Purpose: Energy-resolved CT has the potential to improve the contrast-to-noise ratio(CNR) through optimal weighting of photonsdetected in energy bins. In general, optimal weighting gives higher weight to the lower energy photons that contain the most contrast information. However, low-energy photons are generally most corrupted by scatter and spectrum tailing, an effect caused by the limited energy resolution of the detector. This article first quantifies the effects of spectrum tailing on energy-resolved data, which may also be beneficial for material decomposition applications. Subsequently, the combined effects of energy weighting, spectrum tailing, and scatter are investigated through simulations.
Methods: The study first investigated the effects of spectrum tailing on the estimated attenuation coefficients of homogeneous slab objects. Next, the study compared the CNR and artifact performance of images simulated with varying levels of scatter and spectrum tailing effects, and reconstructed with energy integrating, photon-counting, and two optimal linear weighting methods: Projection-based and image-based weighting. Realistic detector energy-response functions were simulated based on a previously proposed model. The energy-response functions represent the probability that a photon incident on the detector at a particular energy will be detected at a different energy. Realistic scatter was simulated with Monte Carlo methods.
Results: Spectrum tailing resulted in a negative shift in the estimated attenuation coefficient of slab objects compared to an ideal detector. The magnitude of the shift varied with material composition, increased with material thickness, and decreased with photon energy. Spectrum tailing caused cupping artifacts and CT number inaccuracies in imagesreconstructed with optimal energy weighting, and did not impact imagesreconstructed with photon counting weighting. Spectrum tailing did not significantly impact the CNR in reconstructed images.Scatter reduced the CNR for all energy-weighting methods; however, the effect was greater for optimal energy weighting. For example, optimal energy weighting improved the CNR of iodine and water compared to energy-integrating weighting by a factor of in the absence of scatter and by a factor of in the presence of scatter (8.9° cone angle, SPR 0.5). Without scatter correction, the difference in CNR resulting from photon-counting and optimal energy weighting was negligible for cone angles greater than 4.4° . Optimal weights combined with deterministic scatter correction provided a 1.3 and 1.1 improvement in CNR compared to energy-integrating and photon-counting weighting, respectively, for the 8.9° cone angle simulation. In the absence of spectrum tailing, image-based weighting demonstrated reduced cupping artifact compared to projection-based weighting; however, both weighting methods exhibited similar cupping artifacts when spectrum tailing was simulated. There were no statistically significant differences in the CNR resulting from projection and image-based weighting for any of the simulated conditions.
Conclusions: Optimal linear energy weighting introduces artifacts and CT number inaccuracies due to spectrum tailing. While optimal energy weighting has the potential to improve CNR compared to conventional weighting methods, the benefits are reduced as scatter increases. Efficient methods for reducing scatter and correcting spectrum tailing effects are required to obtain the highest benefit from optimal energy weighting.
Computer simulations were performed on the Marquette University High Performance Computing Cluster (NSF Grant No. CTS-0521602). The author would like to thank Lars E. Olson, PhD, and David Herzfeld (Marquette University) for help with the cluster and Chong Zhang (Marquette University) for assistance with the derivation in the Appendix.
II. METHODS AND MATERIALS
II.A. Theoretical considerations
II.A.1. Projection-based weighting
II.A.2. Image-based weighting
II.A.3. Spectrum tailing
II.B. Simulation studies—Spectrum tailing and the estimated attenuation coefficient
II.C. Simulation studies—Energy weighting, spectrum tailing, and scatter
II.C.1. System configuration
II.C.2. Spectrum tailing
II.C.3. Scatter simulations
II.C.4. Combined spectrum tailing/scatter simulations
II.C.5. Energy weighting
II.C.6. Image reconstruction and analysis
III.A. Spectrum tailing and the estimated attenuation coefficient
III.B. Energy weighting, spectrum tailing, and scatter
III.B.1. Spectrum tailing
III.B.2. Scatter with and without spectrum tailing
IV. DISCUSSION AND CONCLUSIONS
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