Arterial tree tracking from anatomical landmarks in magnetic resonance angiography scans

Alison O'Neil, Erin Beveridge, Graeme Houston, Lynne McCormick, Ian Poole

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1 Citation (Scopus)

    Abstract

    This paper reports on arterial tree tracking in fourteen Contrast Enhanced MRA volumetric scans, given the positions of a predefined set of vascular landmarks, by using the A* algorithm to find the optimal path for each vessel based on voxel intensity and a learnt vascular probability atlas. The algorithm is intended for use in conjunction with an automatic landmark detection step, to enable fully automatic arterial tree tracking. The scan is filtered to give two further images using the top-hat transform with 4mm and 8mm cubic structuring elements. Vessels are then tracked independently on the scan in which the vessel of interest is best enhanced, as determined from knowledge of typical vessel diameter and surrounding structures. A vascular probability atlas modelling expected vessel location and orientation is constructed by non-rigidly registering the training scans to the test scan using a 3D thin plate spline to match landmark correspondences, and employing kernel density estimation with the ground truth center line points to form a probability density distribution. Threshold estimation by histogram analysis is used to segment background from vessel intensities. The A* algorithm is run using a linear cost function constructed from the threshold and the vascular atlas prior. Tracking results are presented for all major arteries excluding those in the upper limbs. An improvement was observed when tracking was informed by contextual information, with particular benefit for peripheral vessels.
    Original languageEnglish
    Title of host publicationMedical Imaging 2014
    Subtitle of host publicationImage Processing
    EditorsSebastien Ourselin, Martin A. Styner
    Place of PublicationBellingham
    PublisherSPIE-International Society for Optical Engineering
    ISBN (Print)9780819498274
    DOIs
    Publication statusPublished - 2014
    EventSPIE Medical Imaging 2014: Image Processing - Town and Country Resort and Convention Center, San Diego, United States
    Duration: 16 Feb 201418 Feb 2014
    http://spie.org/x106604.xml

    Publication series

    NameProceedings of SPIE
    PublisherSPIE
    Volume9034

    Conference

    ConferenceSPIE Medical Imaging 2014: Image Processing
    CountryUnited States
    CitySan Diego
    Period16/02/1418/02/14
    Internet address

    Fingerprint

    Angiography
    Magnetic resonance
    Cost functions
    Splines

    Cite this

    O'Neil, A., Beveridge, E., Houston, G., McCormick, L., & Poole, I. (2014). Arterial tree tracking from anatomical landmarks in magnetic resonance angiography scans. In S. Ourselin, & M. A. Styner (Eds.), Medical Imaging 2014: Image Processing [90342S] (Proceedings of SPIE; Vol. 9034). Bellingham: SPIE-International Society for Optical Engineering. https://doi.org/10.1117/12.2043264
    O'Neil, Alison ; Beveridge, Erin ; Houston, Graeme ; McCormick, Lynne ; Poole, Ian. / Arterial tree tracking from anatomical landmarks in magnetic resonance angiography scans. Medical Imaging 2014: Image Processing. editor / Sebastien Ourselin ; Martin A. Styner. Bellingham : SPIE-International Society for Optical Engineering, 2014. (Proceedings of SPIE).
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    abstract = "This paper reports on arterial tree tracking in fourteen Contrast Enhanced MRA volumetric scans, given the positions of a predefined set of vascular landmarks, by using the A* algorithm to find the optimal path for each vessel based on voxel intensity and a learnt vascular probability atlas. The algorithm is intended for use in conjunction with an automatic landmark detection step, to enable fully automatic arterial tree tracking. The scan is filtered to give two further images using the top-hat transform with 4mm and 8mm cubic structuring elements. Vessels are then tracked independently on the scan in which the vessel of interest is best enhanced, as determined from knowledge of typical vessel diameter and surrounding structures. A vascular probability atlas modelling expected vessel location and orientation is constructed by non-rigidly registering the training scans to the test scan using a 3D thin plate spline to match landmark correspondences, and employing kernel density estimation with the ground truth center line points to form a probability density distribution. Threshold estimation by histogram analysis is used to segment background from vessel intensities. The A* algorithm is run using a linear cost function constructed from the threshold and the vascular atlas prior. Tracking results are presented for all major arteries excluding those in the upper limbs. An improvement was observed when tracking was informed by contextual information, with particular benefit for peripheral vessels.",
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    O'Neil, A, Beveridge, E, Houston, G, McCormick, L & Poole, I 2014, Arterial tree tracking from anatomical landmarks in magnetic resonance angiography scans. in S Ourselin & MA Styner (eds), Medical Imaging 2014: Image Processing., 90342S, Proceedings of SPIE, vol. 9034, SPIE-International Society for Optical Engineering, Bellingham, SPIE Medical Imaging 2014: Image Processing, San Diego, United States, 16/02/14. https://doi.org/10.1117/12.2043264

    Arterial tree tracking from anatomical landmarks in magnetic resonance angiography scans. / O'Neil, Alison; Beveridge, Erin; Houston, Graeme; McCormick, Lynne; Poole, Ian.

    Medical Imaging 2014: Image Processing. ed. / Sebastien Ourselin; Martin A. Styner. Bellingham : SPIE-International Society for Optical Engineering, 2014. 90342S (Proceedings of SPIE; Vol. 9034).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

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    AB - This paper reports on arterial tree tracking in fourteen Contrast Enhanced MRA volumetric scans, given the positions of a predefined set of vascular landmarks, by using the A* algorithm to find the optimal path for each vessel based on voxel intensity and a learnt vascular probability atlas. The algorithm is intended for use in conjunction with an automatic landmark detection step, to enable fully automatic arterial tree tracking. The scan is filtered to give two further images using the top-hat transform with 4mm and 8mm cubic structuring elements. Vessels are then tracked independently on the scan in which the vessel of interest is best enhanced, as determined from knowledge of typical vessel diameter and surrounding structures. A vascular probability atlas modelling expected vessel location and orientation is constructed by non-rigidly registering the training scans to the test scan using a 3D thin plate spline to match landmark correspondences, and employing kernel density estimation with the ground truth center line points to form a probability density distribution. Threshold estimation by histogram analysis is used to segment background from vessel intensities. The A* algorithm is run using a linear cost function constructed from the threshold and the vascular atlas prior. Tracking results are presented for all major arteries excluding those in the upper limbs. An improvement was observed when tracking was informed by contextual information, with particular benefit for peripheral vessels.

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    O'Neil A, Beveridge E, Houston G, McCormick L, Poole I. Arterial tree tracking from anatomical landmarks in magnetic resonance angiography scans. In Ourselin S, Styner MA, editors, Medical Imaging 2014: Image Processing. Bellingham: SPIE-International Society for Optical Engineering. 2014. 90342S. (Proceedings of SPIE). https://doi.org/10.1117/12.2043264