Model fitting for sequences of images

H. Malcolm Hudson, Craig Walsh

    Research output: Contribution to journalArticlepeer-review

    1 Citation (Scopus)

    Abstract

    The aim of this paper is to introduce methods for estimating mixture probabilities which specify time-activity curves (distributions of activity in time) in sequences of medical images. These parameters are of medical significance as they represent metabolic processes (functioning) occurring within each voxel within an imaged region within the body. Each distribution of activity by time is modelled as a mixture of specified (basis) components. The component distributions are those in a large (spectral) class of specified densities, for instance, exponential decay distributions with specified half-life. We describe the integration of maximum likelihood expectation maximization (ML-EM) reconstruction from indirect data with mixture modelling of the original time activity, and assess our ability to determine the distribution of this activity, varying in space and time.

    Original languageEnglish
    Pages (from-to)323-335
    Number of pages13
    JournalComputational Statistics and Data Analysis
    Volume32
    Issue number3-4
    Publication statusPublished - 28 Jan 2000

    Keywords

    • Emission tomography
    • EMPIRA
    • ML-EM
    • NNLS
    • Spectral model

    Fingerprint

    Dive into the research topics of 'Model fitting for sequences of images'. Together they form a unique fingerprint.

    Cite this