Published in:
01-02-2004 | Experimental
Low-contrast detectability in volume rendering: a phantom study on multidector-row spiral CT data
Authors:
Hoen-Oh Shin, Christian V. Falck, Michael Galanski
Published in:
European Radiology
|
Issue 2/2004
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Abstract
To cope with the increasing amount of CT data, there is growing interest in direct volume-rendering techniques (VRT) as a diagnostic tool. The aim of this phantom study was to analyze the low-contrast detectability (LCD) of VRT compared with multi-planar reformations (MPR). Soft tissue lesions were simulated by spheres of different diameters (3–8 mm). The average lesion density was 15 HU compared with a background density of 35 HU. Two different CT protocols with 40 and 150 mAs were performed on a multi-detector row CT. The scanning parameters were as following: 140 kV; 2×0.5-mm slice collimation; pitch 2 (table movement per rotation/single slice collimation), and reconstruction with 0.5-mm slice thickness at 0.5-mm interval. A B30 kernel was used for reconstruction. The VRT was performed by mapping Hounsfield values to gray levels equal to a CT window (center: 60 HU; window: 370 HU ). A linear ramp was applied for the opacity transfer function varying the maximum opacity between 0.1 and 1.0. A statistical method based on the Rose model was used to calculate the detection threshold depending on lesion size and image noise. Additionally, clinical data of 2 patients with three liver lesions of different sizes and density were evaluated. In VRT, LCD was most dependent on object size. Regarding lesions larger than 5 mm, VRT is significantly superior to MPR (p<0.05) for all opacity settings. In lesions sized 3–5 mm a maximum opacity level approximately 40–50% showed a near equivalent detectability in VRT and MPR. For higher opacity levels VRT was superior to MPR. Only for 3-mm lesions MPR performed slightly better in low-contrast detectability (p<0.05). Compared with MPR, VRT shows similar performance in LCD. Due to noise suppression effects, it is suited for visualization of data with high noise content.