Study of Brain Geriatric Consequences in Sudan using CT, MRI

Abstract

The aim of this study was assessment of geriatric brain consequences in Sudan by using computer tomography and magnetic resonance image. The methodology was based on measurement of brain ventricles, cranialvolume, Hounsfield unit, signal intensity of gray and white matters. The results showed that: aging shows less significant (R2 =0.4) impact on ventricle volume generally (due to gender factor) and the correlation best fitted to equation: y = 1.4588x - 40.742, where x refers to age in years and y refers to ventricle volume in cm3. The impact of aging in ventricles volume for male and female shows significant (p = 0.05) increment in ventricle volume after 69 years with prominent effect among male; while before the age of 69 years old the impact on volume was so steady. Aging was less significant (R2 =0.4) impact on ventricle/cranium volume ratio generally and the correlation has been increases following the aging increment that could be best fitted to equation: y = 0.0005x - 0.0139, where x refers to age in years and y refers to ventricle/cranium volume ratio. The aging showed less significant impact (R2 = 0.3) in signal intensity (T1) of white and gray matter which are in decreasing proportionality with aging and having prominent high signal intensity of white mater relative to gray mater. The correlation between ageing and signal intensity for white/gray matter at (T1) could be best fitted to equation y = 0.9337x + 831.09 (white matter) and y=1.2823x +799.03 (gray matter), where x refers to age in years and y refers to signal intensity of (T1). A reduced signal intensity has been noticed at (T2) following aging for white and gray matter have with correlation could be fitted to equations of the form y = -6.6489x + 1278.2 (white matter) and y = -4.7937x + 1028.4 (gray matter). In the correlation between age in years and signal intensity (T1, T2) for white matter in; there is decreasing proportional correlation between aging and signal intensity (T1, T2) for white matter with prominent signal of T1 relative to T2. The relevant correlation could be fitted to equations: y = -3.758x + 1035.7 (white matter at T1) and the other is y = -4.7937x + 1028.4 (white matter at T2) with high significant correlation (R2 = 0.9). same correlation has been noticed in the correlation between age in years and signal intensity (T1, T2) for gray matter; with only shifting of signal intensity of gray matter at T2 to higher value relative to T1. In the correlation between age in year and the HU for white and gray matter; the age showed high significant (R2 = 0.8) reducing impact in white matter HU that fitted to equations of the following forms: y = 0.5274x + 9.6864; while there is an increasing impact in gray matter HU that fitted to equation: y = -0.2618x + 40.093, where x refers to age in year and y refers to HU for relative white and gray matter. In the correlation between HU (CT parameter) and signal intensity (MRI parameter) of white and gray matter, It is obviously noticed that: the HU influencing the signal intensity significantly (R2 = 0.7) as increasing correlation fitted to equation: y = 14.121x + 385.94, and as a reduction significant (R2 = 0.8) impact in gray matter that could be fitted the equation of the following form: y = -10.614x + 1307.9, where x refers to HU and y refers to signal intensity for white and gray matter.