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Materials & Method: CTs for 73 patients from the local Veteran Affairs database were selected. Exclusion criteria: AD, NPH, tumor, and alcohol abuse. Protocol: conventional clinical acquisition (Toshiba; helical, 120 kVp, X-ray tube current 300mA, slice thickness 3-5mm). Locally developed, automatic algorithm was used to segment intracranial cavity (ICC) using (a) white matter seed (b) constrained growth, limited by inner skull layer and (c) topological connectivity. ICC was further segmented into CSF and brain parenchyma using a threshold of 16 Hu. Results: Age distribution: 25–95yrs; (Mean 67±17.5yrs.). Significant correlation was found between age and CSF/ICC(r=0.695, p<0.01 2-tailed). A quadratic model (y=0.06–0.001x+2.56x10-5x2 ; where y=CSF/ICC and x=age) was a better fit to data (r=0.716, p < 0.01). This is in agreement with MRI literature. For example, Smith et al. found annual CSF/ICC increase in 58 – 94.5 y.o. individuals to be 0.2%/year, whereas our data, restricted to the same age group yield 0.3%/year(0.2–0.4%/yrs. 95%C.I.). Slightly increased atrophy among elderly VA patients is attributable to the presence of other comorbidities. Conclusion: Brain atrophy can be reliably calculated using automated software and conventional CT. Compared to MRI, CT is more widely available, cheaper, and less affected by head motion due to ~100 times shorter scan time. Work is in progress to improve the precision of the measurements, possibly leading to assessment of longitudinal changes within the patient. |
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CITATIONS
Cited by 2 patents.
Brain
Computed tomography
Neuroimaging
Image segmentation
Magnetic resonance imaging
Algorithm development
Tissues
