(Sunday, 7/12/2020) [Eastern Time (GMT-4)]
Room: AAPM ePoster Library
Purpose:
Image quality estimation in CT is crucial for technology assessment, procedure optimization, and overall radiological benefit evaluation, with noise magnitude playing a key role. Over the years, several methods have been proposed to estimate noise surrogates in vivo. The most accurate approach is to assess ensemble noise by scanning a patient multiple times and sampling each pixel noise within the ensemble of images, an ethically undoable repeated imaging process. Such impasse can be surmounted using Virtual Imaging Trials (VITs) that use computer-based simulations to simulate clinically realistic scenarios. The purpose of this study was to compare two different noise magnitude estimation methods with the ensemble noise measured in a VIT population.
Methods:
This study included a set of 47 XCAT-phantom repeated chest exams acquired virtually using a scanner-specific simulator (DukeSim) modeling a commercial scanner geometry, reconstructed with FBP and IR algorithms. Noise magnitudes were calculated in soft tissues (GNI) and air surrounding the patient (AIRn), applying [-300,100]HU and HU<-900 thresholds, respectively. Furthermore, for each pixel in GNI threshold, the ensemble noise magnitudes in soft tissues (En) were calculated across images. Noise magnitudes from different methods were compared in terms of percentage difference with correspondent En median values.
Results:
For FBP reconstructed images, median En was 30.6 HU; median GNI was 40.1 HU (+31%) and median AIRn was 25.1 HU (-18%). For IR images, median En was 19.5 HU; median GNI was 25.1 HU (+29%) and median AIRn was 18.8 HU (-4%).
Conclusion:
Compared to ensemble noise, GNI overestimates the tissue noise by about 30%, while AIRn underestimates it by 4 to 18%, depending on the reconstruction used. These differences may be applied as adjustment or calibration factors to the related noise estimation methods to most closely represent clinical results. However, air noise cannot be assumed to represent tissue noise.
Keywords
Taxonomy
IM- CT: Quality Control and Image Quality Assessment
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