The clinical availability of volumetric high-resolution MDCT image data has increased the importance of regional assessment of pulmonary structure and function. Identification of the lung lobes is essential, not only so that quantitative measures can be reported on a lobe-by-lobe basis, but also to design and plan interventions targeted toward a specific lung region. Importantly, approximately 25% of pulmonary CT-images show incomplete lobar tissues and therefore there may exist inter-lobar communication to adversely affect therapy. Accurate identification of the lobar tissues, in both the complete and the incomplete cases, is urgently needed for quantitative lung analysis. The technology development research proposed below is motivated by the following overriding hypothesis: Our novel tissue detection will provide accurate and robust assessment of pulmonary lobes in 3-D, even in the presence of parenchymal disease or anatomical abnormality causing incomplete or disintegrating tissues. The Phase-I feasibility proposal presented here has the following specific aims: 1. Develop a method for automated accurate identification of lobar tissue surfaces in volumetric high- resolution MDCT images. 2. Develop a method for identification and quantitative assessment of incomplete and/or disintegrating fissures. 3. Demonstrate feasibility of the developed lobar segmentation method in subjects with pathologies causing tissue disintegration such as emphysema. As part of the Phase I proposal, we will demonstrate that identification (segmentation) of lobar tissue surfaces can be accomplished with high accuracy in complete tissues. We will also demonstrate that the accuracy in incomplete tissues can be accomplished with a positioning error that is comparable to an inter-observer variability of expert observers performing the same task. Once feasibility of the proposed quantification is proven, Phase II will concentrate on development and validation of a comprehensive software tool for assessment of lobar morphology, volumetric characterization, collateral cross-lobar ventilation and perfusion, and identification of airways and vessels connecting the neighboring lobes in lungs with incomplete tissues. Advances in medical imaging and novel, non-surgical therapies for lung disease make it critical to accurately identify and describe lung anatomy. Identification of the lung lobes is essential, not only to the compartmentalization of quantitative measures used to phenotype pathology and to follow changes in pathology, but also to design interventions seeking to eliminate ventilation to a targeted lung region. In the approximately 25% of CT-imaged patients with incomplete tissues, area of tissue incompleteness and the level of inter-lobar communication must be determined since it affects lobar-based analysis as well as the outcomes of interventional treatment. No such methods are currently available. In this project we will develop methods to identify the five lobes of the human lung in CT images, including subjects with incomplete tissue anatomy. This will make it possible to measure and study disease on a regional lobe-by-lobe basis. The proposed methods are urgently needed to take advantage of emerging therapies for the early treatment of lung disease.
Thesaurus Terms: Lung Imaging /Visualization /Scanning, Method Development, Respiratory Disorder Diagnosis, Three Dimensional Imaging /Topography Automated Data Processing, Computed Axial Tomography, Emphysema, Image Processing, Lung Disorder, Pathologic Process Bioimaging /Biomedical Imaging, Human Subject, Patient Oriented Research
