Noise suppression for dual-energy CT via penalized weighted least-square optimization with similarity-based regularization

Abstract

Purpose: Dual-energy CT (DECT) expands applications of CT imaging in its capability to decompose CT images into material images. However, decomposition via direct matrix inversion leads to large noise amplification and limits quantitative use of DECT. Their group has previously developed a noise suppression algorithm via penalized weighted least-square optimization with edge-preservation regularization (PWLS-EPR). In this paper, the authors improve method performance using the same framework of penalized weighted least-square optimization but with similarity-based regularization (PWLS-SBR), which substantially enhances the quality of decomposed images by retaining a more uniform noise power spectrum (NPS). Methods: The design of PWLS-SBR is based on the fact that averaging pixels of similar materials gives a low-noise image. For each pixel, the authors calculate the similarity to other pixels in its neighborhood by comparing CT values. Using an empirical Gaussian model, the authors assign high/low similarity value to one neighboring pixel if its CT value is close/far to the CT value of the pixel of interest. These similarity values are organized in matrix form, such that multiplication of the similarity matrix to the image vector reduces image noise. The similarity matrices are calculated on both high-and low-energy CT images and averaged. In PWLS-SBR, the authors include a regularization term to minimize the L-2 norm of the difference between the images without and with noise suppression via similarity matrix multiplication. By using all pixel information of the initial CT images rather than just those lying on or near edges, PWLS-SBR is superior to the previously developed PWLS-EPR, as supported by comparison studies on phantoms and a head-and-neck patient. Results: On the line-pair slice of the Catphan C 600 phantom, PWLS-SBR outperforms PWLS-EPR and retains spatial resolution of 8 lp/cm, comparable to the original CT images, even at 90% reduction in noise standard deviation (STD). Similar performance on spatial resolution is observed on an anthropomorphic head phantom. In addition, results of PWLS-SBR show substantially improved image quality due to preservation of image NPS. On the Catphan C 600 phantom, NPS using PWLS-SBR has a correlation of 93% with that via direct matrix inversion, while the correlation drops to −52% for PWLS-EPR. Electron density measurement studies indicate high accuracy of PWLS-SBR. On seven different materials, the measured electron densities calculated from the decomposed material images using PWLS-SBR have a root-mean-square error (RMSE) of 1.20%, while the results of PWLS-EPR have a RMSE of 2.21%. In the study on a head-and-neck patient, PWLS-SBR is shown to reduce noise STD by a factor of 3 on material images with image qualities comparable to CT images, whereas fine structures are lost in the PWLS-EPR result. Additionally, PWLS-SBR better preserves low contrast on the tissue image. Conclusions: The authors propose improvements to the regularization term of an optimization frame...

Image domain dual material decomposition for dual‐energy CT using butterfly network

“…17 Unlike the projection domain methods, image domain-based decomposition first reconstructs dual-energy images individually or jointly from two measured datasets, subsequently forming the basis images by a linear combination of the reconstructed images. 18 It is more convenient to directly apply on the DECT images acquired from commercial CT scanner compared with the other two methods. 19,20 Noise magnification in decomposed material images from noise correlation in high-and low-energy CT images is a common problem encountered in the current DECT.…”

Deep learning-based relative stopping power mapping generation with cone-beam CT in proton radiation therapy

“…Recently, dual energy CT (DECT) has been introduced to radiation therapy simulation for its ability in providing material specific information by differentiating the energy dependence of photoelectric and Compton interactions of different materials [8,9,30,31]. Parametric maps, such as RSP, electron density, effective atomic number and mean excitation energy (I), can be derived from DECT images in a voxel-wise manner using physical equations [38,40].…”

“…Meanwhile, Twin-beam DECT (TBCT) has been introduced into radiation therapy simulation for its good temporal coherence, full field-of-view, and low hardware complexity and cost, while it has poorer energy spectra separation when compared with other DECT modalities [6,19,32]. The strong overlapping of energy spectra of linear attenuation coefficient among different materials would lead to significant noise magnification from the acquired projection to the results of material differentiation [9,20,22,23]. The physical derivation does not accommodate these non-idealities, and would magnify the noise and artifacts on the DECT images to the derived parametric maps that directly lead to uncertainty and inaccuracy.…”

Improved Material Decomposition With a Two-Step Regularization for Spectral CT

“…For example, Niu et al first proposed an iterative full variance-covariance matrix of material components based material decomposition method by introducing the quadratic smoothness penalty function to constrain the feasibility of material images [42]. Then, nonlocal mean [40], spectral diffusion [43], nonlinear decomposition [44], similarity-based regularization (PWLS-SBR) [45], entropy minimization [46], data-driven sparsity [47], multiscale penalized weighted least-squares [48], fully convolutional network [49] and PWLS-TNV- [50] were further proposed. However, these methods are mainly developed for dual energy CT rather than spectral CT. Usually, there are only two basis materials within the imaging object for dual energy CT.…”