  Perfusion imaging is crucial in acute ischemic stroke for quantifying the
salvageable penumbra and irreversibly damaged core lesions. As such, it helps
clinicians to decide on the optimal reperfusion treatment. In perfusion CT
imaging, deconvolution methods are used to obtain clinically interpretable
perfusion parameters that allow identifying brain tissue abnormalities.
Deconvolution methods require the selection of two reference vascular functions
as inputs to the model: the arterial input function (AIF) and the venous output
function, with the AIF as the most critical model input. When manually
performed, the vascular function selection is time demanding, suffers from poor
reproducibility and is subject to the professionals' experience. This leads to
potentially unreliable quantification of the penumbra and core lesions and,
hence, might harm the treatment decision process. In this work we automatize
the perfusion analysis with AIFNet, a fully automatic and end-to-end trainable
deep learning approach for estimating the vascular functions. Unlike previous
methods using clustering or segmentation techniques to select vascular voxels,
AIFNet is directly optimized at the vascular function estimation, which allows
to better recognise the time-curve profiles. Validation on the public ISLES18
stroke database shows that AIFNet reaches inter-rater performance for the
vascular function estimation and, subsequently, for the parameter maps and core
lesion quantification obtained through deconvolution. We conclude that AIFNet
has potential for clinical transfer and could be incorporated in perfusion
deconvolution software.
