Feasibility of dual‐energy CBCT material decomposition in the human torso with 2D anti‐scatter grids and grid‐based scatter sampling

Altunbas, Cem

doi:10.1002/mp.16611

Cited by 1 publication

(1 citation statement)

References 41 publications

(136 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compared with traditional computed tomography (CT), DECT can obtain quantitative properties of different materials, which can be exploited by using the attenuation information under different spectra called material decomposition (MD) ( 4 ). However, it is difficult to obtain accurate material maps due to noise ( 5 ), scatter ( 6 ), beam-hardening ( 7 ), and so on ( 8 ).…”

Section: Introductionmentioning

confidence: 99%

MISD-IR: material-image subspace decomposition-based iterative reconstruction with spectrum estimation for dual-energy computed tomography

Ren,

Wang,

Cai

et al. 2024

Quant Imaging Med Surg

View full text Add to dashboard Cite

Background Dual-energy computed tomography (DECT) is a promising technique, which can provide unique capability for material quantification. The iterative reconstruction of material maps requires spectral information and its accuracy is affected by spectral mismatch. Simultaneously estimating the spectra and reconstructing material maps avoids extra workload on spectrum estimation and the negative impact of spectral mismatch. However, existing methods are not satisfactory in image detail preservation, edge retention, and convergence rate. The purpose of this paper was to mine the similarity between the reconstructed images and the material images to improve the imaging quality, and to design an effective iteration strategy to improve the convergence efficiency. Methods The material-image subspace decomposition-based iterative reconstruction (MISD-IR) with spectrum estimation was proposed for DECT. MISD-IR is an optimized model combining spectral estimation and material reconstruction with fast convergence speed and promising noise suppression capability. We proposed to reconstruct the material maps based on extended simultaneous algebraic reconstruction techniques and estimation of the spectrum with model spectral. To stabilize the iteration and alleviate the influence of errors, we introduced a weighted proximal operator based on the block coordinate descending algorithm (WP-BCD). Furthermore, the reconstructed computed tomography (CT) images were introduced to suppress the noise based on subspace decomposition, which relies on non-local regularization to prevent noise accumulation. Results In numerical experiments, the results of MISD-IR were closer to the ground truth compared with other methods. In real scanning data experiments, the results of MISD-IR showed sharper edges and details. Compared with other one-step iterative methods in the experiment, the running time of MISD-IR was reduced by 75%. Conclusions The proposed MISD-IR can achieve accurate material decomposition (MD) without known energy spectrum in advance, and has good retention of image edges and details. Compared with other one-step iterative methods, it has high convergence efficiency.

show abstract