Non-invasive arterial input function estimation using an MRA atlas and machine learning.

Journal: EJNMMI research
Published Date: May 23, 2025

Abstract

BACKGROUND: Quantifying biological parameters of interest through dynamic positron emission tomography (PET) requires an arterial input function (AIF) conventionally obtained from arterial blood samples. The AIF can also be non-invasively estimated from blood pools in PET images, often identified using co-registered MRI images. Deploying methods without blood sampling or the use of MRI generally requires total body PET systems with a long axial field-of-view (LAFOV) that includes a large cardiovascular blood pool. However, the number of such systems in clinical use is currently much smaller than that of short axial field-of-view (SAFOV) scanners. We propose a data-driven approach for AIF estimation for SAFOV PET scanners, which is non-invasive and does not require MRI or blood sampling using brain PET scans. The proposed method was validated using dynamic F-fluorodeoxyglucose [F]FDG total body PET data from 10 subjects. A variational inference-based machine learning approach was employed to correct for peak activity. The prior was estimated using a probabilistic vascular MRI atlas, registered to each subject's PET image to identify cerebral arteries in the brain.

Authors

  • Rajat Vashistha
    ARC Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Queensland, Australia.
  • Hamed Moradi
    Centre for Advanced Imaging, University of Queensland, Brisbane, Australia.
  • Amanda Hammond
    Siemens Healthcare Pty Ltd, Melbourne, Australia.
  • Kieran O'Brien
    Center for Advanced Imaging, University of Queensland, St Lucia, QLD, Australia.
  • Axel Rominger
  • Hasan Sari
    Department of Radiology, Massachusetts General Hospital & Harvard Medical School, Boston, MA, United States of America.
  • Kuangyu Shi
    Universitätsklinik für Nuklearmedizin, Inselspital University Hospital Bern, University of Bern, Bern, Switzerland.
  • Viktor Vegh
    Centre for Advanced Imaging, The University of Queensland, Brisbane, Queensland, Australia.
  • David Reutens
    Centre for Advanced Imaging, University of Queensland, Brisbane, Australia.

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