Image-spectral decomposition extended-learning assisted by sparsity for multi-energy computed tomography reconstruction

Abstract

Background: Multi-energy computed tomography (CT) provides multiple channel-wise reconstructed images, and they can be used for material identification and k-edge imaging. Nonetheless, the projection datasets are frequently corrupted by various noises (e.g., electronic, Poisson) in the acquisition process, resulting in lower signal-noise-ratio (SNR) measurements. Multi-energy CT images have local sparsity, nonlocal self-similarity in spatial dimension, and correlation in spectral dimension. Methods:In this paper, we propose an image-spectral decomposition extended-learning assisted by sparsity (IDEAS) method to fully exploit these intrinsic priors for multi-energy CT image reconstruction.Particularly, a nonlocal low-rank Tucker decomposition (TD) is employed to utilize the correlation and nonlocal self-similarity priors. Moreover, considering the advantages of multi-task tensor dictionary learning (TDL) in sparse representation, an adaptive spatial dictionary and an adaptive spectral dictionary are trained during the iterative reconstruction process. Furthermore, a weighted total variation (TV) regularization term is employed to encourage local sparsity.Results: Numerical simulation, physical phantom, and preclinical mouse experiments are performed to validate the proposed IDEAS algorithm. Specifically, in the simulation experiments, the proposed IDEAS reconstructed high-quality images that are very close to the references. For example, the root mean square error (RMSE) of IDEAS image in energy bin 1 is as low as 0.0672, while the RMSE of other methods are higher than 0.0843. Besides, the structural similarity (SSIM) of IDEAS reconstructed image in energy bin 1 is greater than 0.98. For material decomposition, the RMSE of IDEAS bone component is as low as 0.0152, and other methods are higher than 0.0199. In addition, the computational cost of IDEAS is as low as 98.8 s for one iteration, and the competing tensor decomposition method is higher than 327 s. Conclusions:To further improve the quality of the reconstructed multi-energy CT images, multiple prior regularizations are introduced to the multi-energy CT reconstructed model, leading to an IDEAS method.Both qualitative and quantitative evaluation of our results confirm the outstanding performance of the proposed algorithm compared to the state-of-the-arts.

Volume Conservation Constrained Multi-Material Reconstruction for Inconsistent Spectral CT Imaging

“…Fang et al applied the unsupervised denoising method called Noise2Noise [42] as the prior knowledge to estimate the material maps directly from the raw projection data [43]. And other researcheres also find the deep learning based method has certain advantages in medical image analysis [44][45][46][47]. These methods also encourage us to combine model-driven and data-driven methods to achieve accurate decomposition of materials by eliminating the influence of beam-hardening artifacts while suppressing noise in the future.…”

Sparse-View Spectral CT Reconstruction Based on Tensor Decomposition and Total Generalized Variation

“…The phantom mainly consists of three parts: soft tissue, bone, and iodine. The X-ray source had a tube voltage of 50 kVp, and the energy spectrum was divided into eight channels: [16,22) keV, [22,25) keV, [25,28) keV, [28,31) keV, [31,34) keV, [34,37) keV, [37,41) keV, and [41,50) keV, as depicted in Figure 3. The experiment was conducted using an equidistant fan-beam scan.…”

“…In 2016, Zeng et al proposed a novel algorithm that combines penalized weighted least squares (PWLS) with structural tensor total variation (STV) regularization and employed an alternating optimization algorithm to solve the objective function, resulting in higher-quality spectral CT images [11]. Subsequently, more reconstruction algorithms based on single-energy channel regularization constraints have been proposed, all of which have achieved satisfactory reconstruction results [12][13][14][15][16]. However, these algorithms only process CT images at each energy channel separately during the image reconstruction stage, focusing solely on the correlation between singlechannel images.…”

Hybrid U-Net and Swin-transformer network for limited-angle cardiac computed tomography

“…The celebrated total variation (TV) regularization model is based on the piecewise constancy of the image, which means the sparsity of the discrete gradient transform. TV can suppress noise while preserving edges (Yu et al 2005 , Wang et al 2023a ). For example, TV can be coupled with SART (Andersen and Kak 1984 ) (simultaneous algebraic reconstruction technique) to enhance reconstruction performance.…”