  As a potential non-invasive biomarker for ischaemic stroke, intracranial
arterial calcification (IAC) could be used for stroke risk assessment on CT
head scans routinely acquired for other reasons (e.g. trauma, confusion).
Artificial intelligence methods can support IAC scoring, but they have not yet
been developed for clinical imaging. Large heterogeneous clinical CT datasets
are necessary for the training of such methods, but they exhibit expected and
unexpected data anomalies. Using CTs from a large clinical trial, the third
International Stroke Trial (IST-3), we propose a pipeline that uses as input
non-enhanced CT scans to output regions of interest capturing selected large
intracranial arteries for IAC scoring. Our method uses co-registration with
templates. We focus on quality control, using information presence along the
z-axis of the imaging to group and apply similarity measures (structural
similarity index measure) to triage assessment of individual image series.
Additionally, we propose superimposing thresholded binary masks of the series
to inspect large quantities of data in parallel. We identify and exclude
unrecoverable samples and registration failures. In total, our pipeline
processes 10,659 CT series, rejecting 4,322 (41%) in the entire process, 1,450
(14% of the total) during quality control, and outputting 6,337 series. Our
pipeline enables effective and efficient region of interest localisation for
targeted IAC segmentation.
