RESUMEN
BACKGROUND: Synthetic MRI (SyMRI) is a quantitative technique that allows measurements 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 manually defined ROIs (centrum semiovale, middle cerebellar peduncles, thalamus, and insular cortex) and in segmented whole-brain components (brain parenchyma, gray matter, white matter, myelin, CSF, and stromal structures) were extracted from the SyMRI segmentation 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 statistically 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 statistically 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 segmented 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.