leading to sub-optimal treatment deliveries and risk to healthy tissue. Real-time tracking of tumor motion during RT will help reduce unnecessary margin area and benefit cancer patients by allowing the treatment volume to closely match the positional variation of the tumor volume over time. In this work, we propose a fast approach which enables transferring the pre-estimated target (e.g. tumor) motion extracted from ultrasound (US) image sequences in training stage (e.g. before RT) to online data in real-time (e.g. acquired during RT). The method is based on extracting feature points of the target object, exploiting low-dimensional description of the feature motion through slow feature analysis, and finding the most similar image frame from training data for estimating current/online object location. The approach is evaluated on two 2D + time and one 3D + time US acquisitions. The locations of six annotated fiducials are used for designing experiments and validating tracking accuracy. The average fiducial distance between expert's annotation and the location extracted from our indexed training frame is 1.9±0.5mm. Adding a fast template matching procedure within a small search range reduces the distance to 1.4±0.4mm. The tracking time per frame is on the order of millisecond, which is below the frame acquisition time.

Paper Details

Date Published: 17 March 2015
PDF: 7 pages
Proc. SPIE 9419, Medical Imaging 2015: Ultrasonic Imaging and Tomography, 94190S (17 March 2015); doi: 10.1117/12.2082591
Show Author Affiliations
Kunlin Cao, GE Global Research Ctr. (United States)
Bryan Bednarz, Univ. of Wisconsin (United States)
L. Scott Smith, GE Global Research Ctr. (United States)
Thomas K. F. Foo, GE Global Research Ctr. (United States)
Kedar A. Patwardhan, GE Global Research Ctr. (United States)

Published in SPIE Proceedings Vol. 9419:
Medical Imaging 2015: Ultrasonic Imaging and Tomography
Johan G. Bosch; Neb Duric, Editor(s)

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