Residual Neural Network for Filter Kernel Design in Filtered Back-projection for CT Image Reconstruction

2021 ◽  
pp. 164-169
Author(s):  
Jintian Xu ◽  
Chengjin Sun ◽  
Yixing Huang ◽  
Xiaolin Huang
Keyword(s):  
Neural Network ◽  
Image Reconstruction ◽  
Ct Image ◽  
Back Projection ◽  
Filtered Back Projection ◽  
Filter Kernel ◽  
Ct Image Reconstruction ◽  
Kernel Design

scholarly journals Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction

2019 ◽  
Vol 1 (6) ◽  
pp. 269-276 ◽  
Author(s):  
Hongming Shan ◽  
Atul Padole ◽  
Fatemeh Homayounieh ◽  
Uwe Kruger ◽  
Ruhani Doda Khera ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Reconstruction ◽  
Deep Neural Network ◽  
Low Dose ◽  
Ct Image ◽  
Competitive Performance ◽  
Low Dose Ct ◽  
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>

Two new neural network approaches to two-dimensional CT image reconstruction

1999 ◽  
Vol 103 (2) ◽  
pp. 295-302 ◽  
Author(s):  
Fath El Alem F. Ali ◽  
Zensho Nakao ◽  
Yen-Wei Chen
Keyword(s):  
Neural Network ◽  
Image Reconstruction ◽  
Ct Image ◽  
Two Dimensional ◽  
Ct Image Reconstruction ◽  
Network Approaches

scholarly journals Deep-Neural-Network-Based Sinogram Synthesis for Sparse-View CT Image Reconstruction

2019 ◽  
Vol 3 (2) ◽  
pp. 109-119 ◽  
Author(s):  
Hoyeon Lee ◽  
Jongha Lee ◽  
Hyeongseok Kim ◽  
Byungchul Cho ◽  
Seungryong Cho
Keyword(s):  
Neural Network ◽  
Image Reconstruction ◽  
Deep Neural Network ◽  
Ct Image ◽  
Ct Image Reconstruction

Application of Computed Tomographic Image Reconstruction Algorithms Based on Filtered Back-Projection in Diagnosis of Bone Trauma Diseases

2020 ◽  
Vol 10 (5) ◽  
pp. 1219-1224
Author(s):  
Xianyu Li ◽  
Yulin He ◽  
Qun Hua
Keyword(s):  
Image Reconstruction ◽  
Reconstruction Algorithm ◽  
Projection Data ◽  
Ct Image ◽  
Back Projection ◽  
Post Processing ◽  
Filtered Back Projection ◽  
X Ray ◽  
Image Reconstruction Algorithm ◽  
Bone Trauma

Objective: To improve the diagnostic rate of bone trauma diseases by applying image reconstruction algorithm based on filtered back-projection to CT images of bone trauma. Methods: Sixty-three patients with bone trauma in our hospital were selected. After hospitalization, 63 patients took satisfactory localization images to make the lesions on the localization images close to or even exceed the resolution of conventional X-ray films. After scanning, the post-processing workstation software was used for post-processing of image reconstruction algorithm based on filtered back-projection. Finally, the diagnostic accuracy of X-ray plain film, common CT image and image examination based on filtered back-projection was compared statistically. Results: Among 63 cases of bone trauma, 48 cases were found by routine CT cross-sectional examination. The image reconstruction algorithm based on filtered back-projection was applied to all cases of wrist ulnar and trauma examination. The three-dimensional imaging can display the length, direction, shape of articular surface and fracture end of bone trauma as well as the size and spatial position of free small bone fragments stereoscopically and accurately. The relationship between bone trauma and placement. Discussion: Experiments show that when the projection data are complete, the filtering back-projection algorithm can reconstruct the image better, and the overall evaluation of the new filtering function is the best. Usually, the projection data are often incomplete, sometimes even seriously insufficient. At this time, it is necessary to adopt iterative reconstruction algorithm. However, no matter which algorithm is adopted, the reconstruction speed and accuracy are improved, and the quality of the reconstructed image is improved. It remains the direction of future efforts. The FBP method is the basic common algorithm for reconstructing image, and it is also the basis of many other algorithms. It is widely used in medical CT and other fields. Conclusion: The improved CT image reconstruction algorithm based on filtered back-projection has high application value in the diagnosis of bone trauma diseases. By comparing the three indexes of serial processing time, information transfer interface and image noise, the suspicious site of bone trauma can be diagnosed clearly. In recent years, with the popularization of CT and the emergence of spiral CT, it has a good guiding significance for defining clinical diagnosis and treatment.

Sign in / Sign up

Export Citation Format

Share Document