Multi-stage automated local arterial input function selection in perfusion MRI



Cerebral blood flow (CBF) quantification using dynamic-susceptibility contrast MRI can be achieved via model-independent deconvolution, with local arterial input function (AIF) deconvolution methods identifying multiple arterial regions with unique corresponding arterial input functions. The clinical application of local AIF methods necessitates an efficient and fully automated solution. To date, such local AIF methods have relied on the computation of a singular surrogate measure of bolus arrival time or custom arterial scoring functions to infer vascular supply origins. This paper aims to introduce a new local AIF method that alternatively utilises a multi-stage approach to perform AIF selection.

Material and methods

A fully automated, multi-stage local AIF method is proposed, leveraging both signal-based cluster analysis and priority flooding to define arterial regions and their corresponding vascular supply origins. The introduced method was applied to data from four patients with cerebrovascular disease who showed significant artefacts when using a prevailing automated local AIF method.


The immediately apparent image artefacts found using the pre-existing method due to poor AIF selection were found to be absent when using the proposed method.


The results suggest the proposed solution provides a more robust approach to perfusion quantification than currently available fully automated local AIF methods.

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    Note that in practice, the deconvolution approach leads to CBF values in relative (arbitrary) units, and a further scaling is required for converting CBF to absolute units in ml/100 g/min [3].


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We are grateful to Siemens Healthineers (Erlangen, Germany), the National Health and Medical Research Council (NHMRC) of Australia, and the Victorian Government's Operational Infrastructure Support Program for their support. We thank Prof. Fenella Kirkham and Dr. Vijeya Ganesan (UCL GOS Institute of Child Health, London, UK) for their contribution to patient selection.


This study was funded by the Australian National Health and Medical Research Council (NHMRC; APP1091593), National Health and Medical Research Council (APP1117724).

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Correspondence to Alan Connelly.

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Author Tabbara declares he has no conflict of interest. Author Connelly has received research funding from Siemens Healthcare Pty Ltd, Australia. Author Calamante has received research funding from Siemens Healthcare Pty Ltd, Australia.

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This article does not contain any studies with animals performed by any of the authors. All procedures performed in studies involving human participants were in accordance with the ethical standards of our institutional research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individual participants included in the study.

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Tabbara, R., Connelly, A. & Calamante, F. Multi-stage automated local arterial input function selection in perfusion MRI. Magn Reson Mater Phy 33, 357–365 (2020).

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  • Perfusion MRI
  • Arterial input function
  • Bolus dispersion
  • Cerebral blood flow
  • Stroke