Image Reconstruction of High-Quality Photoacoustic Tomography Using Wavelet-Analysis-Based Algorithm

2008 ◽  
Author(s):  
Lvming Zeng ◽  
Guodong Liu ◽  
Bilin Shao ◽  
Zhong Ren ◽  
Zhen Huang
Keyword(s):  
Image Reconstruction ◽  
Wavelet Analysis ◽  
Photoacoustic Tomography ◽  
High Quality

Half-Time Image Reconstruction in Photoacoustic Tomography

2017 ◽  
pp. 155-164
Author(s):  
Mark A. Anastasio ◽  
Jin Zhang ◽  
Xiaochuan Pan ◽  
Lihong V. Wang
Keyword(s):  
Image Reconstruction ◽  
Photoacoustic Tomography ◽  
Half Time ◽  
Time Image

An Efficient Light-Weight Network for Fast Reconstruction on MR Images

2021 ◽  
Vol 17 ◽  
Author(s):  
Bowen Zhen ◽  
Yingjie Zheng ◽  
Bensheng Qiu
Keyword(s):  
Feature Extraction ◽  
Image Reconstruction ◽  
Data Consistency ◽  
Training Procedure ◽  
Light Weight ◽  
Mr Images ◽  
High Quality ◽  
Mr Image ◽  
Generalization Ability ◽  
Mr Image Reconstruction

Background: In recent years, deep learning (DL) algorithms have emerged in endlessly and achieved impressive performance, which makes it possible to accelerate magnetic resonance (MR) image reconstruction with DL instead of compressed sensing (CS) methods. However, a DL-based MR image reconstruction method has always suffered from its heavy learning parameters and poor generalization ability so far. Therefore, an efficient light-weight network is still in desperate need of fast MR image reconstruction. Methods: We propose an efficient and light-weight MR reconstruction network (named RecNet) that uses a Convolutional Neural Network (CNN) to fast reconstruct high-quality MR images. Specifically, the network is composed of cascade modules, and each cascade module is further divided into feature extraction blocks and a data consistency layer. The feature extraction block can not only effectively extract the features of MR images, but also do not introduce too many parameters for the whole network. To stabilize the training procedure, the correction information of image frequency is adopted in the data consistency (DC) layer. Results: We have evaluated RecNet on a public dataset and the results show that the image quality reconstructed by RecNet is the best on the peak a signal-to-noise ratio (PSNR) and structural similarity index (SSIM) evaluation standards. In addition, the pre-trained RecNet can also reconstruct high-quality MR images on an unseen dataset. Conclusion: The results demonstrate that the RecNet has superior reconstruction ability in various metrics than comparative methods. The RecNet can quickly generate high-quality MR images in fewer parameters. Furthermore, the RecNet has an excellent generalization ability on pathological images and different sampling rates data.

Bayesian Image Reconstruction in Quantitative Photoacoustic Tomography

2013 ◽  
Vol 32 (12) ◽  
pp. 2287-2298 ◽  
Author(s):  
Tanja Tarvainen ◽  
Aki Pulkkinen ◽  
Ben T. Cox ◽  
Jari P. Kaipio ◽  
Simon R. Arridge
Keyword(s):  
Image Reconstruction ◽  
Photoacoustic Tomography ◽  
Bayesian Image Reconstruction

Evaluation of high-quality image reconstruction techniques applied to high-resolution Z-contrast imaging

2017 ◽  
Vol 182 ◽  
pp. 283-291 ◽  
Author(s):  
G. Bárcena-González ◽  
M.P. Guerrero-Lebrero ◽  
E. Guerrero ◽  
A. Yañez ◽  
D. Fernández-Reyes ◽  
...  
Keyword(s):  
High Resolution ◽  
Image Reconstruction ◽  
Contrast Imaging ◽  
High Quality ◽  
Image Reconstruction Techniques ◽  
High Quality Image ◽  
Quality Image ◽  
Z Contrast

scholarly journals A Novel Blind Restoration and Reconstruction Approach for CT Images Based on Sparse Representation and Hierarchical Bayesian-MAP

Algorithms ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 174
Author(s):  
Sun ◽  
Zhang ◽  
Li ◽  
Meng
Keyword(s):  
Image Reconstruction ◽  
Radiation Dose ◽  
Image Restoration ◽  
Sparse Representation ◽  
Iterative Reconstruction ◽  
Model Parameters ◽  
Projection Data ◽  
Ct Image ◽  
High Quality ◽  
Ct Image Reconstruction

Computed tomography (CT) image reconstruction and restoration are very important in medical image processing, and are associated together to be an inverse problem. Image iterative reconstruction is a key tool to increase the applicability of CT imaging and reduce radiation dose. Nevertheless, traditional image iterative reconstruction methods are limited by the sampling theorem and also the blurring of projection data will propagate unhampered artifact in the reconstructed image. To overcome these problems, image restoration techniques should be developed to accurately correct a wide variety of image degrading effects in order to effectively improve image reconstruction. In this paper, a blind image restoration technique is embedded in the compressive sensing CT image reconstruction, which can result in a high-quality reconstruction image using fewer projection data. Because a small amount of data can be obtained by radiation in a shorter time, high-quality image reconstruction with less data is equivalent to reducing radiation dose. Technically, both the blurring process and the sparse representation of the sharp CT image are first modeled as a serial of parameters. The sharp CT image will be obtained from the estimated sparse representation. Then, the model parameters are estimated by a hierarchical Bayesian maximum posteriori formulation. Finally, the estimated model parameters are optimized to obtain the final image reconstruction. We demonstrate the effectiveness of the proposed method with the simulation experiments in terms of the peak signal to noise ratio (PSNR), and structural similarity index (SSIM).

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