A novel CT-based automated analysis method provides comparable results with MRI in measuring brain atrophy and white matter lesions

Abstract

Purpose Automated analysis of neuroimaging data is commonly based on magnetic resonance imaging (MRI), but sometimes the availability is limited or a patient might have contradictions to MRI. Therefore, automated analyses of computed tomography (CT) images would be beneficial. Methods We developed an automated method to evaluate medial temporal lobe atrophy (MTA), global cortical atrophy (GCA), and the severity of white matter lesions (WMLs) from a CT scan and compared the results to those obtained from MRI in a cohort of 214 subjects gathered from Kuopio and Helsinki University Hospital registers from 2005 - 2016. Results The correlation coefficients of computational measures between CT and MRI were 0.9 (MTA), 0.82 (GCA), and 0.86 (Fazekas). CT-based measures were identical to MRI-based measures in 60% (MTA), 62% (GCA) and 60% (Fazekas) of cases when the measures were rounded to the nearest full grade variable. However, the difference in measures was 1 or less in 97–98% of cases. Similar results were obtained for cortical atrophy ratings, especially in the frontal and temporal lobes, when assessing the brain lobes separately. Bland–Altman plots and weighted kappa values demonstrated high agreement regarding measures based on CT and MRI. Conclusions MTA, GCA, and Fazekas grades can also be assessed reliably from a CT scan with our method. Even though the measures obtained with the different imaging modalities were not identical in a relatively extensive cohort, the differences were minor. This expands the possibility of using this automated analysis method when MRI is inaccessible or contraindicated.

Grey matter atrophy in patients with benign multiple sclerosis

“…Also, voxel size variation does not seem to affect the results in cNeuro® MRI quantification tool. Nevertheless, we consider that our results are logical and suggest the methodology is quite robust (Kaipainen et al, 2021).…”

Fully Automatic Classification of Brain Atrophy on NCCT Images in Cerebral Small Vessel Disease: A Pilot Study Using Deep Learning Models

“…Subsequently, Chen and Pitkänen used machine learning and convolutional networks to further quantify white matter lesions on NCCT ( 41 , 42 ). Recently, Kaipainen completed the simultaneous assessment of white matter lesions and brain atrophy, but the automated training is very complicated and requires MRI as template mapping ( 43 ). Moreover, the ventricular system has not been integrated.…”

Assessing CT-based Volumetric Analysis via Transfer Learning with MRI and Manual Labels for Idiopathic Normal Pressure Hydrocephalus

“…Interestingly, while numerous studies have successfully demonstrated brain tissue segmentation and volumetric assessment using MR images 26,27 , these techniques have encountered challenges when attempted using CT scans with inferior soft tissue contrast. Recent research has thus explored robust automated and semi-automated segmentation(s) 28,29 and volumetric assessments of CT images using state-of-the-art image processing techniques centred on deep learning; specifically, segmentation models employing deep learning architectures, such as fully connected convolutional neural networks and 2D/3D U-Nets have been utilized [30][31][32][33] . However, most deep-learningbased studies have thus far been conducted using limited datasets and relied on manually annotated structural labels.…”