The goal of the proposed research is to develop automated methods for accurate and reliable quantitative vascular measurements (vessel diameters, cross sections, and distances) in clinical settings. The developed technology will not only provide the cardiologist with immediate quantitative information as well as the means for longitudinal patient assessment, but may also be the basis for more extensive and cross-modality patient evaluations. Under the previously funded research, we developed a system for accurate and reproducible determination of 3D vascular tree centerlines from biplane image pairs within 5 minutes of initiation of analysis. In this continuation proposal, we will not only improve the centerline accuracy, but we will also develop new techniques (a) for fully automatic centerline determination, (b) for lumen reconstruction using breakthroughs developed in our lab, and (c) for alignment and comparison with IVUS data with speeds sufficient to provide clinically critical information during interventions. The studies will extend beyond evaluations of the accuracy and reliability of the measured quantities to include evaluations of impact of these data on clinical decisions during catheterization procedures in pig models and in patients. With the increasing complexity of cardiovascular interventions and the need for accurate characterization of the vessel lumen in clinical studies for cross modafity and longitudinal comparisons, we see this research as one of the critical next steps in vascular analysis. As a result, we are convinced that the proposed research will result in more accurate and more comprehensive vascular data, safer angiographic studies, better longitudinal tracking of patients, quantitative evaluations of progression and regression of stenosis, and subsequent outcome analysis.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Project (R01)
Project #
5R01HL052567-09
Application #
7039062
Study Section
Diagnostic Imaging Study Section (DMG)
Program Officer
Pandit, Sunil
Project Start
1996-02-01
Project End
2008-05-30
Budget Start
2006-06-01
Budget End
2008-05-30
Support Year
9
Fiscal Year
2006
Total Cost
$290,140
Indirect Cost
Name
State University of New York at Buffalo
Department
Neurosurgery
Type
Schools of Medicine
DUNS #
038633251
City
Buffalo
State
NY
Country
United States
Zip Code
14260
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Dmochowski, Jacek; Hoffmann, Kenneth R; Singh, Vikas et al. (2005) Effects of point configuration on the accuracy in 3D reconstruction from biplane images. Med Phys 32:2862-9
Xu, Jinhui; Xu, Guang; Chen, Zhenming et al. (2005) Efficient Algorithms for Determining 3-D Bi-Plane Imaging Geometry. J Comb Optim 10:113-132
Subramanian, Navneeth; Kesavadas, T; Hoffmann, Kenneth R (2004) A prototype virtual reality system for preoperative planning of neuro-endovascular interventions. Stud Health Technol Inform 98:376-81

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