Heart disease is the leading cause of death and disability in the United States today. Single photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) is now the most widely applied noninvasive method for the detection and risk stratification of coronary artery disease (CAD). Hypothesis: Parametric images of coronary flow reserve (CFR) from dynamic cardiac SPECT will provide more sensitive measures of infarct, ischemia, and lesions on the margin of hemodynamic significance than visual interpretations of static images of conventional MPI SPECT. This application is well timed with the recent introduction of new cardiac stressing agents that allow faster hyperemic response of coronary arteries, and new dedicated cardiac SPECT systems enabling rapid acquisition of dynamic data. However, there has not been accompanying development of algorithms that reduce dynamically acquired data to diagnostic clinical parameters. This proposal will investigate clinical roles of dynamic SPECT by applying algorithms for processing dynamic data acquired in clinical studies using existing SPECT systems as well as a new dedicated cardiac SPECT systems. The developed protocols and algorithms can be implemented in the clinic without additional costs. This is a further advantage of the method in this time of high and rising healthcare costs. The proposed work applies mathematical tools to accurately and precisely quantify kinetic parameters;validates these methods using computer simulations, phantom experiments, and clinical studies;and will perfect clinical roles for dynamic cardiac SPECT. Innovative methods proposed include: use of multi-resolution spatiotemporal mechanical models of the beating heart to estimate model parameters that delineate changes in tracer concentration and cardiac deformation as a function of time (5D dynamic modeling);and development of a deformable phantom to generate more realistic data of cardiac, lung, and patent motion for validation of motion correction algorithms. An unique aspect is our ability to estimate kinetic model parameters, including the blood input function, directly from projections using slow camera rotation speeds without the need for arterial blood sampling. Misalignment between SPECT and CT will be corrected. Kinetic information will be used to estimate scatter components from different organs based on the kinetics of the tracer in the organ. The incorporation of these new algorithms with the new fast dedicated cardiac SPECT cameras will enable quantitation of CFR in a time equal to that of present quantitative PET. Furthermore, our methods will make dynamic SPECT useful in imaging clinics with existing scanners that cannot perform rapid acquisitions. The methods developed will not only be applicable for imaging the myocardium with a variety of perfusion and metabolic agents, which could impact our understanding of the pathophysiology of a variety of cardiovascular indications, but will also have application for imaging tumors and other organ systems such as the kidney and brain.

Public Health Relevance

Dynamic cardiac SPECT will acquire superior diagnostic information when compared to conventional cardiac SPECT studies, the most widely applied and best established noninvasive method for coronary artery disease (CAD) detection and risk stratification. This proposal will prescribe acquisition and computational processing methods which will provide clinical protocols that improve health care through better diagnosis, risk stratification, and management of ischemic heart disease without additional costs for the SPECT procedure. The relevance to the NIH mission and public health is the potential for improved identification of flow limiting lesions with improved diagnosis and management of patients with CAD, the leading cause of death, disability, and cost in the developed world.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Project (R01)
Project #
5R01HL050663-17
Application #
8657077
Study Section
Biomedical Imaging Technology Study Section (BMIT)
Program Officer
Buxton, Denis B
Project Start
1995-01-01
Project End
2015-04-30
Budget Start
2014-05-01
Budget End
2015-04-30
Support Year
17
Fiscal Year
2014
Total Cost
Indirect Cost
Name
Lawrence Berkeley National Laboratory
Department
Radiation-Diagnostic/Oncology
Type
Organized Research Units
DUNS #
City
Berkeley
State
CA
Country
United States
Zip Code
94720
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