Poster — Thur Eve — 03: Application of the non-negative matrix factorization technique to [11C]-DTBZ dynamic PET data for the early detection of Parkinson's disease

Authors

  • Lee Dong-Chang,

    1. CancerCare Manitoba, Winnipeg, MB, Canada
    2. Department of Oncology, University of Alberta, Edmonton, AB, Canada
    3. Cross Cancer Institute, Alberta Health Services, Edmonton, AB, Canada
    4. Division of Neurology, University of Alberta, Edmonton, AB, Canada
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  • Jans Hans,

    1. CancerCare Manitoba, Winnipeg, MB, Canada
    2. Department of Oncology, University of Alberta, Edmonton, AB, Canada
    3. Cross Cancer Institute, Alberta Health Services, Edmonton, AB, Canada
    4. Division of Neurology, University of Alberta, Edmonton, AB, Canada
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  • Martin Wayne,

    1. CancerCare Manitoba, Winnipeg, MB, Canada
    2. Department of Oncology, University of Alberta, Edmonton, AB, Canada
    3. Cross Cancer Institute, Alberta Health Services, Edmonton, AB, Canada
    4. Division of Neurology, University of Alberta, Edmonton, AB, Canada
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  • Wieler Marguerite,

    1. CancerCare Manitoba, Winnipeg, MB, Canada
    2. Department of Oncology, University of Alberta, Edmonton, AB, Canada
    3. Cross Cancer Institute, Alberta Health Services, Edmonton, AB, Canada
    4. Division of Neurology, University of Alberta, Edmonton, AB, Canada
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  • McEwan Sandy,

    1. CancerCare Manitoba, Winnipeg, MB, Canada
    2. Department of Oncology, University of Alberta, Edmonton, AB, Canada
    3. Cross Cancer Institute, Alberta Health Services, Edmonton, AB, Canada
    4. Division of Neurology, University of Alberta, Edmonton, AB, Canada
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  • Riauka Terence

    1. CancerCare Manitoba, Winnipeg, MB, Canada
    2. Department of Oncology, University of Alberta, Edmonton, AB, Canada
    3. Cross Cancer Institute, Alberta Health Services, Edmonton, AB, Canada
    4. Division of Neurology, University of Alberta, Edmonton, AB, Canada
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Abstract

In this work, a class of non-negative matrix factorization (NMF) technique known as alternating non-negative least squares, combined with the projected gradient method, is used to analyze twenty-five [11C]-DTBZ dynamic PET/CT brain data. For each subject, a two-factor model is assumed and two factors representing the striatum (factor 1) and the non-striatum (factor 2) tissues are extracted using the proposed NMF technique and commercially available factor analysis software “Pixies”. The extracted factor 1 and 2 curves represent the binding site of the radiotracer and describe the uptake and clearance of the radiotracer by soft tissues in the brain, respectively. The proposed NMF technique uses prior information about the dynamic data to obtain sample time-activity curves representing the striatum and the non-striatum tissues. These curves are then used for “warm” starting the optimization. Factor solutions from the two methods are compared graphically and quantitatively. In healthy subjects, radiotracer uptake by factors 1 and 2 are approximately 35–40% and 60–65%, respectively. The solutions are also used to develop a factor-based metric for the detection of early, untreated Parkinson's disease. The metric stratifies healthy subjects from suspected Parkinson's patients (based on the graphical method). The analysis shows that both techniques produce comparable results with similar computational time. The “semi-automatic” approach used by the NMF technique allows clinicians to manually set a starting condition for “warm” starting the optimization in order to facilitate control and efficient interaction with the data.

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