3 March 2017 Automated method to compute Evans index for diagnosis of idiopathic normal pressure hydrocephalus on brain CT images
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The early diagnosis of idiopathic normal pressure hydrocephalus (iNPH) considered as a treatable dementia is important. The iNPH causes enlargement of lateral ventricles (LVs). The degree of the enlargement of the LVs on CT or MR images is evaluated by using a diagnostic imaging criterion, Evans index. Evans index is defined as the ratio of the maximal width of frontal horns (FH) of the LVs to the maximal width of the inner skull (IS). Evans index is the most commonly used parameter for the evaluation of ventricular enlargement. However, manual measurement of Evans index is a time-consuming process. In this study, we present an automated method to compute Evans index on brain CT images. The algorithm of the method consisted of five major steps: standardization of CT data to an atlas, extraction of FH and IS regions, the search for the outmost points of bilateral FH regions, determination of the maximal widths of both the FH and the IS, and calculation of Evans index. The standardization to the atlas was performed by using linear affine transformation and non-linear wrapping techniques. The FH regions were segmented by using a three dimensional region growing technique. This scheme was applied to CT scans from 44 subjects, including 13 iNPH patients. The average difference in Evans index between the proposed method and manual measurement was 0.01 (1.6%), and the correlation coefficient of these data for the Evans index was 0.98. Therefore, this computerized method may have the potential to accurately compute Evans index for the diagnosis of iNPH on CT images.
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Noriyuki Takahashi, Noriyuki Takahashi, Toshibumi Kinoshita, Toshibumi Kinoshita, Tomomi Ohmura, Tomomi Ohmura, Eri Matsuyama, Eri Matsuyama, Hideto Toyoshima, Hideto Toyoshima, } "Automated method to compute Evans index for diagnosis of idiopathic normal pressure hydrocephalus on brain CT images", Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101342C (3 March 2017); doi: 10.1117/12.2251322; https://doi.org/10.1117/12.2251322

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