Fast and effective single‐scan dual‐energy cone‐beam CT reconstruction and decomposition denoising based on dual‐energy vectorization

Abstract

Purpose Flat‐panel detector (FPD) based dual‐energy cone‐beam computed tomography (DE‐CBCT) is a promising imaging technique for dedicated clinical applications. In this paper, we proposed a fully analytical method for fast and effective single‐scan DE‐CBCT image reconstruction and decomposition. Methods A rotatable Mo filter was inserted between an x‐ray source and imaged object to alternately produce low and high‐energy x‐ray spectra. First, filtered‐backprojection (FBP) method was applied on down‐sampled projections to reconstruct low and high‐energy images. Then, the two images were converted into a vectorized form represented with an amplitude and an argument image. Using amplitude image as a guide, a joint bilateral filter was applied to denoise the argument image. Then, high‐quality dual‐energy images were recovered from the amplitude image and the denoised argument image. Finally, the recovered dual‐energy images were further used for low‐noise material decomposition and electron density synthesis. Imaging was conducted on a Catphan®600 phantom and an anthropomorphic head phantom. The proposed method was evaluated via comparison with the traditional two‐scan method and a commonly used filtering method (HYPR‐LR). Results On the Catphan®600 phantom, the proposed method successfully reduced streaking artifacts and preserved spatial resolution and noise‐power‐spectrum (NPS) pattern. In the electron density image, the proposed method increased contrast‐to‐noise ratio (CNR) by more than 2.5 times and achieved <1.2% error for electron density values. On the anthropomorphic head phantom, the proposed method greatly improved the soft‐tissue contrast and the fine detail differentiation ability. In the selected ROIs on different human tissues, the differences between the CT number obtained by the proposed method and that by the two‐scan method were less than 4 HU. In the material images, the proposed method suppressed noise by over 75.5% compared with two‐scan results, and by over 40.4% compared with HYPR‐LR results. Implementation of the whole algorithm took 44.5 s for volumetric imaging, including projection preprocessing, FBP reconstruction, joint bilateral filtering, and material decomposition. Conclusions Using down‐sampled projections in single‐scan DE‐CBCT, the proposed method could effectively and efficiently produce high‐quality DE‐CBCT images and low‐noise material decomposition images. This method demonstrated superior performance on spatial resolution enhancement, NPS preservation, noise reduction, and electron density accuracy, indicating better prospect in material differentiation and dose calculation.

Likelihood‐based bilateral filters for pre‐estimated basis sinograms using photon‐counting CT

“…The blue dotted square inside a liver (70 × 70; see NPS in Figure 5a; water image) has a slight intensity variation. The noise power spectrum (NPS) was calculated to assess noise texture 27,31 when the ROI (NPS) has a similar noise level. Last, the ROI3 in the liver was used to evaluate vessel structures after noise reduction using various methods.…”

“…The DE‐vec method is dedicated to the dual‐energy CT, 31 so it was applied only to the abdominal phantom study where two energy bins were used. Let the dual‐energy vectorized signal at the j th pixel is given by $$$$\begin{eqnarray} \vec{\mu }_{j} = \def\eqcellsep{&}\begin{bmatrix} \mu _{1,j} \\ \mu _{2,j} \end{bmatrix}, \end{eqnarray}$$$$where $$\mu _{b,j}$$ is the j th pixel value for the b th bin image.…”

“…Let the dual‐energy vectorized signal at the j th pixel is given by $$$$\begin{eqnarray} \vec{\mu }_{j} = \def\eqcellsep{&}\begin{bmatrix} \mu _{1,j} \\ \mu _{2,j} \end{bmatrix}, \end{eqnarray}$$$$where $$\mu _{b,j}$$ is the j th pixel value for the b th bin image. Then, the DE‐vec filter can be described as follows 31 : $$$$\begin{eqnarray} \angle {\vec{\mu }_{j}}^{DE-vec} = \mathcal {C}(j) \sum \limits _{i\in \mathcal {N}_j} \angle {\vec{\mu }_{i}} \,\, \mathcal {G}(i,j,\sigma _1) \exp {{\left\lbrace -\frac{{\left(|\vec{\mu }_{i}|-|\vec{\mu }_{j}|\right)}^2}{\sigma _2^2}\right\rbrace} },\nonumber \\ \end{eqnarray}$$$$where $$\angle {\vec{\mu }}$$ and $$|\vec{\mu }|$$ are the angle and magnitude for the vector $$\vec{\mu }$$, respectively. $$\mathcal {C}(j)$$ is the normalization factor, …”

“…Edge-preserving filtering in the image domain is another active research topic for spectral material decomposition. [27][28][29][30][31] For example,Zhao et al 28 proposed HYPR-NLM: The modified local highlY constrained backPRojection reconstruction (HYPR-LR) for spectral CT 32 by substituting the nonlocal mean (NLM) for the low-pass filtering (LPF) in HYPR-LR. The method uses the composite image as robust guidance but filters the basis images instead of bin-wise images.…”

Residual W-shape network (ResWnet) for dual-energy cone-beam CT imaging

“…Our group implemented a rotation filter [14] configuration to acquired dual-energy data within single rotation. We further proposed a joint bilateral filtering-based algorithm to suppress the image streaks and amplified decomposition noise [15]. However, photon scatter, a major issue in CBCT imaging [16], was not taken into consideration in our previous research.…”