Image Quality and Dose Reduction Evaluation of a New CT Iterative Reconstruction Algorithm Using Model Observers
M Kupinski1*, H Tseng2, J Fan3, P Sainath4, J Hsieh5, (1), (2), 3) GE Healthcare, Waukesha, WI, (4) GE Healthcare, Waukesha, WI, (5) GE Healthcare Technologies, Brookfield, WITU-C-103-5 Tuesday 10:30AM - 12:30PM Room: 103
To evaluate the impact on image quality of a new iterative reconstruction (IR) algorithm for CT systems and quantitate the amount of dose reduction by using mathematical model observers.
Two phantoms with low-contrast inserts (head mode and body mode) were used to evaluate task performance for both location known signal detection and location unknown signal detection tasks. The phantom consisted of low contrast cylindrical signals with contrasts ranging from -2 Hounsfield units (HU) to -10 HU and sizes ranging from 5mm to 7mm. The phantoms were scanned by multi row CT scanner using a range of tube currents 30-400mAs, and energy 120 kVp. The performance of a traditional filtered backprojection (FBP) algorithm and the new IR were evaluated using the channelized Hotelling observer. Seven channels designed to match human performance were selected to evaluate the image quality. Area under ROC curves and LROC curves were used to determine the image quality.
Evaluation of model observer performances indicates that a 3x dose reduction can be reached using the new IR algorithm in two modes (head and body) while maintaining image quality for location known and location unknown detection tasks.
Overall, image quality as measured by task performance is greatly improved by using the IR compared to traditional FBP at equal mAs and kVp. Thus, this study indicates that a significant dose reduction in CT systems can be achieved by using this new IR algorithm. This new IR and the phantoms presented in this study will be used in future investigations of more complicated imaging tasks such as size and contrast estimation.
Funding Support, Disclosures, and Conflict of Interest: GE Healthcare, Waukesha, WI
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