Spectral CT reconstruction algorithm based on adaptive tight frame wavelet and total variation

2019 ◽  
Author(s):  
Huihua Kong ◽  
Lei Lei ◽  
Ping Chen
Keyword(s):  
Total Variation ◽  
Reconstruction Algorithm ◽  
Tight Frame ◽  
Ct Reconstruction ◽  
Spectral Ct

scholarly journals Synergistic multi-spectral CT reconstruction with directional total variation

2021 ◽  
Vol 379 (2204) ◽  
pp. 20200198 ◽  
Author(s):  
Evelyn Cueva ◽  
Alexander Meaney ◽  
Samuli Siltanen ◽  
Matthias J. Ehrhardt
Keyword(s):  
Total Variation ◽  
Structural Information ◽  
Signal To Noise Ratio ◽  
Ct Reconstruction ◽  
Spectral Ct ◽  
Iterative Regularization ◽  
Tomographic Image Reconstruction ◽  
Available Energy ◽  
Computational Speed ◽  
Energy Channels

This work considers synergistic multi-spectral CT reconstruction where information from all available energy channels is combined to improve the reconstruction of each individual channel. We propose to fuse these available data (represented by a single sinogram) to obtain a polyenergetic image which keeps structural information shared by the energy channels with increased signal-to-noise ratio. This new image is used as prior information during a channel-by-channel minimization process through the directional total variation. We analyse the use of directional total variation within variational regularization and iterative regularization. Our numerical results on simulated and experimental data show improvements in terms of image quality and in computational speed. This article is part of the theme issue ‘Synergistic tomographic image reconstruction: part 2’.

scholarly journals Implementation of a Framelet-Based Spectral Reconstruction for Multi-Slice Spiral CT

2021 ◽  
Vol 9 ◽  
Author(s):  
Xin Li ◽  
Yanbo Zhang ◽  
Shuwei Mao ◽  
Jiehua Zhu ◽  
Yangbo Ye
Keyword(s):  
Color Image ◽  
Reconstruction Algorithm ◽  
Computational Time ◽  
Spectral Information ◽  
Ct Reconstruction ◽  
Spectral Ct ◽  
Clinical Images ◽  
X Ray ◽  
Spiral Scanning ◽  
Spectral Reconstruction

Spectral CT utilizes spectral information of X-ray sources to reconstruct energy-resolved X-ray images and has wide medical applications. Compared with conventional energy-integrated CT scanners, however, spectral CT faces serious technical difficulties in hardware, and hence its clinical use has been expensive and limited. The goal of this paper is to present a software solution and an implementation of a framelet-based spectral reconstruction algorithm for multi-slice spiral scanning based on a conventional energy-integrated CT hardware platform. In the present work, we implement the framelet-based spectral reconstruction algorithm using compute unified device architecture (CUDA) with bowtie filtration. The platform CUDA enables fast execution of the program, while the bowtie filter reduces radiation exposure. We also adopt an order-subset technique to accelerate the convergence. The multi-slice spiral scanning geometry with these additional features will make the framelet-based spectral reconstruction algorithm more powerful for clinical applications. The method provides spectral information from just one scan with a standard energy-integrating detector and produces color CT images, spectral curves of the attenuation coefficient at every point inside the object, and photoelectric images, which are all valuable imaging tools in cancerous diagnosis. The proposed algorithm is tested with a Catphan phantom and real patient data sets for its performance. In experiments with the Catphan 504 phantom, the synthesized color image reveals changes in the level of colors and details and the yellow color in Teflon indicates a special spectral property which is invisible in regular CT reconstruction. In experiments with clinical images, the synthesized color images provide some extra details which are helpful for clinical diagnosis, for example, details about the renal pelvis and lumbar join. The numerical studies indicate that the proposed method provides spectral image information which can reveal fine structures in clinical images and that the algorithm is efficient regarding to the computational time. Thus, the proposed algorithm has a great potential in practical application.

scholarly journals CT reconstruction algorithm based on truncated TV

2021 ◽  
Vol 1920 (1) ◽  
pp. 012036
Author(s):  
Hongyan Shi ◽  
Aidi Wu ◽  
Shidi Yang ◽  
Dongjiang Ji
Keyword(s):  
Reconstruction Algorithm ◽  
Ct Reconstruction

scholarly journals X-RAY CT Reconstruction by using Spatially Non Homogeneous ICD Optimization

2019 ◽  
Vol 8 (6S3) ◽  
pp. 2043-2046
Keyword(s):  
Search Algorithm ◽  
Computational Cost ◽  
Selection Criterion ◽  
Closed Form Solution ◽  
Helical Ct ◽  
Reconstruction Algorithm ◽  
Form Solution ◽  
Ct Reconstruction ◽  
Practical Applications ◽  
Voxel Selection

Recent applications of conventional iterative coordinate descent (ICD) algorithms to multislice helical CT reconstructions have shown that conventional ICD can greatly improve image quality by increasing resolution as well as reducing noise and some artifacts. However, high computational cost and long reconstruction times remain as a barrier to the use of conventional algorithm in the practical applications. Among the various iterative methods that have been studied for conventional, ICD has been found to have relatively low overall computational requirements due to its fast convergence. This paper presents a fast model-based iterative reconstruction algorithm using spatially nonhomogeneous ICD (NH-ICD) optimization. The NH-ICD algorithm speeds up convergence by focusing computation where it is most needed. The NH-ICD algorithm has a mechanism that adaptively selects voxels for update. First, a voxel selection criterion VSC determines the voxels in greatest need of update. Then a voxel selection algorithm VSA selects the order of successive voxel updates based upon the need for repeated updates of some locations, while retaining characteristics for global convergence. In order to speed up each voxel update, we also propose a fast 3-D optimization algorithm that uses a quadratic substitute function to upper bound the local 3-D objective function, so that a closed form solution can be obtained rather than using a computationally expensive line search algorithm. The experimental results show that the proposed method accelerates the reconstructions by roughly a factor of three on average for typical 3-D multislice geometries.

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