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    Dense motion propagation from sparse samples

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    Authors
    Smith, Rhodri L.
    Dasari, Paul K. R.
    Lindsay, Clifford
    King, Michael A.
    Wells, Kevin
    UMass Chan Affiliations
    Department of Radiology, Division of Nuclear Medicine
    Document Type
    Journal Article
    Publication Date
    2019-10-21
    Keywords
    respiratory motion correction
    PET-MR
    manifold alignment
    sparse motion model
    Biological and Chemical Physics
    Medical Biophysics
    Radiology
    
    Metadata
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    Link to Full Text
    https://doi.org/10.1088/1361-6560/ab41a0
    Abstract
    There are many applications for which sparse, or partial sampling of dynamic image data can be used for articulating or estimating motion within the medical imaging area. In this new work, we propose a generalized framework for dense motion propagation from sparse samples which represents an example of transfer learning and manifold alignment, allowing the transfer of knowledge across imaging sources of different domains which exhibit different features. Many such examples exist in medical imaging, from mapping 2D ultrasound or fluoroscopy to 3D or 4D data or monitoring dynamic dose delivery from partial imaging data in radiotherapy. To illustrate this approach we animate, or articulate, a high resolution static MR image with 4D free breathing respiratory motion derived from low resolution sparse planar samples of motion. In this work we demonstrate that sparse sampling of dynamic MRI may be used as a viable approach to successfully build models of free- breathing respiratory motion by constrained articulation. Such models demonstrate high contrast, and high temporal and spatial resolution that have so far been hitherto unavailable with conventional imaging methods. The articulation is based on using a propagation model, in the eigen domain, to estimate complete 4D motion vector fields from sparsely sampled free-breathing dynamic MRI data. We demonstrate that this approach can provide equivalent motion vector fields compared to fully sampled 4D dynamic data, whilst preserving the corresponding high resolution/high contrast inherent in the original static volume. Validation is performed on three 4D MRI datasets using eight extracted slices from a fast 4D acquisition (0.7 s per volume). The estimated deformation fields from sparse sampling are compared to the fully sampled equivalents, resulting in an rms error of the order of 2 mm across the entire image volume. We also present exemplar 4D high contrast, high resolution articulated volunteer datasets using this methodology. This approach facilitates greater freedom in the acquisition of free breathing respiratory motion sequences which may be used to inform motion modelling methods in a range of imaging modalities and demonstrates that sparse sampling of free breathing data may be used within a manifold alignment and transfer learning paradigm to estimate fully sampled motion. The method may also be applied to other examples of sparse sampling to produce dense motion propagation.
    Source

    Phys Med Biol. 2019 Oct 21;64(20):205023. doi: 10.1088/1361-6560/ab41a0. Link to article on publisher's site

    DOI
    10.1088/1361-6560/ab41a0
    Permanent Link to this Item
    http://hdl.handle.net/20.500.14038/48395
    PubMed ID
    31487702
    Related Resources

    Link to Article in PubMed

    ae974a485f413a2113503eed53cd6c53
    10.1088/1361-6560/ab41a0
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