Quantitative brain relaxation atlases for personalized detection and characterization of brain pathology

Abstract

Purpose To exploit the improved comparability and hardware independency of quantitative MRI, databases of MR physical parameters in healthy tissue are required, to which tissue properties of patients can be compared. In this work, normative values for longitudinal and transverse relaxation times in the brain were established and tested in single‐subject comparisons for detection of abnormal relaxation times. Methods Relaxometry maps of the brain were acquired from 52 healthy volunteers. After spatially normalizing the volumes into a common space, T1 and T2 inter‐subject variability within the healthy cohort was modeled voxel‐wise. A method for a single‐subject comparison against the atlases was developed by computing z‐scores with respect to the established healthy norms. The comparison was applied to two multiple sclerosis and one clinically isolated syndrome cases for a proof of concept. Results The established atlases exhibit a low variation in white matter structures (median RMSE of models equal to 32 ms for T1 and 4 ms for T2), indicating that relaxation times are in a narrow range for normal tissues. The proposed method for single‐subject comparison detected relaxation time deviations from healthy norms in the example patient data sets. Relaxation times were found to be increased in brain lesions (mean z‐scores >5). Moreover, subtle and confluent differences (z‐scores ~2–4) were observed in clinically plausible regions (between lesions, corpus callosum). Conclusions Brain T1 and T2 quantitative norms were derived voxel‐wise with low variability in healthy tissue. Example patient deviation maps demonstrated good sensitivity of the atlases for detecting relaxation time alterations.

Silent myelin-weighted magnetic resonance imaging

“…However, the average within-subject CoV was lower than the between-subject CoV, indicating that partial volume effects and registration quality affected the between-subject figure. These issues are not unique to ihMTR but affect all quantitative MRI measures 66 , 67 .…”

Periventricular gradient of T1 tissue alterations in multiple sclerosis

“…A reference T 1 atlas of healthy tissue was established by linear, voxel-wise modelling of the T 1 inter-subject variability, including age and sex as covariates: with being the expected value, being the model intercept, and sex a categorical variable equal to 1 if the subject is male or 0 if female ( Fig. 1 ) as detailed in Piredda et al (2020 ).

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An atlas for human brain myelin content throughout the adult life span

“…GRASE echo 1 voxel intensities were squared to accentuate tissue contrast, which improved brain extractions and intra-subject registrations. This process was motivated by previous studies that manipulated image intensities for similar purposes 8 .…”

“…Combining datasets reduces noise from individual measurements and from biological variation between subjects, which can arise from age, sex, ethnicity, pathology, hemispheric asymmetry, or other factors. If demographic factors can sufficiently characterize biological variations, then atlases could be used to evaluate quantitative MRI metrics on an individual basis 8 . Voxel-wise analysis of individual MWF maps has been used to study brain and spinal cord in a variety of diseases, but with relatively small sample sizes and limited characterization of possible demographic factor influences 9,10 .…”