Details

Autor(en) / Beteiligte

• Nowinski, Wieslaw L.
• Gomolka, Ryszard S.
• Qian, Guoyu
• Gupta, Varsha
• Ullman, Natalie L.
• Hanley, Daniel F.

Titel

Characterization of Intraventricular and Intracerebral Hematomas in Non-Contrast CT

Ist Teil von

• The neuroradiology journal, 2014-06, Vol.27 (3), p.299-315

Ort / Verlag

London, England: SAGE Publications

Erscheinungsjahr

2014

Link zum Volltext

Quelle

MEDLINE

Beschreibungen/Notizen

• Characterization of hematomas is essential in scan reading, manual delineation, and designing automatic segmentation algorithms. Our purpose is to characterize the distribution of intraventricular (IVH) and intracerebral hematomas (ICH) in NCCT scans, study their relationship to gray matter (GM), and to introduce a new tool for quantitative hematoma delineation. We used 289 serial retrospective scans of 51 patients. Hematomas were manually delineated in a two-stage process. Hematoma contours generated in the first stage were quantified and enhanced in the second stage. Delineation was based on new quantitative rules and hematoma profiling, and assisted by a dedicated tool superimposing quantitative information on scans with 3D hematoma display. The tool provides: density maps (40–85HU), contrast maps (8/15HU), mean horizontal/vertical contrasts for hematoma contours, and hematoma contours below a specified mean contrast (8HU). White matter (WM) and GM were segmented automatically. IVH/ICH on serial NCCT is characterized by 59.0HU mean, 60.0HU median, 11.6HU standard deviation, 23.9HU mean contrast, 0.99HV/day slope, and 0.24 skewness (changing over time from negative to positive). Its 0.1st −99.9th percentile range corresponds to 25–88HU range. WM and GM are highly correlated (R2=0.88; p<10−10) whereas the GM-GS correlation is weak (R2=0.14; p<10−10). The intersection point of mean GM-hematoma density distributions is at 55.6±5.8HU with the corresponding GM/hematoma percentiles of 88th/40th. Objective characterization of IVH/ICH and stating the rules quantitatively will aid raters to delineate hematomas more robustly and facilitate designing algorithms for automatic hematoma segmentation. Our two-stage process is general and potentially applicable to delineate other pathologies on various modalities more robustly and quantitatively.