Spectral-Image Decomposition With Energy-Fusion Sensing for Spectral CT Reconstruction

• Published in: IEEE transactions on instrumentation and measurement 2021, Vol.70, p.1-11
• Main Authors: Wang, Shaoyu, Yu, Haijun, Xi, Yarui, Gong, Changcheng, Wu, Weiwen, Liu, Fenglin
• Format: Article
• Language: English
• Subjects: Attenuation coefficients; Computational geometry; Computed tomography; Convexity; Decomposition; Domains; Image quality; Image reconstruction; Low-rank; Mathematical model; Mathematical models; Medical imaging; Numerical models; Optimization; prior image; Self-similarity; Sensors; Sparsity; Spectra; spectral computed tomography (CT); tensor decomposition; Tensors
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2021.3078555

Spectral-computed tomography (CT) has been demonstrating its great advantages in lesion detection, tissue characterization, and material decomposition. However, the quality of images is often significantly corrupted with various noises, which brings a great challenge for its applications. Because the channel-wise images from different energy interval share similar structure and physical message, the spatial sparsity, global correlation across the spectrum (GCS), and nonlocal self-similarity (NSS) as three important characteristics are employed to spectral CT reconstruction. In this study, we propose a spectral-image decomposition with energy-fusion sensing (SIDES) reconstruction method, which encourages to obtain better quality spectral images and material decomposition results by establishing a unified tensor decomposition model. First, considering the noise distribution in channel-wise and the difference of linear attenuation coefficients within channel-cross, an adaptive weighted full-spectrum prior image as additional supervised information is incorporated to formulate a new weighted prior image-based tensor. Cooperating with original image tensor, they fully explore the spatial sparsity, GCS, and NSS properties. Then, we formulate nonlocal similar patch-based tensor groups to encode the NSS property from image-domain and residual-image-domain (which is expanded by prior-image and image-self). Next, low-rank regularized Tucker tensor decomposition is employed to fully explore the intrinsic knowledge with the help of prior-image supervision. Finally, the relaxed convex optimization model is optimized by dividing reconstruction model into several subproblem using split-Bregman method. Numerical simulations and real experiments are designed to validate and evaluate the SIDES method and the results demonstrate that the SIDES reconstruction outperforms the state-of-the-art.