On Few-View Tomography and Staircase Artifacts

Zeng, G.L.

doi:10.1109/tns.2015.2395952

Cited by 12 publications

(8 citation statements)

References 29 publications

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“…Reconstruction of the intermediate three‐dimensional activity distribution from a limited number of projections and a limited angular coverage is a challenging task. Iterative algorithms based on total variation minimization have been fairly successful for few‐view CT image reconstruction as well as for SPECT imaging . Total variation minimization encourages piecewise constant objects, while preserving edges .…”

Section: Discussionmentioning

confidence: 85%

“…Iterative algorithms based on total variation minimization have been fairly successful for few-view CT image reconstruction 23,24 as well as for SPECT imaging. 25,26 Total variation minimization encourages piecewise constant objects, while preserving edges. 27 Simulations, not presented in this paper, showed that total variation minimization did not improve nuclear image quality, since noise amplitude was larger than edge contrast.…”

Section: Discussionmentioning

confidence: 99%

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Simultaneous fluoroscopic and nuclear imaging: impact of collimator choice on nuclear image quality

et al. 2017

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Section: Discussionmentioning

confidence: 85%

Section: Discussionmentioning

confidence: 99%

Simultaneous fluoroscopic and nuclear imaging: impact of collimator choice on nuclear image quality

et al. 2017

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“…In addition to the conventional MLEM algorithm [19,20], a MAP-EM-TV (maximum a posteriori, expectation maximization, total variation) algorithm was also used to test our argument [21,22]. The purpose of the MAP algorithm is to control noise.…”

Section: Computer Simulationsmentioning

confidence: 99%

Counter examples for unmatched projector/backprojector in an iterative algorithm

Zeng

2019

Chin J Acad Radiol

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It is rather controversial whether it is justified to use an unmatched projector/backprojector pair in an iterative image reconstruction algorithm. One common concern of using an unmatched projector/backprojector pair is that the optimal solution cannot be reached. This concern is misleading and must be clarified. We define a figure-of-merit in the image domain as the distance between the reconstructed image and the true image, as the normalized mean-squared-error (NMSE). The NMSE is used to determine whether an unmatched matched projector/backprojector pair can provide a better image than a matched projector/backprojector pair. Hot and cold lesion's contrast-to-noise ratio is also used as an alternative secondary figureof-merit for algorithm comparison. Computer-generated counterexamples are used to test the performance for matched and unmatched projection/backprojection pairs for different reconstruction algorithms. The projectors are ray-driven, and the backprojectors are ray-driven and pixel-driven. For the attenuation-free data examples, the unmatched pixel-driven backprojector outperforms the matched ray-driven backprojector. For the attenuated data example, the matched ray-driven backprojector performs better. The ray-driven backprojector can be slightly improved by using an attenuation coefficient that is larger than the true one; in this case the backprojector becomes unmatched. Unmatched projector/backprojector pairs are fairly flexible. If the backprojector is properly chosen, good results can be obtained. However, we have not found a general rule to select a good backprojector.

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“…The data fidelity term encourages the forward projections of the reconstruction to match the measurements, while a popular Bayesian term uses the total variation (TV) norm of the reconstruction [ 1 , 2 ]. Minimizing the TV norm encourages the image to be piecewise constant [ 3 – 5 ].…”

Section: Introductionmentioning

confidence: 99%

Iterative Image Reconstruction with Under-Sampled Data Assisted by a Neural Network.

Zeng

2023

J Biotechnology App

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Background: Image reconstruction with under-sampled data is usually achieved by an iterative algorithm, which minimizes an objective function. The objective function commonly contains a data fidelity term and one or more Bayesian terms. A popular Bayesian term is the total variation (TV) norm of the image. Methods: This paper suggests an addition Bayesian term that is generated by a neural network. This neural network is essentially a classifier. This classifier can recognize the artifacts caused by the incomplete data. This classifier is trained by patient images reconstructed by complete and incomplete data sets. This newly introduced Bayesian term is referred to as the CNN score, which is a real number in (-∞, ∞). Results: Patient studies show the good correlation between the CNN score and the severeness of the artifacts due to the incomplete measurements. Conclusions: A neural network can extract features from images that are suffering from incomplete measurements and convert the features into a CNN score. An iterative image reconstruction algorithm can be developed to minimize this CNN score to suppress the artifacts in the reconstructed image.

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On Few-View Tomography and Staircase Artifacts

Cited by 12 publications

References 29 publications

Simultaneous fluoroscopic and nuclear imaging: impact of collimator choice on nuclear image quality

Simultaneous fluoroscopic and nuclear imaging: impact of collimator choice on nuclear image quality

Counter examples for unmatched projector/backprojector in an iterative algorithm

Iterative Image Reconstruction with Under-Sampled Data Assisted by a Neural Network.

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