Increased Resolution of Structural MRI at 3T Improves Estimation of Regional Cortical Degeneration in Individual Dementia Patients Using Surface Thickness Maps

Abstract

Background: Objective measurement of regional cortical atrophy in individual patients would be a highly desirable adjunct for diagnosis of degenerative dementias. Objective: We hypothesized that increasing the resolution of magnetic resonance scans would improve the sensitivity of cortical atrophy detection for individual patients. Methods: 46 participants including 8 semantic-variant primary progressive aphasia (svPPA), seven posterior cortical atrophy (PCA), and 31 cognitively unimpaired participants underwent clinical assessment and 3.0T brain scans. SvPPA and PCA were chosen because there is overwhelming prior knowledge of the expected atrophy pattern. Two sets of T1-weighted images with 0.8 mm3 (HighRes) and conventional 1.0 mm3 (ConvRes) resolution were acquired. The cortical ribbon was segmented using FreeSurfer software to obtain surface-based thickness maps. Inter-sequence performance was assessed in terms of cortical thickness and sub-cortical volume reproducibility, signal-to-noise and contrast-to-noise ratios. For clinical cases, diagnostic effect size (Cohen’s d) and lesion distribution (z-score and t-value maps) were compared between HighRes and ConvRes scans. Results: The HighRes scans produced higher image quality scores at 90 seconds extra scan time. The effect size of cortical thickness differences between patients and cognitively unimpaired participants was 15–20% larger for HighRes scans. HighRes scans showed more robust patterns of atrophy in expected regions in each and every individual patient. Conclusions: HighRes T1-weighted scans showed superior precision for identifying the severity of cortical atrophy in individual patients, offering a proof-of-concept for clinical translation. Studying svPPA and PCA, two syndromes with well-defined focal atrophy patterns, offers a method to clinically validate and contrast automated algorithms.