Multi-Material Blind Beam Hardening Correction Based on Non-Linearity Adjustment of Projections
Beam hardening (BH) is one of the major artifacts that severely reduces the quality of Computed Tomography (CT) imaging. In a polychromatic X-ray beam, since low-energy photons are more preferentially absorbed, the attenuation of the beam is no longer a linear function of the absorber thickness. The existing BH correction methods either require a given material, which might be unfeasible in reality, or they require a long computation time. This work aims to propose a fast and accurate BH correction method that requires no prior knowledge of the materials and corrects first and higher-order BH artifacts. In the first step, a wide sweep of the material is performed based on an experimentally measured look-up table to obtain the closest estimate of the material. Then the non-linearity effect of the BH is corrected by adding the difference between the estimated monochromatic and the polychromatic simulated projections of the segmented image. The estimated monochromatic projection is simulated by selecting the energy from the polychromatic spectrum which produces the lowest mean square error (MSE) with the acquired projection from the scanner. The polychromatic projection is estimated by minimizing the difference between the acquired projection and the weighted sum of the simulated polychromatic projections using different spectra of different filtration. To evaluate the proposed BH correction method, we have conducted extensive experiments on the real-world CT data. Compared to the state-of-the-art empirical BH correction method, the experiments show that the proposed method can highly reduce the BH artifacts without prior knowledge of the materials.
Code (0)
등록된 구현이 없습니다.
Tasks
Computed Tomography (CT)Similar Papers 제목 키워드 기반
CT sinogram-consistency learning for metal-induced beam hardening correction
This paper proposes a sinogram consistency learning method to deal with beam-hardening related artifacts in polychromatic computerized tomography (CT). The presence of highly attenuating materials in the scan field cause…
CT ReconstructionSplat-based Metal Artifact Reduction in Cone-Beam CT via Polychromatic Modeling
Cone-beam computed tomography (CBCT) enables volumetric reconstruction from X-ray projections, but suffers from severe artifacts--especially beam hardening--when imaging materials with high attenuation such as metals. Th…
X-ray Photon-Counting Data Correction through Deep Learning
X-ray photon-counting detectors (PCDs) are drawing an increasing attention in recent years due to their low noise and energy discrimination capabilities. The energy/spectral dimension associated with PCDs potentially bri…
Deep LearningMetal Artifact ReductionBlock Matching Frame based Material Reconstruction for Spectral CT
Spectral computed tomography (CT) has a great potential in material identification and decomposition. To achieve high-quality material composition images and further suppress the x-ray beam hardening artifacts, we first …
Computed Tomography (CT)Plug-and-Play with 2.5D Artifact Reduction Prior for Fast and Accurate Industrial Computed Tomography Reconstruction
Cone-beam X-ray computed tomography (XCT) is an essential imaging technique for generating 3D reconstructions of internal structures, with applications ranging from medical to industrial imaging. Producing high-quality r…
3D ReconstructionDefect Detection