Paper
12 April 2005 Parametric modeling for quantitative analysis of pulmonary structure to function relationships
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Abstract
While lung anatomy is well understood, pulmonary structure-to-function relationships such as the complex elastic deformation of the lung during respiration are less well documented. Current methods for studying lung anatomy include conventional chest radiography, high-resolution computed tomography (CT scan) and magnetic resonance imaging with polarized gases (MRI scan). Pulmonary physiology can be studied using spirometry or V/Q nuclear medicine tests (V/Q scan). V/Q scanning and MRI scans may demonstrate global and regional function. However, each of these individual imaging methods lacks the ability to provide high-resolution anatomic detail, associated pulmonary mechanics and functional variability of the entire respiratory cycle. Specifically, spirometry provides only a one-dimensional gross estimate of pulmonary function, and V/Q scans have poor spatial resolution, reducing its potential for regional assessment of structure-to-function relationships. We have developed a method which utilizes standard clinical CT scanning to provide data for computation of dynamic anatomic parametric models of the lung during respiration which correlates high-resolution anatomy to underlying physiology. The lungs are segmented from both inspiration and expiration three-dimensional (3D) data sets and transformed into a geometric description of the surface of the lung. Parametric mapping of lung surface deformation then provides a visual and quantitative description of the mechanical properties of the lung. Any alteration in lung mechanics is manifest by alterations in normal deformation of the lung wall. The method produces a high-resolution anatomic and functional composite picture from sparse temporal-spatial methods which quantitatively illustrates detailed anatomic structure to pulmonary function relationships impossible for translational methods to provide.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Clifton R. Haider, Brian J. Bartholmai M.D., David R. Holmes III, Jon J. Camp, and Richard A. Robb "Parametric modeling for quantitative analysis of pulmonary structure to function relationships", Proc. SPIE 5744, Medical Imaging 2005: Visualization, Image-Guided Procedures, and Display, (12 April 2005); https://doi.org/10.1117/12.595244
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KEYWORDS
Lung

Computed tomography

Physiology

Magnetic resonance imaging

Data modeling

Visualization

Mechanics

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