scholarly journals Evolution from total variation to nonlinear sparsifying transform for sparse-view CT image reconstruction

2019 ◽  
Author(s):  
Jian Dong ◽  
Chunxiao Han ◽  
Zhuanping Qin ◽  
Yanqiu Che
Keyword(s):  
Image Reconstruction ◽  
Image Quality ◽  
Total Variation ◽  
Ct Images ◽  
Difficult Problem ◽  
Ct Image ◽  
Body Parts ◽  
Reconstruction Algorithms ◽  
Intensity Changes ◽  
Ct Image Reconstruction

AbstractSparse-view CT has been widely studied as an effective strategy for reducing radiation dose to patients. However, the conventional image reconstruction algorithms, such as filtered back-projection method and classical algebraic reconstruction techniques, can no longer be competent in the image reconstruction task of sparse-view CT. A new principle, called compressed sensing (CS), has been therefore developed to provide an effective solution for the ill-posed inverse problem of sparse-view CT image reconstruction. Total variation (TV) minimization, which is most extensively studied among the existing CS techniques, has been recognized as a powerful tool for dealing with this difficult problem in image reconstruction community. However, in recent years, the drawbacks of TV are being increasingly reported, which are appearance of patchy artifacts, depict of incorrect object boundaries, and loss in image textures or smooth intensity changes. These degradations appear especially in reconstructing actual CT images with complicated intensity changes. In order to address these drawbacks, a series of advanced algorithms using nonlinear sparsifying transform (NLST) have been proposed very recently. The NLST-based CS is based on a different framework from the TV, and it achieves an improvement in image quality. Since it is a relatively newly proposed idea, within the scope of our knowledge, there exist few literatures that discusses comprehensively how the image quality improvement occurs in comparison with the conventional TV method. In this study, we investigated the image quality differences between the conventional TV minimization and the NLST-based CS, as well as image quality differences among different kinds of NLST-based CS algorithms in the sparse-view CT image reconstruction. More specifically, image reconstructions of actual CT images of different body parts were carried out to demonstrate the image quality differences.

scholarly journals Deep Learning CT Image Reconstruction in Clinical Practice

2020 ◽  
Author(s):  
Clemens Arndt ◽  
Felix Güttler ◽  
Andreas Heinrich ◽  
Florian Bürckenmeyer ◽  
Ioannis Diamantis ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Reconstruction ◽  
Image Quality ◽  
Emergency Services ◽  
Ct Image ◽  
Reconstruction Algorithms ◽  
Key Points ◽  
Ct Image Reconstruction ◽  
Medical Subspecialty ◽  
Image Reconstruction Algorithms

Background Computed tomography (CT) is a central modality in modern radiology contributing to diagnostic medicine in almost every medical subspecialty, but particularly in emergency services. To solve the inverse problem of reconstructing anatomical slice images from the raw output the scanner measures, several methods have been developed, with filtered back projection (FBP) and iterative reconstruction (IR) subsequently providing criterion standards. Currently there are new approaches to reconstruction in the field of artificial intelligence utilizing the upcoming possibilities of machine learning (ML), or more specifically, deep learning (DL). Method This review covers the principles of present CT image reconstruction as well as the basic concepts of DL and its implementation in reconstruction. Subsequently commercially available algorithms and current limitations are being discussed. Results and Conclusion DL is an ML method that utilizes a trained artificial neural network to solve specific problems. Currently two vendors are providing DL image reconstruction algorithms for the clinical routine. For these algorithms, a decrease in image noise and an increase in overall image quality that could potentially facilitate the diagnostic confidence in lesion conspicuity or may translate to dose reduction for given clinical tasks have been shown. One study showed equal diagnostic accuracy in the detection of coronary artery stenosis for DL reconstructed images compared to IR at higher image quality levels. Consequently, a lot more research is necessary and should aim at diagnostic superiority in the clinical context covering a broadness of pathologies to demonstrate the reliability of such DL approaches. Key Points:  Citation Format

Ultra-limited-angle CT image reconstruction algorithm based on reweighting and edge-preserving

2022 ◽  
pp. 1-13
Author(s):  
Lei Shi ◽  
Gangrong Qu ◽  
Yunsong Zhao
Keyword(s):  
Image Reconstruction ◽  
Condition Number ◽  
Reconstruction Algorithm ◽  
Projection Data ◽  
Ct Image ◽  
Reconstruction Algorithms ◽  
Edge Preserving ◽  
Image Reconstruction Algorithm ◽  
Ct Image Reconstruction ◽  
Limited Angle

