scholarly journals A Bayesian Hyperparameter Inference for Radon-Transformed Image Reconstruction

2011 ◽  
Vol 2011 ◽  
pp. 1-10 ◽  
Author(s):  
Hayaru Shouno ◽  
Madomi Yamasaki ◽  
Masato Okada
Keyword(s):  
Bayesian Inference ◽  
Image Reconstruction ◽  
Prior Information ◽  
Additive White Gaussian Noise ◽  
Estimation Accuracy ◽  
Reconstruction Method ◽  
Model Framework ◽  
Poissonian Noise ◽  
Observation Noise

We develop a hyperparameter inference method for image reconstruction from Radon transform which often appears in the computed tomography, in the manner of Bayesian inference. Hyperparameters are often introduced in Bayesian inference to control the strength ratio between prior information and the fidelity to the observation. Since the quality of the reconstructed image is controlled by the estimation accuracy of these hyperparameters, we apply Bayesian inference into the filtered back-projection (FBP) reconstruction method with hyperparameters inference and demonstrate that the estimated hyperparameters can adapt to the noise level in the observation automatically. In the computer simulation, at first, we show that our algorithm works well in the model framework environment, that is, observation noise is an additive white Gaussian noise case. Then, we also show that our algorithm works well in the more realistic environment, that is, observation noise is Poissonian noise case. After that, we demonstrate an application for the real chest CT image reconstruction under the Gaussian and Poissonian observation noises.

scholarly journals Evaluation of a new image reconstruction method for digital breast tomosynthesis: effects on the visibility of breast lesions and breast density

2019 ◽  
Vol 92 (1103) ◽  
pp. 20190345 ◽  
Author(s):  
Julia Krammer ◽  
Sergei Zolotarev ◽  
Inge Hillman ◽  
Konstantinos Karalis ◽  
Dzmitry Stsepankou ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Image Reconstruction ◽  
Image Quality ◽  
Breast Density ◽  
Breast Imaging ◽  
Reconstruction Method ◽  
Back Projection ◽  
Filtered Back Projection ◽  
Breast Tomosynthesis

Objective: To compare image quality and breast density of two reconstruction methods, the widely-used filtered-back projection (FBP) reconstruction and the iterative heuristic Bayesian inference reconstruction (Bayesian inference reconstruction plus the method of total variation applied, HBI). Methods: Thirty-two clinical DBT data sets with malignant and benign findings, n = 27 and 17, respectively, were reconstructed using FBP and HBI. Three experienced radiologists evaluated the images independently using a 5-point visual grading scale and classified breast density according to the American College of Radiology Breast Imaging-Reporting And Data System Atlas, fifth edition. Image quality metrics included lesion conspicuity, clarity of lesion borders and spicules, noise level, artifacts surrounding the lesion, visibility of parenchyma and breast density. Results: For masses, the image quality of HBI reconstructions was superior to that of FBP in terms of conspicuity,clarity of lesion borders and spicules (p < 0.01). HBI and FBP were not significantly different in calcification conspicuity. Overall, HBI reduced noise and supressed artifacts surrounding the lesions better (p < 0.01). The visibility of fibroglandular parenchyma increased using the HBI method (p < 0.01). On average, five cases per radiologist were downgraded from BI-RADS breast density category C/D to A/B. Conclusion: HBI significantly improves lesion visibility compared to FBP. HBI-visibility of breast parenchyma increased, leading to a lower breast density rating. Applying the HBIR algorithm should improve the diagnostic performance of DBT and decrease the need for additional imaging in patients with dense breasts. Advances in knowledge: Iterative heuristic Bayesian inference (HBI) image reconstruction substantially improves the image quality of breast tomosynthesis leading to a better visibility of breast carcinomas and reduction of the perceived breast density compared to the widely-used filtered-back projection (FPB) reconstruction. Applying HBI should improve the accuracy of breast tomosynthesis and reduce the number of unnecessary breast biopsies. It may also reduce the radiation dose for the patients, which is especially important in the screening context.

Adaptive Dual Conjudate Gradient Projection Algorithm for Compressed Sensing Image Reconstruction

2013 ◽  
Vol 347-350 ◽  
pp. 2600-2604
Author(s):  
Hai Xia Yan ◽  
Yan Jun Liu
Keyword(s):  
Image Reconstruction ◽  
Compressed Sensing ◽  
Gradient Projection ◽  
Projection Algorithm ◽  
Sparse Reconstruction ◽  
Reconstruction Method ◽  
Reconstruction Accuracy ◽  
Reconstruction Quality ◽  
Dual Gradient

In order to improve the quality of noise signals reconstruction method, an algorithm of adaptive dual gradient projection for sparse reconstruction of compressed sensing theory is proposed. In ADGPSR algorithm, the pursuit direction is updated in two conjudate directions, the better original signals estimated value is computed by conjudate coefficient. Thus the reconstruction quality is improved. Experiment results show that, compared with the GPSR algorithm, the ADGPSR algorithm improves the signals reconstruction accuracy, improves PSNR of reconstruction signals, and exhibits higher robustness under different noise intensities.

