Accurate and robust sparse‐view angle CT image reconstruction using deep learning and prior image constrained compressed sensing (DL‐PICCS)

2021 ◽  
Author(s):  
Chengzhu Zhang ◽  
Yinsheng Li ◽  
Guang‐Hong Chen
Keyword(s):  
Deep Learning ◽  
Image Reconstruction ◽  
Compressed Sensing ◽  
Ct Image ◽  
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.

A Fourier-based compressed sensing technique for accelerated CT image reconstruction using first-order methods

2014 ◽  
Vol 59 (12) ◽  
pp. 3097-3119 ◽  
Author(s):  
Kihwan Choi ◽  
Ruijiang Li ◽  
Haewon Nam ◽  
Lei Xing
Keyword(s):  
Image Reconstruction ◽  
Compressed Sensing ◽  
Ct Image ◽  
First Order ◽  
Ct Image Reconstruction ◽  
First Order Methods

Compressed sensing algorithms for fan-beam CT image reconstruction

2011 ◽  
Author(s):  
Jun Zhang ◽  
Jun Wang ◽  
Guangwu Xu ◽  
Jean-Baptiste Thibault
Keyword(s):  
Image Reconstruction ◽  
Compressed Sensing ◽  
Ct Image ◽  
Ct Image Reconstruction

scholarly journals A Deep Recurrent Neural Network with Gated Momentum Unit for CT Image Reconstruction

2021 ◽  
Author(s):  
Masaki Ikuta
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Reconstruction ◽  
Image Data ◽  
Observation Data ◽  
Ct Image ◽  
Image Study ◽  
Data Set ◽  
Image Domain ◽  
Ct Image Reconstruction

<div><div><div><p>Many algorithms and methods have been proposed for Computed Tomography (CT) image reconstruction, partic- ularly with the recent surge of interest in machine learning and deep learning methods. The majority of recently proposed methods are, however, limited to the image domain processing where deep learning is used to learn the mapping from a noisy image data set to a true image data set. While deep learning-based methods can produce higher quality images than conventional model-based post-processing algorithms, these methods have lim- itations. Deep learning-based methods used in the image domain are not sufficient for compensating for lost information during a forward and a backward projection in CT image reconstruction especially with a presence of high noise. In this paper, we propose a new Recurrent Neural Network (RNN) architecture for CT image reconstruction. We propose the Gated Momentum Unit (GMU) that has been extended from the Gated Recurrent Unit (GRU) but it is specifically designed for image processing inverse problems. This new RNN cell performs an iterative optimization with an accelerated convergence. The GMU has a few gates to regulate information flow where the gates decide to keep important long-term information and discard insignificant short- term detail. Besides, the GMU has a likelihood term and a prior term analogous to the Iterative Reconstruction (IR). This helps ensure estimated images are consistent with observation data while the prior term makes sure the likelihood term does not overfit each individual observation data. We conducted a synthetic image study along with a real CT image study to demonstrate this proposed method achieved the highest level of Peak Signal to Noise Ratio (PSNR) and Structure Similarity (SSIM). Also, we showed this algorithm converged faster than other well-known methods.</p></div></div></div>

Deep Learning with Adaptive Hyper-parameters for Low-Dose CT Image Reconstruction

2021 ◽  
pp. 1-1
Author(s):  
Qiaoqiao Ding ◽  
Yuesong Nan ◽  
Hao Gao ◽  
Hui Ji
Keyword(s):  
Deep Learning ◽  
Image Reconstruction ◽  
Low Dose ◽  
Ct Image ◽  
Low Dose Ct ◽  
Ct Image Reconstruction

Improving Sparse Compressed Sensing Medical CT Image Reconstruction

2019 ◽  
Vol 53 (3) ◽  
pp. 281-289 ◽  
Author(s):  
Jingyu Zhang ◽  
Jianfu Teng ◽  
Yu Bai
Keyword(s):  
Image Reconstruction ◽  
Compressed Sensing ◽  
Ct Image ◽  
Ct Image Reconstruction
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