Objective: To develop an automated algorithm for precise quantification of basal ganglia calcification burden based on CT scans in Chinese population.

Background: Basal ganglia calcification is commonly seen in normal aging, but it can also be attributed to pathological conditions such as Fahr’s disease. Current visual grading system^[1], which assigns scores from 0 to 5 based on the area and density of calcification, is limited by its subjective nature and the potential to overlook subtle calcifications. This study aims to overcome these limitations by introducing an objective and automated quantification method.

Method: We analyzed 990 unselected CT scans from our neurology department, divided into three age groups: 20-40, 40-60, and 60-80 years, with 330 scans each. First, CT scans were normalized and segmented into grey matter, white matter and CSF using CTSeg^[2]. Calcification volume were calculated by total volume minus volume of grey matter, white matter and CSF. We defined calcification mass as a marker for basal ganglia calcification burden which was calcification volume multiplied by its density (Hounsfield units). An age-specific threshold for basal ganglia calcification mass was established at the 99th percentile within each age category.

Results: The comparison between the calcification masses obtained by the automated algorithm and manual labeling showed a significant correlation (Spearman rank test, p < 0.001, r = 0.528). The 99th percentile thresholds for basal ganglia mass in the age groups of 20-40, 40-60, and 60-80 years were 22,563.59, 24,875.79, and 25,212.44, respectively.

Conclusion: This study demonstrates the feasibility of accurately quantifying basal ganglia calcification burden using an automated algorithm. Further validation in patients with Fahr’s disease is necessary, and this method shows promise for monitoring brain calcification progression in affected individuals.

References: [1] Nicolas G, Pottier C, Charbonnier C, et al. Phenotypic spectrum of probable and genetically-confirmed idiopathic basal ganglia calcification. Brain. 2013;136(Pt 11):3395-3407. doi:10.1093/brain/awt255
[2] Brudfors M, Balbastre Y, Flandin G, Nachev P, Ashburner J. Flexible Bayesian Modelling for Nonlinear Image Registration. In: Martel AL, Abolmaesumi P, Stoyanov D, et al., eds. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. Lecture Notes in Computer Science. Springer International Publishing; 2020:253-263. doi:10.1007/978-3-030-59716-0_25

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MDS Abstracts - https://www.mdsabstracts.org/abstract/automatic-quantification-of-basal-ganglia-calcification-burden-in-chinese-population/