This project seeks to prove the commercial feasibility of a new approach to analyzing dynamic positron emission tomography (PET) data that would improve sensitivity, quantitative accuracy, and accessibility of imaging the biomarkers of Alzheimer's disease (AD). Recent progress in understanding the nature of neurodegenerative diseases ? especially evidence that the onset of cognitive symptoms of AD can be mitigated ?amplify the critical need of improved quantitative evaluation of AD biomarkers. Dynamic PET may be the most accurate modality capable of achieving this goal. However, current strategies of analyzing dynamic PET images either require complex acquisition protocols with invasive arterial blood sampling procedures or rely on accuracy-degrading approximations such as compartment modeling. We have developed Intelligent Dynamics-Driven Quantitative Diagnostics (IDDQD), a novel processing approach based on factor analysis of dynamic structures with partial clustering used to initiate the process. We have shown that an early version of IDDQD can extract blood and tissue tracer dynamics and the corresponding spatial distributions from 11C-PIB PET scans. SolvingDynamics Inc plans to offer a Research-as-a-Service data processing workflow that will apply IDDQD to dynamic brain PET datasets acquired by the customers, producing accurate quantitative tracer dynamics time-activity curves (TACs) and the distribution of the targeted tissues, including the AD biomarkers beta-amyloid and tau. Our proprietary algorithm does not require the tracer dynamics model to achieve steady state, so a shorter scan can be used to generate results of similar or better accuracy than those produced by current approaches, such as reference tissue- based methods. In this proposal, SolvingDynamics seeks to prove the feasibility of our proposed approach by comparing the diagnostics obtained using IDDQD analysis of dynamic PET data and those obtained from independent measurements. A subcontract group at Lawrence Berkeley National Laboratory has been conducting dynamic 11C-PIB PET studies for several years and has accumulated over 70 cases with PET data matched to both cognitive-memory tests and to post-mortem pathology studies. SolvingDynamics will retrospectively apply its analysis technique to these datasets and compare its computed tissue distributions to standardized uptake volume ratio (SUVR) and distribution volume ratio (DVR) data. A subset of 20 dynamic 11C-PIB PET datasets ranging in length from 15 to 90 minutes will also be analyzed in order to validate the feasibility of reducing the imaging time. In addition, similar studies aimed at reducing imaging time with IDDQD will be performed for 20 PET scans acquired using 18F-AV1451 tracer to image tau.

Public Health Relevance

Positron emission tomography image analysis methodology to be validated in this project aims to improve sensitivity and specificity of tissue analysis in dynamic positron emission tomography, improving diagnosis of Alzheimer's diseases and other neurodegenerative disorders. The main benefits are expected in medical research developing treatments for dementia and in clinical diagnosis, allowing early detection of the disease with less cost and reduced radiation dose to the patients.

National Institute of Health (NIH)
National Institute on Aging (NIA)
Small Business Innovation Research Grants (SBIR) - Phase I (R43)
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Special Emphasis Panel (ZRG1)
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Hsiao, John
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Solvingdynamics, Inc.
United States
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