AN IMPROVED 3DRP PET IMAGE RECONSTRUCTION METHOD WITH REBINNED DIRECT IMAGE ESTIMATE

2006 ◽  
Vol 18 (05) ◽  
pp. 237-245
Author(s):  
WEI-MIN JENG ◽  
HSUAN-HUI WANG
Keyword(s):  
Image Reconstruction ◽  
Image Quality ◽  
Three Dimensional ◽  
Simulation Software ◽  
Direct Image ◽  
Projection Data ◽  
Reconstruction Method ◽  
Two Dimensional ◽  
Dimensional Image

The quality of traditional two-dimensional image reconstruction for PET has been efficiently improved by three-dimensional image reconstruction, but the sensitivity of the data and the quality of the image are restricted by the limit of modality physics. In analytical image reconstruction algorithm, 3DRP method compensates the unmeasured events by forward projection based on the initial direct image estimate. However, the original 3DRP method merely depends on the parallel projections without taking into account the oblique projections. In our proposed 3DRP-SSRB method, we improve the first image estimate by incorporating the rebinned oblique data. SSRB method was used to perform the rebinning operation to make uses of the oblique projection data to improve the sensitivity information. And then project the improved image estimate forward and reconstruct the final image. Conflicting parameters of reconstructed image quality of 3DRP are experimented by simulated three-dimensional phantom study with regard to both system sensitivity and image quality factors. PET simulation software package was used to conduct the experiment along with the MATLAB software to evaluate the effectiveness of two-dimensional FBP, 3DRP, and our proposed 3DRP-SSRB methods. The result demonstrated its better image quality by having better mean squared error numbers in most of output image slices.

scholarly journals Research on Image Reconstruction of Compressed Sensing Based on a Multi-Feature Residual Network

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4202
Author(s):  
Ruili Nan ◽  
Guiling Sun ◽  
Zhihong Wang ◽  
Xiangnan Ren
Keyword(s):  
Image Reconstruction ◽  
Compressed Sensing ◽  
Linear Mapping ◽  
Growth Monitoring ◽  
Reconstruction Method ◽  
Residual Network ◽  
Multi Scale ◽  
Reconstruction Methods ◽  
Image Reconstruction Method

In order to solve the problem of how to quickly and accurately obtain crop images during crop growth monitoring, this paper proposes a deep compressed sensing image reconstruction method based on a multi-feature residual network. In this method, the initial reconstructed image obtained by linear mapping is input to a multi-feature residual reconstruction network, and multi-scale convolution is used to autonomously learn different features of the crop image to realize deep reconstruction of the image, and complete the inverse solution of compressed sensing. Compared with traditional image reconstruction methods, the deep learning-based method relaxes the assumptions about the sparsity of the original crop image and converts multiple iterations into deep neural network calculations to obtain higher accuracy. The experimental results show that the compressed sensing image reconstruction method based on the multi-feature residual network proposed in this paper can improve the quality of crop image reconstruction.

Wavelet-Domain High Resolution Image Reconstruction

2013 ◽  
Vol 756-759 ◽  
pp. 3309-3312
Author(s):  
Li Wen Dong
Keyword(s):  
High Resolution ◽  
Image Reconstruction ◽  
Reconstruction Method ◽  
Wavelet Domain ◽  
Reconstruction Process ◽  
Resolution Image ◽  
High Resolution Image ◽  
Image Reconstruction Method ◽  
High Resolution Images

The aliasing due to subsampling and the blur from the finite detector size can decrease quality of the images and make fine details and structures difficult to interpret. High resolution images can be reconstructed from several adjacent frames in a sequence by reconstruction process. This paper describes the high resolution image reconstruction method based on wavelet domain. In this approach both the image sequences and the degradation operator are presented by orthogonal wavelet with compact support. Experimental results demonstrate that the proposed method is effective to improve image details.

Image Reconstruction with the Fourier Coefficients for Magnetic Induction Tomography

2020 ◽  
Vol 16 (2) ◽  
pp. 156-163
Author(s):  
Jingwen Wang ◽  
Xu Wang ◽  
Dan Yang ◽  
Kaiyang Wang
Keyword(s):  
Inverse Problem ◽  
Image Reconstruction ◽  
Magnetic Induction ◽  
Fourier Coefficients ◽  
Signal Reconstruction ◽  
Reconstruction Algorithm ◽  
Reconstruction Method ◽  
Measurement Equation ◽  
Image Reconstruction Method ◽  
Magnetic Induction Tomography

Background: Image reconstruction of magnetic induction tomography (MIT) is a typical ill-posed inverse problem, which means that the measurements are always far from enough. Thus, MIT image reconstruction results using conventional algorithms such as linear back projection and Landweber often suffer from limitations such as low resolution and blurred edges. Methods: In this paper, based on the recent finite rate of innovation (FRI) framework, a novel image reconstruction method with MIT system is presented. Results: This is achieved through modeling and sampling the MIT signals in FRI framework, resulting in a few new measurements, namely, fourier coefficients. Because each new measurement contains all the pixel position and conductivity information of the dense phase medium, the illposed inverse problem can be improved, by rebuilding the MIT measurement equation with the measurement voltage and the new measurements. Finally, a sparsity-based signal reconstruction algorithm is presented to reconstruct the original MIT image signal, by solving this new measurement equation. Conclusion: Experiments show that the proposed method has better indicators such as image error and correlation coefficient. Therefore, it is a kind of MIT image reconstruction method with high accuracy.

Sign in / Sign up

Export Citation Format

Share Document