Background:
Acquiring brain magnetic resonance images (MRI) with sufficient tissue contrast is important for supporting diagnosis and follow-up of MS patients. Qualitymetrix, a software performing automated quality assessment of brain MRI scans, provides an overview of a scanners performance and quality, including image contrast scores, to help reveal suboptimal protocols and monitor scan consistency over time.
Objectives:
To explore relations between slice thickness/gap parameters and tissue contrast in a real-world MRI dataset of MS patients using qualitymetrix.
To explore relations between tissue contrast and test-retest volumetric measurement differences in a test-retest MRI dataset of MS patients.
Methods:
The real-world dataset consisted of 140 sessions from 83 patients, acquired with 58 different scanners. There were 66 T1-weighted scans without contrast and 128 FLAIR scans. Slice thickness varied between 0.5mm and 5mm and slice gap between -1mm (overlapping) and 2mm. Tissue contrast scores, defined as the gray matter (GM) to white matter (WM) contrast-to-noise ratio (CNR) for T1 images, and lesion to normal-appearing WM CNR for FLAIR images, were computed with qualitymetrix.
An independent test-retest dataset (3D T1 without contrast and 3D FLAIR from 10 MS patients on 3 different scanners, comprising 27 intra-scanner and 53 inter-scanner test-retest pairs) was used to assess the relation between CNR and test-retest differences in brain volumes (WM, GM and FLAIR lesions) computed with icobrain.
Results:
Significant negative correlations were found between CNR and slice thickness and gap (correlation coefficients between -0.68 and -0.37), indicating that larger slice thickness or slice gap have a detrimental effect on image contrast.
Significant negative correlations were also found between the test-retest average CNR and the test-retest absolute volume difference (strongest correlation: -0.51), indicating that reliability of automated brain volumetry decreases with lower contrast. On the other hand, positive correlations were found between absolute CNR difference and absolute volume difference in test-retest pairs (strongest correlation: 0.74), indicating the importance of contrast consistency over time.
Conclusions:
Contrast-to-noise in brain MRI is an objective quality measure that is negatively influenced by larger slice thickness or gap in MR images. Good and consistent image contrast is also associated with higher reliability of automated brain volumetry in MS.
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