Pre-processing and quality control of large clinical CT head datasets for intracranial arterial calcification segmentation

TL;DR

This paper presents a pipeline for pre-processing and quality control of large clinical CT datasets to facilitate intracranial arterial calcification segmentation, addressing data heterogeneity and anomalies in clinical imaging.

Contribution

The authors develop a novel pipeline that uses co-registration, similarity measures, and mask superimposition for quality control and region localization in large CT datasets.

Findings

Processed 10,659 CT series, rejecting 41% due to quality issues.

Successfully identified and excluded unrecoverable samples and registration failures.

Enabled effective localization of regions of interest for IAC segmentation.

Abstract

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.