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Proceedings Paper

Effects of window width and window level adjustment on detection tasks in computed tomography images
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Paper Abstract

This study experimentally evaluated the effect of window width (WW) and window level (WL) on the task based detectability index (d’). Window-level transformation is frequently performed on CT images to improve visualization in clinical arena. Numerous model observers and metrics have been used to assess CT image quality. However, objective assessment is typically performed on the reconstructed CT image without considering the WW and WL settings used by the reader. In this study, the ACR CT phantom was scanned at 120 kV and 90 mAs and images were reconstructed using filtered backprojection. The bone and acrylic contrast objects from module one of the ACR phantom were selected for calculating the effect of the WW/WL on the detectability index (d’). The d’ for each object at 90 mAs was calculated for a range of WW and WL values. The results demonstrated that the d’ values were affected by the WW and WL settings. For example, WL setting of 20 HU and window width of 150 HU resulted in a 35% decrease in d’ compared to that of the untransformed image. A WL of 20 and WW of 1400 resulted in a 33% decrease in the d’ value for the high contrast object. The investigated WW and WL settings did not improve d’ for any of the investigated objects when reconstructed with the standard kernel. The results suggest that d’ is affected by the WW/WL settings. However, because d’ does not model the contrast adaptation of the human visual system, it may not represent the change in perceived image quality with window-level transformation.

Paper Details

Date Published: 10 March 2017
PDF: 7 pages
Proc. SPIE 10136, Medical Imaging 2017: Image Perception, Observer Performance, and Technology Assessment, 101361G (10 March 2017); doi: 10.1117/12.2255575
Show Author Affiliations
P. Khobragade, Marquette Univ. (United States)
Jiahua Fan, GE Healthcare (United States)
Franco Rupcich, GE Healthcare (United States)
Dominic J. Crotty, GE Healthcare (United States)
Taly Gilat Schmidt, Marquette Univ. (United States)


Published in SPIE Proceedings Vol. 10136:
Medical Imaging 2017: Image Perception, Observer Performance, and Technology Assessment
Matthew A. Kupinski; Robert M. Nishikawa, Editor(s)

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