Computational and Mathematical Methods in Medicine
Volume 2013 (2013), Article ID 624683, 9 pages
Semiautomatic Segmentation of Ventilated Airspaces in Healthy and Asthmatic Subjects Using Hyperpolarized MRI
1Boston University, School of Medicine, Boston, MA 02118, USA
2Department of Biomedical Engineering, College of Engineering, Boston University, Boston, MA 02115, USA
3Department of Radiology, Brigham and Women’s Hospital, Boston, MA 02115, USA
4Department of Radiology, University of Massachusetts Medical School, Worcester, MA 01655, USA
5Dana Farber Cancer Institute, Boston, MA 02115, USA
6Department of Chemistry, Lakehead University, Thunder Bay, ON, Canada P7A 5E1
7Thunder Bay Regional Research Institute, Thunder Bay, ON, Canada P7B 6V4
Received 16 November 2012; Revised 25 January 2013; Accepted 20 February 2013
Academic Editor: Yoram Louzoun
Copyright © 2013 J. K. Lui et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
A segmentation algorithm to isolate areas of ventilation from hyperpolarized helium-3 magnetic resonance imaging (HP 3He MRI) is described. The algorithm was tested with HP 3He MRI data from four healthy and six asthmatic subjects. Ventilated lung volume (VLV) measured using our semiautomated technique was compared to that obtained from manual outlining of ventilated lung regions and to standard spirometric measurements. VLVs from both approaches were highly correlated (; ) with a mean difference of 3.8 mL and 95% agreement indices of −30.8 mL and 38.4 mL. There was no significant difference between the VLVs obtained through the semiautomatic approach and the manual approach. A Dice coefficient which quantified the intersection of the two datasets was calculated and ranged from 0.95 to 0.97 with a mean of 0.96 ± 0.01 (mean ± SD). VLVs obtained through the semiautomatic algorithm were also highly correlated with measurements of forced expiratory volume in one second (FEV1) (; ) and forced vital capacity (FVC) (; ). The technique may open new pathways toward advancing more quantitative characterization of ventilation for routine clinical assessment for asthma severity as well as a number of other respiratory diseases.