Background: Synthetic MRI (SyMRI) is a quantitative technique that allows measure- ments of T1 and T2 relaxation times (RTs). Brain RT evolution across lifespan is well described for the younger population. The aim was to study RTs of brain parenchyma in a healthy geriatric population in order to define the normal value of structures in this group population. Normal values for geriatric population could help find biomarker for age-related brain disease.

Materials and methods: Fifty-four normal-functioning individuals (22 females, 32 males) with mean age of 83 years (range 56–98) underwent SyMRI. RT values in manu- ally defined ROIs (centrum semiovale, middle cerebellar peduncles, thalamus, and insu- lar cortex) and in segmented whole-brain components (brain parenchyma, gray matter, white matter, myelin, CSF, and stromal structures) were extracted from the SyMRI seg- mentation software. Patients’ results were combined into the group age. Main ROI- based and whole-brain results were compared for the all dataset and for age group results as well.

Results: For white matter, RTs between ROI-based analyses and whole-brain results for T2 and for T1 were statistically different and a trend of increasing T1 in centrum semiovale and cerebellar peduncle was observed. For gray matter, thalamic T1 was sta- tistically different from insular T1. A difference was also found between left and right insula (p < .0001). T1 RTs of ROI-based and whole-brain-based analyses were statis- tically different (p < .0001). No significant difference in T1 and T2 was found between age groups on ROI-based analysis, but T1 in centrum semiovale and thalamus increased with age. No statistical difference between age groups was found for the various seg- mented volumes except for myelin between 65–74 years of age and the 95–105 years of age groups (p = .038).

Conclusions: SyMRI is a new tool that allows faster imaging and permits to obtain quantitative T1 and T2. By defining RT values of different brain components of normal-functioning elderly individuals, this technique may be used as a biomarker for clinical disorders like dementia.