Algebraic Iterative Reconstruction-Reprojection (AIRR) Method for High Performance Sparse-View CT Reconstruction

2016 ◽  
Vol 10 (6) ◽  
pp. 2007-2014 ◽  
Author(s):  
Ali Pour Yazdanpanah ◽  
Emma E. Regentova ◽  
George Bebis
Keyword(s):  
Iterative Reconstruction ◽  
High Performance ◽  
Ct Reconstruction ◽  
Algebraic Iterative Reconstruction

scholarly journals The Value of Iterative Reconstruction of Algorithm-Based Coronary Artery Computed Tomography Imaging in the Diagnosis of Old Myocardial Infarction

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Liang Guo ◽  
Lu Ren ◽  
Yajun Shao ◽  
Wei Li ◽  
Shangxian Yu
Keyword(s):  
Coronary Artery ◽  
Image Quality ◽  
Iterative Reconstruction ◽  
Reconstruction Algorithm ◽  
Quality Parameters ◽  
Control Group ◽  
Iterative Reconstruction Algorithm ◽  
Algebraic Iterative Reconstruction ◽  
Experimental Group ◽  
Number Of Iterations

The objective of this study was to compare the diagnostic value of computed tomography (CT) based on iterative reconstruction algorithm in old myocardial infarction (OMI), thereby providing theoretical guidance and practical basis for clinical treatment. In this study, in order to provide theoretical guidance and practical basis for the diagnosis and treatment of clinical OMI, 10 patients with OMI were selected and divided into two groups, with 5 patients in each group. In addition, an algebraic iterative reconstruction algorithm is constructed, which starts from the initial estimation value, compares, and corrects the estimation results and the measured results continuously until the error between the two results is less than the predetermined value. The experimental group was optimized by algebraic iterative reconstruction algorithm, and the control group was reconstructed by the hospital original method. The image quality parameters under different iteration times were analyzed and compared to obtain the optimal iteration times. The value of iterative reconstruction algorithm in clinical diagnosis was investigated by analyzing the time of drawing and the accuracy of diagnosis after drawing. Through the analysis and comparison of the image quality parameters of the patients from the experimental group, it was found that the image quality firstly increased with the increase in the number of iterations but decreased with the increase of the number of iterations after a certain number of iterations. The results showed that the optimal number of iterations was 13 times. The drawing time of the experimental group and the control group was 54.27 minutes and 117.87 minutes in turn, so the difference between the two groups was significant ( P < 0.05 ). Besides, there was a statistically marked difference in the accuracy rate of the experimental group (93.33%) and the control group (73.33%) ( P < 0.05 ). In conclusion, the time required for coronary artery CT imaging using algebraic iterative reconstruction algorithm was greatly reduced and the diagnostic accuracy was hugely improved. Therefore, the coronary artery CT imaging based on iterative reconstruction algorithm could make more effective use of medical resources and improve the diagnostic accuracy in the diagnosis of OMI.

scholarly journals Row Action Algebraic Reconstruction Techniques Implementation in Graphic Cards using SCILAB (Benchmark in Medical Imaging, Seismic Imaging and Walnut Imaging)

2020 ◽  
Author(s):  
Agah Drajat Garnadi ◽  
Muhammad Ilyas ◽  
M.T. Julianto ◽  
S. Nurdiati
Keyword(s):  
Medical Imaging ◽  
Inverse Problems ◽  
Iterative Reconstruction ◽  
Seismic Tomography ◽  
Seismic Imaging ◽  
Test Problems ◽  
Reconstruction Methods ◽  
Algebraic Reconstruction Techniques ◽  
Algebraic Iterative Reconstruction ◽  
Action Methods

In this article we present a SCILAB implementation of algebraic iterative reconstruction methods for discretisation of inverse problems in imaging. These so-called row action methods rely on semi-convergence for achieving the necessary regularisation of the problem. We implement this method using SCILAB and provide a few simplified test problems: medical tomography, seismic tomography and walnut tomography.Numerical results show the capability of this method for the original and perturbed right-hand side vector.

High-Performance Image Reconstruction (HPIR) in Three Dimensions

2010 ◽  
pp. 121-162
Author(s):  
Olivier Bockenbach ◽  
Michael Knaup ◽  
Sven Steckmann ◽  
Marc Kachelrieß
Keyword(s):  
Image Reconstruction ◽  
Graphics Processing Units ◽  
High Performance ◽  
Three Dimensions ◽  
Ct Reconstruction ◽  
Reconstruction Algorithms ◽  
Destructive Testing ◽  
Processing Power ◽  
Field Programmable ◽  
The Way

Commonly used in medical imaging for diagnostic purposes, in luggage scanning, as well as in industrial non-destructive testing applications, Computed Tomography (CT) is an imaging technique that provides cross sections of an object from measurements taken from different angular positions around the object. CT, also referred to as Image Reconstruction (IR), is known to be a very compute-intensive problem. In its simplest form, the computational load is a function of O(M × N3), where M represents the number of measurements taken around the object and N is the dimension of the object. Furthermore, research institutes report that the increase in processing power required by CT is consistently above Moore‘s Law. On the other hand, the changing work flow in hospital requires obtaining CT images faster with better quality from lower dose. In some cases, real time is needed. High Performance Image Reconstruction (HPIR) has to be used to match the performance requirements involved by the use of modern CT reconstruction algorithms in hospitals. Traditionally, this problem had been solved by the design of specific hardware. Nowadays, the evolution of technology makes it possible to use Components of the Shelf (COTS). Typical HPIR platforms can be built around multicore processors such as the Cell Broadband Engine (CBE), General-Purpose Graphics Processing Units (GPGPU) or Field Programmable Gate Arrays (FPGA). These platforms exhibit different level in the parallelism required to implement CT reconstruction algorithms. They also have different properties in the way the computation can be carried out, potentially requiring drastic changes in the way an algorithm can be implemented. Furthermore, because of their COTS nature, it is not always easy to take the best advantages of a given platform and compromises have to be made. Finally, a fully fleshed reconstruction platform also includes the data acquisition interface as well as the vizualisation of the reconstructed slices. These parts are the area of excellence of FPGAs and GPGPUs. However, more often then not, the processing power available in those units exceeds the requirement of a given pipeline and the remaining real estate and processing power can be used for the core of the reconstruction pipeline. Indeed, several design options can be considered for a given algorithm with yet another set of compromises.

scholarly journals Fixing Nonconvergence of Algebraic Iterative Reconstruction with an Unmatched Backprojector

2019 ◽  
Vol 41 (3) ◽  
pp. A1822-A1839
Author(s):  
Yiqiu Dong ◽  
Per Christian Hansen ◽  
Michiel E. Hochstenbach ◽  
Nicolai André Brogaard Riis
Keyword(s):  
Iterative Reconstruction ◽  
Algebraic Iterative Reconstruction
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