Randomized Iterative Reconstruction for Sparse View X-ray Computed Tomography

2017/03/06 by Dragoş Trincă, Trinca, D., Yutian Zhong +1
Medicine · Engineering · #Medical Imaging Techniques and Applications #Advanced X-ray and CT Imaging #Advanced MRI Techniques and Applications

paper · pdf · doi:10.48550/arxiv.1703.04393

Abstract

With the availability of more powerful computers, iterative reconstruction algorithms are the subject of an ongoing work in the design of more efficient reconstruction algorithms for X-ray computed tomography. In this work, we show how two analytical reconstruction algorithms can be improved by correcting the corresponding reconstructions using a randomized iterative reconstruction algorithm. The combined analytical reconstruction followed by randomized iterative reconstruction can also be viewed as a reconstruction algorithm which, in the experiments we have conducted, uses up to 35% less projection angles as compared to the analytical reconstruction algorithms and produces the same results in terms of quality of reconstruction, without increasing the execution time significantly.

Related

• Comparison of Sinogram-based Iterative Reconstruction with Compressed Sensing Techniques in X-ray CT
• Visual C++ Implementation of Sinogram-based Adaptive Iterative Reconstruction for Sparse View X-Ray CT
• RISING a new framework for few-view tomographic image reconstruction with deep learning
• Adaptive Adjustment of Relaxation Parameters for Algebraic\n Reconstruction Technique and its Possible Application to Sparsity Prior X-ray\n CT Reconstruction
• Sparse-View X-Ray CT Reconstruction Using ℓ₁ Prior with Learned Transform