Iterative Reconstruction from Few-view Projections

Flores, Liubov Alexandrovna; Vidal, Vicente; Verd, Gumersindo

doi:10.1016/j.procs.2015.05.188

Cited by 16 publications

(16 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…That is why the algebraic methods of approximation started to be applied to reconstruct CT images, such as the LSQR (Least Squares QR) algorithm. They are capable of working with fewer views, as we have shown in our previous works [2][3][4][5]. But working with less projections also means more noise in the images.…”

Section: Introductionmentioning

confidence: 91%

“…N denotes the resolution of the image (128×128 pixels, 256×256 pixels, etc). This phase has been analyzed in previous works [2][3][4][5]9], using several different methods to solve the equations system. Nevertheless, in those works, the approach did not include an image filter combined with the iterative process of resolution, which we will study in this paper.…”

Section: Ct Image Reconstruction Processmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of image filters for their integration with LSQR computerized tomography reconstruction method

2020

Self Cite

View full text Add to dashboard Cite

In CT (computerized tomography) imaging reconstruction, the acquired sinograms are usually noisy, so artifacts will appear on the resulting images. Thus, it is necessary to find the adequate filters to combine with reconstruction methods that eliminate the greater amount of noise possible without altering in excess the information that the image contains. The present work is focused on the evaluation of several filtering techniques applied in the elimination of artifacts present in CT sinograms. In particular, we analyze the elimination of Gaussian and Speckle noise. The chosen filtering techniques have been studied using four functions designed to measure the quality of the filtered image and compare it with a reference image. In this way, we determine the ideal parameters to carry out the filtering process on the sinograms, prior to the process of reconstruction of the images. Moreover, we study their application on reconstructed noisy images when using noisy sinograms and finally we select the best filter to combine with an iterative reconstruction method in order to test if it improves the quality of the images. With this, we can determine the feasibility of using the selected filtering method for our CT reconstructions with projections reduction, concluding that the bilateral filter is the filter that behaves best with our images. We will test it when combined with our iterative reconstruction method, which consists on the Least Squares QR method in combination with a regularization technique and an acceleration step, showing how integrating this filter with our reconstruction method improves the quality of the CT images.

show abstract

Section: Introductionmentioning

confidence: 91%

Section: Ct Image Reconstruction Processmentioning

confidence: 99%

Evaluation of image filters for their integration with LSQR computerized tomography reconstruction method

2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…A common approach to reducing the radiation dose is the use of iterative methods, which do not require a complete set of projections, nor are they restricted in terms of projection angles [9], [10], [11], [12], [13], [14]. These type of methods require fewer projections to reconstruct an image.…”

Section: Introductionmentioning

confidence: 99%

Computed tomography medical image reconstruction on affordable equipment by using Out-Of-Core techniques

Chillarón

Quintana-Ortí

Vidal

et al. 2020

Computer Methods and Programs in Biomedicine

Self Cite

View full text Add to dashboard Cite

“…In our previous research, we proposed the parallel implementation of the method for sparse linear equations and sparse least squares (LSQR) to resolve the reconstruction problem [ 8 , 9 ]. In this work, we analyse the system matrix that simulates the scanning process and affects the quality of the reconstructed image.…”

Section: Introductionmentioning

confidence: 99%

System Matrix Analysis for Computed Tomography Imaging

2015

Self Cite

View full text Add to dashboard Cite

In practical applications of computed tomography imaging (CT), it is often the case that the set of projection data is incomplete owing to the physical conditions of the data acquisition process. On the other hand, the high radiation dose imposed on patients is also undesired. These issues demand that high quality CT images can be reconstructed from limited projection data. For this reason, iterative methods of image reconstruction have become a topic of increased research interest. Several algorithms have been proposed for few-view CT. We consider that the accurate solution of the reconstruction problem also depends on the system matrix that simulates the scanning process. In this work, we analyze the application of the Siddon method to generate elements of the matrix and we present results based on real projection data.

show abstract

Iterative Reconstruction from Few-view Projections

Cited by 16 publications

References 15 publications

Evaluation of image filters for their integration with LSQR computerized tomography reconstruction method

Evaluation of image filters for their integration with LSQR computerized tomography reconstruction method

Computed tomography medical image reconstruction on affordable equipment by using Out-Of-Core techniques

System Matrix Analysis for Computed Tomography Imaging

Contact Info

Product

Resources

About