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On the Performance of Four Dual Energy CT Formalisms for Extracting Proton Stopping Powers

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E Baer

E Baer1*, A Lalonde2 , G Royle1 , H Bouchard2 , (1) University College London, London, England, (2) University of Montreal, Montreal, QC,


SU-F-J-195 (Sunday, July 31, 2016) 3:00 PM - 6:00 PM Room: Exhibit Hall

Purpose: Dual energy CT can predict stopping power ratios (SPR) for ion therapy treatment planning. Several approaches have been proposed recently, however accuracy and practicability in a clinical workflow are unaddressed. The aim of this work is to provide a fair comparison of available approaches in a human-like phantom to find the optimal method for tissue characterization in a clinical situation.

Methods: The SPR determination accuracy is investigated using simulated DECT images. A virtual human-like phantom is created containing 14 different standard human tissues. SECT (120 kV) and DECT images (100 kV and 140 kV Sn) are simulated using the software ImaSim. The single energy CT (SECT) stoichiometric calibration method and four recently published calibration-based DECT methods are implemented and used to predict the SPRs from simulated images. The difference between SPR predictions and theoretical SPR are compared pixelwize. Mean, standard deviation and skewness of the SPR difference distributions are used as measures for bias, dispersion and symmetry.

Results: The average SPR differences and standard deviations are (0.22 ± 1.27)% for SECT, and A) (-0.26 ± 1.30)%, B) (0.08 ± 1.12)%, C) (0.06 ± 1.15)% and D) (-0.05 ± 1.05)% for the four DECT methods. While SPR prediction using SECT is showing a systematic error on SPR, the DECT methods B, C and D are unbiased. The skewness of the SECT distribution is 0.57%, and A) -0.19%, B) -0.56%, C) -0.29% and D) -0.07% for DECT methods respectively.

Conclusion: The here presented DECT methods B, C and D outperform the commonly used SECT stoichiometric calibration. These methods predict SPR accurately without a bias and within ± 1.2% (68th percentile). This indicates that DECT potentially improves accuracy of range predictions in proton therapy. A validation of these findings using clinical CT images of real tissues is necessary.

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