Ms. Vanessa A. Zavaletta Profile

Vanessa A. Zavaletta

Student at Mayo Clinic

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Publications (3)

Proceedings Article | 20 March 2008 Paper

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

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

Proceedings Volume 6916, 691614 (2008) https://doi.org/10.1117/12.773159

KEYWORDS: Lung, Tissues, Image segmentation, Computed tomography, Emphysema, Databases, Chronic obstructive pulmonary disease, Glasses, Image processing, Image analysis

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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 3^rd and limited 4^th 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.

Proceedings Article | 10 May 2007 Paper

Nonlinear histogram binning for quantitative analysis of lung tissue fibrosis in high-resolution CT data

Vanessa Zavaletta, Brian Bartholmai, Richard Robb

Proceedings Volume 6511, 65111Q (2007) https://doi.org/10.1117/12.710220

KEYWORDS: Lung, Tissues, Computed tomography, Glasses, Emphysema, Statistical analysis, Computer programming, Matrices, Error analysis, Quantitative analysis

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Proceedings Article | 13 March 2006 Paper

High resolution multi-detector CT aided tissue analysis and quantification of lung fibrosis

Vanessa Zavaletta, Ronald Karwoski, Brian Bartholmai, Richard Robb

Proceedings Volume 6143, 61432Z (2006) https://doi.org/10.1117/12.653450

KEYWORDS: Lung, Tissues, Computed tomography, Image resolution, Visualization, Image segmentation, Image analysis, Error analysis, Pulmonary fibrosis, Pulmonary function tests

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