BACKGROUND: Ultra-limited-angle image reconstruction problem with a limited-angle scanning range less than or equal to π 2 is severely ill-posed. Due to the considerably large condition number of a linear system for image reconstruction, it is extremely challenging to generate a valid reconstructed image by traditional iterative reconstruction algorithms. OBJECTIVE: To develop and test a valid ultra-limited-angle CT image reconstruction algorithm. METHODS: We propose a new optimized reconstruction model and Reweighted Alternating Edge-preserving Diffusion and Smoothing algorithm in which a reweighted method of improving the condition number is incorporated into the idea of AEDS image reconstruction algorithm. The AEDS algorithm utilizes the property of image sparsity to improve partially the results. In experiments, the different algorithms (the Pre-Landweber, AEDS algorithms and our algorithm) are used to reconstruct the Shepp-Logan phantom from the simulated projection data with noises and the flat object with a large ratio between length and width from the real projection data. PSNR and SSIM are used as the quantitative indices to evaluate quality of reconstructed images. RESULTS: Experiment results showed that for simulated projection data, our algorithm improves PSNR and SSIM from 22.46db to 39.38db and from 0.71 to 0.96, respectively. For real projection data, our algorithm yields the highest PSNR and SSIM of 30.89db and 0.88, which obtains a valid reconstructed result. CONCLUSIONS: Our algorithm successfully combines the merits of several image processing and reconstruction algorithms. Thus, our new algorithm outperforms significantly other two algorithms and is valid for ultra-limited-angle CT image reconstruction.

scholarly journals A biological phantom for evaluation of CT image reconstruction algorithms

2014 ◽  
Author(s):  
J. Cammin ◽  
G. S. K. Fung ◽  
E. K. Fishman ◽  
J. H. Siewerdsen ◽  
J. W. Stayman ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Ct Image ◽  
Reconstruction Algorithms ◽  
Ct Image Reconstruction ◽  
Image Reconstruction Algorithms

Correction to “Total Variation-Stokes Strategy for Sparse-View X-ray CT Image Reconstruction” [Mar 14 749-763]

2014 ◽  
Vol 33 (4) ◽  
pp. 1004-1004 ◽  
Author(s):  
Yan Liu ◽  
Zhengrong Liang ◽  
Jianhua Ma ◽  
Hongbing Lu ◽  
Ke Wang ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Total Variation ◽  
Ct Image ◽  
X Ray ◽  
Ct Image Reconstruction

scholarly journals Improved Compressed Sensing-Based Algorithm for Sparse-View CT Image Reconstruction

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
Zangen Zhu ◽  
Khan Wahid ◽  
Paul Babyn ◽  
David Cooper ◽  
Isaac Pratt ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Compressed Sensing ◽  
Total Variation ◽  
Sampling Rate ◽  
Discrete Wavelet ◽  
Ct Image ◽  
Streak Artifact ◽  
Limited Sampling ◽  
Streak Artifacts ◽  
Ct Image Reconstruction

In computed tomography (CT), there are many situations where reconstruction has to be performed with sparse-view data. In sparse-view CT imaging, strong streak artifacts may appear in conventionally reconstructed images due to limited sampling rate that compromises image quality. Compressed sensing (CS) algorithm has shown potential to accurately recover images from highly undersampled data. In the past few years, total-variation-(TV-) based compressed sensing algorithms have been proposed to suppress the streak artifact in CT image reconstruction. In this paper, we propose an efficient compressed sensing-based algorithm for CT image reconstruction from few-view data where we simultaneously minimize three parameters: theℓ1norm, total variation, and a least squares measure. The main feature of our algorithm is the use of two sparsity transforms—discrete wavelet transform and discrete gradient transform. Experiments have been conducted using simulated phantoms and clinical data to evaluate the performance of the proposed algorithm. The results using the proposed scheme show much smaller streaking artifacts and reconstruction errors than other conventional methods.

scholarly journals Distributed Reconstruction via Alternating Direction Method

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Linyuan Wang ◽  
Ailong Cai ◽  
Hanming Zhang ◽  
Bin Yan ◽  
Lei Li ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Reconstruction ◽  
Total Variation ◽  
Reconstruction Algorithm ◽  
Alternating Direction Method ◽  
Ct Image ◽  
Total Variation Minimization ◽  
Considerable Research ◽  
Alternating Direction ◽  
Ct Image Reconstruction

With the development of compressive sensing theory, image reconstruction from few-view projections has received considerable research attentions in the field of computed tomography (CT). Total-variation- (TV-) based CT image reconstruction has been shown to be experimentally capable of producing accurate reconstructions from sparse-view data. In this study, a distributed reconstruction algorithm based on TV minimization has been developed. This algorithm is very simple as it uses the alternating direction method. The proposed method can accelerate the alternating direction total variation minimization (ADTVM) algorithm without losing accuracy.

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