Image-derived input function derived from a supervised clustering algorithm: Methodology and validation in a clinical protocol using [ 11C](R)-rolipram

Chul Hyoung Lyoo, Paolo Zanotti-Fregonara, Sami S. Zoghbi, Jeih San Liow, Rong Xu, Victor W. Pike, Carlos A. Zarate, Masahiro Fujita, Robert B. Innis

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11 Citations (Scopus)

Abstract

Image-derived input function (IDIF) obtained by manually drawing carotid arteries (manual-IDIF) can be reliably used in [11C](R)-rolipram positron emission tomography (PET) scans. However, manual-IDIF is time consuming and subject to inter-and intra-operator variability. To overcome this limitation, we developed a fully automated technique for deriving IDIF with a supervised clustering algorithm (SVCA). To validate this technique, 25 healthy controls and 26 patients with moderate to severe major depressive disorder (MDD) underwent T1-weighted brain magnetic resonance imaging (MRI) and a 90-minute [11C](R)-rolipram PET scan. For each subject, metabolite-corrected input function was measured from the radial artery. SVCA templates were obtained from 10 additional healthy subjects who underwent the same MRI and PET procedures. Cluster- IDIF was obtained as follows: 1) template mask images were created for carotid and surrounding tissue; 2) parametric image of weights for blood were created using SVCA; 3) mask images to the individual PET image were inversely normalized; 4) carotid and surrounding tissue time activity curves (TACs) were obtained from weighted and unweighted averages of each voxel activity in each mask, respectively; 5) partial volume effects and radiometabolites were corrected using individual arterial data at four points. Logan-distribution volume (VT/fP) values obtained by cluster-IDIF were similar to reference results obtained using arterial data, as well as those obtained using manual-IDIF; 39 of 51 subjects had a V T/fP error of ,5%, and only one had error >10%. With automatic voxel selection, cluster-IDIF curves were less noisy than manual-IDIF and free of operator-related variability. Cluster-IDIF showed widespread decrease of about 20% [11C](R)-rolipram binding in the MDD group. Taken together, the results suggest that cluster-IDIF is a good alternative to full arterial input function for estimating Logan-VT/fP in [11C](R)-rolipram PET clinical scans. This technique enables fully automated extraction of IDIF and can be applied to other radiotracers with similar kinetics.

Original languageEnglish
Article numbere89101
JournalPloS one
Volume9
Issue number2
DOIs
Publication statusPublished - 2014 Feb 20

All Science Journal Classification (ASJC) codes

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • General

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    Lyoo, C. H., Zanotti-Fregonara, P., Zoghbi, S. S., Liow, J. S., Xu, R., Pike, V. W., Zarate, C. A., Fujita, M., & Innis, R. B. (2014). Image-derived input function derived from a supervised clustering algorithm: Methodology and validation in a clinical protocol using [ 11C](R)-rolipram. PloS one, 9(2), [e89101]. https://doi.org/10.1371/journal.pone.0089101