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Proceedings Paper

Calculation of brain atrophy using computed tomography and a new atrophy measurement tool
Author(s): Abdullah Bin Zahid; Artem Mikheev; Andrew Il Yang; Uzma Samadani; Henry Rusinek
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Paper Abstract

Purpose: To determine if brain atrophy can be calculated by performing volumetric analysis on conventional computed tomography (CT) scans in spite of relatively low contrast for this modality.

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.

Paper Details

Date Published: 20 March 2015
PDF: 9 pages
Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 94132S (20 March 2015); doi: 10.1117/12.2080860
Show Author Affiliations
Abdullah Bin Zahid, New York Univ. Medical Ctr. (United States)
Artem Mikheev, New York Univ. Medical Ctr. (United States)
Andrew Il Yang, New York Univ. School of Medicine (United States)
Uzma Samadani, New York Univ. Medical Ctr. (United States)
VA Harbor Healthcare System (United States)
Henry Rusinek, New York Univ. Medical Ctr. (United States)


Published in SPIE Proceedings Vol. 9413:
Medical Imaging 2015: Image Processing
Sébastien Ourselin; Martin A. Styner, Editor(s)

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