Combining Grid Computing and Docker Containers for the Study and Parametrization of CT Image Reconstruction Methods

Chillarón, Mónica; Vidal, Vicente; Segrelles, Damià; Blanquer, Ignácio; Verdú, G.

doi:10.1016/j.procs.2017.05.065

Cited by 12 publications

(24 citation statements)

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“…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: 89%

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

2020

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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.

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Section: Introductionmentioning

confidence: 89%

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

2020

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“…In other works as [14] where we worked with iterative methods, we considered reconstructions with a PSNR of around 60 to be high-quality. Figures 2.1 and 2.2 show the central slice of the phantom and our reconstruction for a resolution with 512 × 512 pixels, the higher resolution we have reconstructed.…”

Section: A Precision and Image Quality Studymentioning

confidence: 99%

“…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%

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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

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“…Thus far this tool has been used to generate and analyze engineering features not features learned from the data using deep learning techniques. We found one other publication from a research group in Spain, which was focused on a problem of CT reconstruction, and the work compared two approaches for managing the software dependencies of the code: store the software libraries on a Storage Element and using containers for executing the job [Chillarón2017].…”

Section: Introductionmentioning

confidence: 99%

CNNcon: A Quantitative Imaging Tool for Lung CT Image Feature Analysis

Causey

Qualls

Moore

et al. 2019

Preprint

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Background: Lung CT scans are widely used for lung cancer screening and diagnosis. Current research focuses on quantitative analytics (radiomics) to improve screening and detection accuracy. However there are very limited numbers of portable software tools for automatic lung CT image analysis. Results: Here we build a Docker container, CNNcon, as a quantitative imaging tool for analyzing lung CT image features. CNNcon is developed from our recently published algorithm for nodule analysis, based on convolutional neural networks (CNN). When provided with a list of the centroid coordinates of regions of interest (ROI) in a volumetric CT study containing potential lung nodules, CNNcon can automatically generate highly accurate malignancy prediction of each ROI. CNNcon can also generate a vector of image features of each ROI, to facilitate further analyses by combining image features and other clinical features. As a Docker container, CNNcon is portable to various computer systems, convenient to install, and easy to use. CNNcon was tested on different computer systems and generated identical results. Conclusions: We anticipate that CNNcon will be a useful tool and broadly acceptable to the research community interested in quantitative image analysis.

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Combining Grid Computing and Docker Containers for the Study and Parametrization of CT Image Reconstruction Methods

Cited by 12 publications

References 13 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

CNNcon: A Quantitative Imaging Tool for Lung CT Image Feature Analysis

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