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Optimal “image‐based” weighting for energy‐resolved CT
Author(s) -
Schmidt Taly Gilat
Publication year - 2009
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.3148535
Subject(s) - weighting , energy (signal processing) , iterative reconstruction , bin , mathematics , computer science , algorithm , projection (relational algebra) , detector , artificial intelligence , computer vision , optics , physics , statistics , acoustics
This paper investigates a method of reconstructing images from energy‐resolved CT data with negligible beam‐hardening artifacts and improved contrast‐to‐nosie ratio (CNR) compared to conventional energy‐weighting methods. Conceptually, the investigated method first reconstructs separate images from each energy bin. The final image is a linear combination of the energy‐bin images, with the weights chosen to maximize the CNR in the final image. The optimal weight of a particular energy‐bin image is derived to be proportional to the contrast‐to‐noise‐variance ratio in that image. The investigated weighting method is referred to as “image‐based” weighting, although, as will be described, the weights can be calculated and the energy‐bin data combined prior to reconstruction. The performance of optimal image‐based energy weighting with respect to CNR and beam‐hardening artifacts was investigated through simulations and compared to that of energy integrating, photon counting, and previously studied optimal “projection‐based” energy weighting. Two acquisitions were simulated: dedicated breast CT and a conventional thorax scan. The energy‐resolving detector was simulated with five energy bins. Four methods of estimating the optimal weights were investigated, including task‐specific and task‐independent methods and methods that require a single reconstruction versus multiple reconstructions. Results demonstrated that optimal image‐based weighting improved the CNR compared to energy‐integrating weighting by factors of 1.15–1.6 depending on the task. Compared to photon‐counting weighting, the CNR improvement ranged from 1.0 to 1.3. The CNR improvement factors were comparable to those of projection‐based optimal energy weighting. The beam‐hardening cupping artifact increased from 5.2% for energy‐integrating weighting to 12.8% for optimal projection‐based weighting, while optimal image‐based weighting reduced the cupping to 0.6%. Overall, optimal image‐based energy weighting provides images with negligible beam‐hardening artifacts and improved CNR compared to energy‐integrating and photon‐counting methods.