Randall W. Grout

Systems for Lung Volume Standardization during Static and Dynamic MDCT-based Quantitative Assessment of Pulmonary Structure and Function

RATIONALE AND OBJECTIVES Multidetector-row computed tomography (MDCT) has emerged as a tool for quantitative assessment of parenchymal destruction, air trapping (density metrics), and airway remodeling (metrics relating airway wall and lumen geometry) in chronic obstructive pulmonary disease (COPD) and asthma. Critical to the accuracy and interpretability of these MDCT-derived metrics is the assurance that the lungs are scanned during a breathhold at a standardized volume. MATERIALS AND METHODS A computer monitored turbine-based flow meter system was developed to control patient breathholds and facilitate static imaging at fixed percentages of the vital capacity. Because of calibration challenges with gas density changes during multibreath xenon CT, an alternative system was required. The design incorporated dual rolling seal pistons. Both systems were tested in a laboratory environment and human subject trials. RESULTS The turbine-based system successfully controlled lung volumes in 32/37 subjects, having a linear relationship for CT measured air volume between repeated scans: for all scans, the mean and confidence interval of the differences (scan1-scan2) was -9 mL (-169, 151); for total lung capacity alone 6 mL (-164, 177); for functional residual capacity alone, -23 mL (-172, 126). The dual-piston system successfully controlled lung volume in 31/41 subjects. Study failures related largely to subject noncompliance with verbal instruction and gas leaks around the mouthpiece. CONCLUSION We demonstrate the successful use of a turbine-based system for static lung volume control and demonstrate its inadequacies for dynamic xenon CT studies. Implementation of a dual-rolling seal spirometer has been shown to adequately control lung volume for multibreath wash-in xenon CT studies. These systems coupled with proper patient coaching provide the tools for the use of CT to quantitate regional lung structure and function. The wash-in xenon CT method for assessing regional lung function, although not necessarily practical for routine clinical studies, provides for a dynamic protocol against which newly emerging single breath, dual-energy xenon CT measures can be validated.

A New Paradigm of Interactive Artery/Vein Separation in Noncontrast Pulmonary CT Imaging Using Multiscale Topomorphologic Opening

Distinguishing pulmonary arterial and venous (A/V) trees via in vivo imaging is a critical first step in the quantification of vascular geometry for the purpose of diagnosing several pulmonary diseases and to develop new image-based phenotypes. A multiscale topomorphologic opening (MSTMO) algorithm has recently been developed in our laboratory for separating A/V trees via noncontrast pulmonary human CT imaging. The method starts with two sets of seeds-one for each of A/V trees and combines fuzzy distance transform and fuzzy connectivity in conjunction with several morphological operations leading to locally adaptive iterative multiscale opening of two mutually conjoined structures. In this paper, we introduce the methods for handling “local update” and “separators” into our previous theoretical formulation and incorporate the algorithm into an effective graphical user interface (GUI). Results of a comprehensive evaluative study assessing both accuracy and reproducibility of the method under the new setup are presented and also, the effectiveness of the GUI-based system toward improving A/V separation results is examined. Accuracy of the method has been evaluated using mathematical phantoms, CT images of contrast-separated pulmonary A/V casting of a pigs lung and noncontrast pulmonary human CT imaging. The method has achieved 99% true A/V labeling in the cast phantom and, almost, 92-94% true labeling in human lung data. Reproducibility of the method has been evaluated using multiuser A/V separation in human CT data along with contrast-enhanced CT images of a pigs lung at different positive end-expiratory pressures (PEEPs). The method has achieved, almost, 92-98% agreements in multiuser A/V labeling with ICC for A/V measures being over 0.96-0.99. Effectiveness of the GUI-based method has been evaluated on human data in terms of improvements of accuracy of A/V separation results and results have shown 8-22% improvements in true A/V labeling. Both qualitative and quantitative results found are very promising.

Multilevel tree analysis of pulmonary artery/vein trees in noncontrast CT images

Diseases like pulmonary embolism and pulmonary hypertension are associated with vascular dystrophy. Identifying such pulmonary artery/vein (A/V) tree dystrophy in terms of quantitative measures via CT imaging significantly facilitates early detection of disease or a treatment monitoring process. A tree structure, consisting of nodes and connected arcs, linked to the volumetric representation allows multi-level geometric and volumetric analysis of A/V trees. Here, a new theory and method is presented to generate multi-level A/V tree representation of volumetric data and to compute quantitative measures of A/V tree geometry and topology at various tree hierarchies. The new method is primarily designed on arc skeleton computation followed by a tree construction based topologic and geometric analysis of the skeleton. The method starts with a volumetric A/V representation as input and generates its topologic and multi-level volumetric tree representations long with different multi-level morphometric measures. A new recursive merging and pruning algorithms are introduced to detect bad junctions and noisy branches often associated with digital geometric and topologic analysis. Also, a new notion of shortest axial path is introduced to improve the skeletal arc joining two junctions. The accuracy of the multi-level tree analysis algorithm has been evaluated using computer generated phantoms and pulmonary CT images of a pig vessel cast phantom while the reproducibility of method is evaluated using multi-user A/V separation of in vivo contrast-enhanced CT images of a pig lung at different respiratory volumes.

Multi-scale topo-morphometric opening of arteries and veins: an evaluative study via pulmonary CT imaging

Distinguishing pulmonary arterial and venous (A/V) trees via in vivo imaging is essential for quantification of vascular geometry useful to diagnose several pulmonary diseases. A multi-scale topomorphologic opening algorithm has recently been introduced separating A/V trees via non-contrast CT imaging. The method starts with two sets of seeds -- one for each of A/V trees and combines fuzzy distance transform, fuzzy connectivity, and morphologic reconstruction leading to locally-adaptive multi-scale opening of two mutually fused structures. Here, we present results of a comprehensive validation study assessing both reproducibility and accuracy of the method. Accuracy of the method is examined using both mathematical phantoms and CT images of contrast-separated pulmonary A/V casting of a pigs lung. Reproducibility of the method is evaluated using multi-user A/V separations of patientss CT pulmonary data and contrast-enhanced CT data of a pigs lung at different volumes. The qualitative and quantitative results are very promising.