|
Standard Database for CT Lung Images
Samuel Armato, Ph.D.
University of Chicago
Grant Number: U01CA091090
The broad, long-term objective of this research project is to create a publicly available standard database of spiral computed tomography (CT) lung images. This lung image database will become an essential resource for the development of computer-aided diagnostic (CAD) techniques designed to help radiologists identify lung cancer in CT scans. The need for a standard lung image database is based on two recent developments. The first is the advancement of multi-slice CT scanners, which acquire images of multiple anatomic sections during each gantry rotation. Consequently, these scanners may generate an extensive amount of image data. The second development is the growing awareness among the American public and clinicians of the potential benefits of lung cancer screening using a low-dose spiral CT protocol. These developments are expected to dramatically increase the burden on radiologists. Moreover, primary interpretation from softcopy display will become a practical necessity.
What emerges from this scenario is a requirement for automated image processing methods that provide radiologists with quantitative information about suspicious abnormalities in the CT image data. Radiologists will then incorporate this information into their diagnostic decision-making process, with the expectation that cancer-detection sensitivity may be improved while decreasing both observer variability and interpretation time. Creation of a standard lung image database is critical to the endeavor of imaging research. This proposal addresses the important clinical and technical issues relevant to the creation of such a database.
The specific aims of the research are: (1) to identify the clinical requirements that must be imposed on a standard CT lung image database, (2) to address the technical issues and criteria involved with case selection for the CT lung image database, (3) to collect cases for the CT lung image database as a member of the Lung Image Database Consortium, (4) to develop strategies for the assessment of image processing and CAD methods using the CT lung image database, and (5) to investigate the effect of image reconstruction, multi-modality image registration, and registration of images acquired at different times on the utility of the CT lung image database.
As a member of the Consortium, we would demonstrate the flexibility necessary to reach consensus on the creation of a database that will serve as a standard resource for imaging research. The ideas presented in this proposal are expected to stimulate the efforts of the Consortium toward that goal.
< Previous | Next Section > Main |
