Image-derived input function estimation on a TOF-enabled PET/MR for cerebral blood flow mapping

Mohammad Mehdi Khalighi, Timothy W. Deller, Audrey Peiwen Fan, Praveen K. Gulaka, Bin Shen, Prachi Singh, Jun Hyung Park, Frederick T. Chin, Greg Zaharchuk

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51 Scopus citations

Abstract

15O-H2O PET imaging is an accurate method to measure cerebral blood flow (CBF) but it requires an arterial input function (AIF). Historically, image-derived AIF estimation suffers from low temporal resolution, spill-in, and spill-over problems. Here, we optimized tracer dose on a time-of-flight PET/MR according to the acquisition-specific noise-equivalent count rate curve. An optimized dose of 850 MBq of 15O-H2O was determined, which allowed sufficient counts to reconstruct a short time-frame PET angiogram (PETA) during the arterial phase. This PETA enabled the measurement of the extent of spill-over, while an MR angiogram was used to measure the true arterial volume for AIF estimation. A segment of the high cervical arteries outside the brain was chosen, where the measured spill-in effects were minimal. CBF studies were performed twice with separate [15O]-H2O injections in 10 healthy subjects, yielding values of 88 ± 16, 44 ± 9, and 58 ± 11 mL/min/100 g for gray matter, white matter, and whole brain, with intra-subject CBF differences of 5.0 ± 4.0%, 4.1 ± 3.3%, and 4.5 ± 3.7%, respectively. A third CBF measurement after the administration of 1 g of acetazolamide showed 35 ± 23%, 29 ± 20%, and 33 ± 22% increase in gray matter, white matter, and whole brain, respectively. Based on these findings, the proposed noninvasive AIF method provides robust CBF measurement with 15O-H2O PET.

Original languageEnglish
Pages (from-to)126-135
Number of pages10
JournalJournal of Cerebral Blood Flow and Metabolism
Volume38
Issue number1
DOIs
StatePublished - Jan 1 2018
Externally publishedYes

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by GE Healthcare. Authors would like to thank Charles Stearns for his support of AS-NECR implementation, and Harsh Gandhi and Dawn Holley for their support on scanning all subjects. Audrey Fan is supported by the Stanford Neurosciences Institute Interdisciplinary Scholar Award. The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Mohammad Mehdi Khalighi and Timothy W Deller are employed by GE Healthcare. Greg Zaharchuk received funding support from GE Healthcare.

FundersFunder number
GE Healthcare
Stanford Neurosciences Institute
GE Healthcare

    Keywords

    • Image derived input function
    • PET/MR
    • cerebral blood flow
    • dynamic PET
    • time-of-flight

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