Processing of CT images for analysis of diffuse lung disease in the lung tissue research consortium

Ronald A. Karwoski, Brian Bartholmai, Vanessa A. Zavaletta, David Holmes, Richard A. Robb

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

The goal of Lung Tissue Resource Consortium (LTRC) is to improve the management of diffuse lung diseases through a better understanding of the biology of Chronic Obstructive Pulmonary Disease (COPD) and fibrotic interstitial lung disease (ILD) including Idiopathic Pulmonary Fibrosis (IPF). Participants are subjected to a battery of tests including tissue biopsies, physiologic testing, clinical history reporting, and CT scanning of the chest. The LTRC is a repository from which investigators can request tissue specimens and test results as well as semi-quantitative radiology reports, pathology reports, and automated quantitative image analysis results from the CT scan data performed by the LTRC core laboratories. The LTRC Radiology Core Laboratory (RCL), in conjunction with the Biomedical Imaging Resource (BIR), has developed novel processing methods for comprehensive characterization of pulmonary processes on volumetric high-resolution CT scans to quantify how these diseases manifest in radiographic images. Specifically, the RCL has implemented a semi-automated method for segmenting the anatomical regions of the lungs and airways. In these anatomic regions, automated quantification of pathologic features of disease including emphysema volumes and tissue classification are performed using both threshold techniques and advanced texture measures to determine the extent and location of emphysema, ground glass opacities, "honeycombing" (HC) and "irregular linear" or "reticular" pulmonary infiltrates and normal lung. Wall thickness measurements of the trachea, and its branches to the 3rd and limited 4th order are also computed. The methods for processing, segmentation and quantification are described. The results are reviewed and verified by an expert radiologist following processing and stored in the public LTRC database for use by pulmonary researchers. To date, over 1200 CT scans have been processed by the RCL and the LTRC project is on target for recruitment of the 2200 patients with 1800 CT scans in the repository for the 5-year effort. Ongoing analysis of the results in the LTRC database by the LTRC participating institutions and outside investigators are underway to look at the clinical and physiological significance of the imaging features of these diseases and correlate these findings with quality of life and other important prognostic indicators of severity. In the future, the quantitative measures of disease may have greater utility by showing correlation with prognosis, disease severity and other physiological parameters. These imaging features may provide non-invasive alternative endpoints or surrogate markers to alleviate the need for tissue biopsy or provide an accurate means to monitor rate of disease progression or response to therapy.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2008 - Physiology, Function, and Structure from Medical Images
DOIs
StatePublished - 2008
EventMedical Imaging 2008 - Physiology, Function, and Structure from Medical Images - San Diego, CA, United States
Duration: Feb 17 2008Feb 19 2008

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume6916
ISSN (Print)1605-7422

Other

OtherMedical Imaging 2008 - Physiology, Function, and Structure from Medical Images
Country/TerritoryUnited States
CitySan Diego, CA
Period2/17/082/19/08

Keywords

  • Automatic lung segmentation
  • Diffuse lung disease
  • Emphysema

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

Fingerprint

Dive into the research topics of 'Processing of CT images for analysis of diffuse lung disease in the lung tissue research consortium'. Together they form a unique fingerprint.

Cite this