Share Email Print

Proceedings Paper

Interactive generation of digital anthropomorphic phantoms from XCAT shape priors
Author(s): C. Lindsay; M. A. Gennert; C. M. Connolly; A. Konik; P. K. Dasari; W. P. Segars; M. A. King
Format Member Price Non-Member Price
PDF $17.00 $21.00
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

In SPECT imaging, patient respiratory and body motion can cause artifacts that degrade image quality. Developing and evaluating motion correction algorithms are facilitated by simulation studies where a numerical phantom and its motion are precisely known, from which image data can be produced. Previous techniques to test motion correction methods generated XCAT phantoms modeled from MRI studies and motion tracking but required manually segmenting the major structures within the whole upper torso, which can take 8 hours to perform. Additionally, segmentation in two dimensional MRI slices and interpolating into three dimensional shapes can lead to appreciable interpolation artifacts as well as requiring expert knowledge of human anatomy in order to identify the regions to be segmented within each slice. We propose a new method that mitigates the long manual segmentation times for segmenting the upper torso. Our interactive method requires that a user provide only an approximate alignment of the base anatomical shapes from the XCAT model with an MRI data. Organ boundaries from aligned XCAT models are warped with displacement fields generated from registering a baseline MR image to MR images acquired during pre-determined motions, which amounts to automated segmentation each organ of interest. With our method we can show the quality of segmentation is equal that of expert manual segmentation does not require a user who is an expert in anatomy, and can be completed in minutes not hours. In some instances, due to interpolation artifacts, our method can generate higher quality models than manual segmentation.

Paper Details

Date Published: 14 April 2012
PDF: 10 pages
Proc. SPIE 8317, Medical Imaging 2012: Biomedical Applications in Molecular, Structural, and Functional Imaging, 83170M (14 April 2012); doi: 10.1117/12.911275
Show Author Affiliations
C. Lindsay, Worcester Polytechnic Institute (United States)
M. A. Gennert, Worcester Polytechnic Institute (United States)
C. M. Connolly, Univ. of Massachusetts Medical School (United States)
A. Konik, Univ. of Massachusetts Medical School (United States)
P. K. Dasari, Worcester Polytechnic Institute (United States)
Univ. of Massachusetts Medical School (United States)
W. P. Segars, Duke Univ. Medical School (United States)
M. A. King, Univ. of Massachusetts Medical School (United States)

Published in SPIE Proceedings Vol. 8317:
Medical Imaging 2012: Biomedical Applications in Molecular, Structural, and Functional Imaging
Robert C. Molthen; John B. Weaver, Editor(s)

© SPIE. Terms of Use
Back to Top