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Manifold drived MR-less PiB SUVR normalisation

Conference Paper


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Abstract


  • Pittsburgh Compound B (PiB) is a C11 PET tracer designed to bind

    to amyloid plaques, one of the hallmark of Alzheimer’s disease. The potential

    of PiB as an early marker of Alzheimer’s disease has lead to an increasing use

    of PiB and the development of several F18 equivalents. Quantitative analysis of

    PiB images requires an accurate normalisation, parcellation and estimation of

    retention in the brain’s gray matter. Typically this relies on co-registered MRI

    to extract the cerebellum, compute the standardized uptake value ratio (SUVR)

    and provide parcellation and segmentation for quantification of neocortical

    SUVR. However, not all subjects undergo MRI. In this paper we propose a

    highly accurate MR-less parcellation, SUVR normalisation and quantification

    method for PiB images. This involves rigidly registering the raw PiB images to

    a PiB atlas, computing pair-wise normalised mutual information, and

    constructing a 2D manifold. Each new scan is mapped on the manifold and its k

    nearest neighbours are selected as atlases in a segmentation propagation scheme

    with their associated MRI segmentations and parcellation used as priors to

    estimate the SUVR normalisation and quantification. Comparison of our MRless

    approach to an MR-based approach showed a coefficient of correlation of

    neocortical PiB SUVR of R2=0.94 and an absolute mean error of 5.9%.

Authors


  •   Bourgeat, Pierrick (external author)
  •   Raniga, Parnesh (external author)
  •   Dore, Vincent (external author)
  •   Zhou, Luping
  •   Macaulay, S L. (external author)
  •   Martins, Ralph N. (external author)
  •   Masters, Colin L. (external author)
  •   Ames, David (external author)
  •   Ellis, Katherine A. (external author)
  •   Villemagne, Victor L. (external author)
  •   Rowe, Christopher C. (external author)
  •   Salvado, Oliver (external author)
  •   Fripp, Jurgen (external author)

Publication Date


  • 2012

Citation


  • Bourgeat, P., Raniga, P., Dore, V., Zhou, L., Macaulay, S. L., Martins, R., Masters, C. L., Ames, D., Ellis, K. A., Villemagne, V. L., Rowe, C. C., Salvado, O. & Fripp, J. (2012). Manifold drived MR-less PiB SUVR normalisation. In L. Wang, P. Yushkevich & S. Ourselin (Eds.), 15th International Conference on Medical Image Computing and Computer Assisted Intervention: Workshop on Novel Imaging Biomarkers for Alzheimer''s Disease and Related Disorders (NIBAD) (pp. 1-12). Nice, France: CreateSpace Independent Publishing Platform.

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=2886&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/1877

Start Page


  • 1

End Page


  • 12

Place Of Publication


  • http://www.google.com.au/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CDAQFjAA&url=http%3A%2F%2Fpicsl.upenn.edu%2Fdocs%2Fnibad12_proceedings_reduced_20120905.pdf&ei=ybvdUuCQEquFiAfV6oGoBg&usg=AFQjCNEHUnD7-NUQq6M9HkEt2U_LW4IoWA&sig2=Yk3DDQ2WXQ3m0eQ_iHMB0A&bvm=bv.59568121,d.aGc

Abstract


  • Pittsburgh Compound B (PiB) is a C11 PET tracer designed to bind

    to amyloid plaques, one of the hallmark of Alzheimer’s disease. The potential

    of PiB as an early marker of Alzheimer’s disease has lead to an increasing use

    of PiB and the development of several F18 equivalents. Quantitative analysis of

    PiB images requires an accurate normalisation, parcellation and estimation of

    retention in the brain’s gray matter. Typically this relies on co-registered MRI

    to extract the cerebellum, compute the standardized uptake value ratio (SUVR)

    and provide parcellation and segmentation for quantification of neocortical

    SUVR. However, not all subjects undergo MRI. In this paper we propose a

    highly accurate MR-less parcellation, SUVR normalisation and quantification

    method for PiB images. This involves rigidly registering the raw PiB images to

    a PiB atlas, computing pair-wise normalised mutual information, and

    constructing a 2D manifold. Each new scan is mapped on the manifold and its k

    nearest neighbours are selected as atlases in a segmentation propagation scheme

    with their associated MRI segmentations and parcellation used as priors to

    estimate the SUVR normalisation and quantification. Comparison of our MRless

    approach to an MR-based approach showed a coefficient of correlation of

    neocortical PiB SUVR of R2=0.94 and an absolute mean error of 5.9%.

Authors


  •   Bourgeat, Pierrick (external author)
  •   Raniga, Parnesh (external author)
  •   Dore, Vincent (external author)
  •   Zhou, Luping
  •   Macaulay, S L. (external author)
  •   Martins, Ralph N. (external author)
  •   Masters, Colin L. (external author)
  •   Ames, David (external author)
  •   Ellis, Katherine A. (external author)
  •   Villemagne, Victor L. (external author)
  •   Rowe, Christopher C. (external author)
  •   Salvado, Oliver (external author)
  •   Fripp, Jurgen (external author)

Publication Date


  • 2012

Citation


  • Bourgeat, P., Raniga, P., Dore, V., Zhou, L., Macaulay, S. L., Martins, R., Masters, C. L., Ames, D., Ellis, K. A., Villemagne, V. L., Rowe, C. C., Salvado, O. & Fripp, J. (2012). Manifold drived MR-less PiB SUVR normalisation. In L. Wang, P. Yushkevich & S. Ourselin (Eds.), 15th International Conference on Medical Image Computing and Computer Assisted Intervention: Workshop on Novel Imaging Biomarkers for Alzheimer''s Disease and Related Disorders (NIBAD) (pp. 1-12). Nice, France: CreateSpace Independent Publishing Platform.

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=2886&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/1877

Start Page


  • 1

End Page


  • 12

Place Of Publication


  • http://www.google.com.au/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CDAQFjAA&url=http%3A%2F%2Fpicsl.upenn.edu%2Fdocs%2Fnibad12_proceedings_reduced_20120905.pdf&ei=ybvdUuCQEquFiAfV6oGoBg&usg=AFQjCNEHUnD7-NUQq6M9HkEt2U_LW4IoWA&sig2=Yk3DDQ2WXQ3m0eQ_iHMB0A&bvm=bv.59568121,d.aGc