Modeling respiratory motion variations in the 4D NCAT phantom

Published

Journal Article

The current 4D NCAT phantom includes a flexible, parameterized respiratory model based on respiratory-gated CT data of a normal subject. A limitation of this model is that it is based on only one realization of the normal respiratory motion. The data upon which it was based also had a resolution lower than that offered by more advanced CT scanners and consisted of only four time frames that did not adequately cover normal tidal breathing. We further develop the 4D NCAT to more accurately model normal and abnormal states of respiration. Over two-hundred sets of 4D respiratory gated CT image data from normal and abnormal patients obtained from the Massachusetts General Hospital were used to characterize variations in the respiratory motion. Each dataset contains twenty time frames over the respiratory cycle with the patient breathing normally. With the improved resolution and better coverage of tidal breathing, this data was used to improve the respiratory model of the 4D NCAT phantom. Using automatic and semiautomatic techniques, the different respiratory structures were segmented from each time frame of each CT dataset. The time series of segmented structures were used to characterize the respiratory motion in each case. From an analysis of all normal and abnormal patient datasets, we determined the range of sizes and shapes of the right and left lungs and the range in motion (expansion in the anterior and inferior (diaphragm) directions) in the different lung regions. This analysis was used to further parameterize the general respiratory model of the 4D NCAT to more realistically model normal and abnormal variations in anatomy and in the respiratory motion. With the ability to model variations in the respiratory motion indicative of a patient population, the phantom will be a great resource to investigate the effects of respiratory motion on medical imaging and to develop compensation methods for these effects. © 2007 IEEE.

Full Text

Duke Authors

Cited Authors

  • Segars, WP; Mori, S; Chen, GTY; Tsui, BMW

Published Date

  • December 1, 2007

Published In

Volume / Issue

  • 4 /

Start / End Page

  • 2677 - 2679

International Standard Serial Number (ISSN)

  • 1095-7863

Digital Object Identifier (DOI)

  • 10.1109/NSSMIC.2007.4436697

Citation Source

  • Scopus