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  4. Experimental Assessment of Dual-Energy CT Estimation Bias Reduction with Joint Statistical Image Reconstruction in Sinogram Domain-Based Material Decomposition

Experimental Assessment of Dual-Energy CT Estimation Bias Reduction with Joint Statistical Image Reconstruction in Sinogram Domain-Based Material Decomposition

J Lu1 , S Zhang2 , J Williamson3 , T Webb4 , D Politte5 , B Whiting6 , J O'Sullivan7*, (1) Washington University, St. Louis, MO, (2) Washington University, St. Louis, MO,(3) Washington University, St. Louis, MO, (4) Washington University, St. Louis, MO, (5) Washington University, St. Louis, MO,(6) University of Pittsburgh, Pittsburgh, PA, (7) Washington University, St. Louis, MO

Presentations

(Sunday, 7/14/2019) 5:00 PM - 6:00 PM

Room: 221CD

Purpose: Sinogram-domain material decomposition (SDMD) is widely assumed to support more accurate multispectral CT imaging because it models beam hardening. We demonstrate that SDMD-based reconstruction from dual-energy CT sinograms results in biased monoenergetic CT images whereas statistical iterative reconstruction (SIR) results in unbiased images. The SDMD bias exhibits a systematic inverse dependence on imaging dose.

Methods: A 215 mm cylindrical water phantom containing samples of water, propanol, ethanol, and butanol, was fabricated and imaged on a Philips Big Bore CT scanner with 90 and 140 kVp known spectra. Raw transmission sinograms, with the vendor’s beam-hardening corrections disabled, were exported. The (90/140 kVp) scans were performed at four dose levels: high (400 mAs/200 mAs), medium (200 /100), low (50 /25), and ultra-low (15/15). Monoenergetic CT numbers as a function of energy were estimated by a SIR engine (JSIR) operating jointly on the dual-energy transmission sinograms, which directly reconstructs the basis-material images, and filtered back projection of the SDMD basis-material sinograms. Both methods assumed a two-component basis-vector model of linear attenuation coefficients (LAC).

Results: Both high- and medium-dose scans resulted in bias less than 1% relative error for both JSIR and SDMD estimation. However, the low- and ultralow- dose scans, SDMD reconstruction revealed bias as large as 6% and 15%, respectively, compared to 1% and 2.5% for JSIR. Stochastic and simplified analytical simulations reveal that SDMD errors are not due to random noise amplification but arise from the fact that the log of the mean is a biased estimator of mean of logarithmically transformed noisy data.

Conclusion: Our work demonstrates that the SDMD reconstruction leads to inherently biased estimates of radiological quantities, and that these errors can be large in practical low-dose scans. In contrast, JSIR, which models the underlying nonlinear signal formation process, is immune to this class of systematic uncertainties.

Funding Support, Disclosures, and Conflict of Interest: This study was supported by NIH R01 CA 149305, NIH R01 CA 212638, and NIH 5T32EB01485505.

Keywords

Dual-energy Imaging, CT, Dose

Taxonomy

IM- CT: Dual Energy and Spectral

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