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Your search keyword '"Lee, Ivan"' showing total 2 results
Start Over Author "Lee, Ivan" Topic computed axial tomography Topic medicine and health sciences Topic research article
2 results on '"Lee, Ivan"'

1. Backprojection Wiener deconvolution for computed tomographic reconstruction.

Author

Wang, Zhenglin, Cai, Jinhai, Guo, William, Donnelley, Martin, Parsons, David, and Lee, Ivan

Subjects
COMPUTED tomography, DECONVOLUTION of digital images, IMAGE reconstruction, WIENER filters (Signal processing), X-ray imaging
Abstract

Analytical CT reconstruction is popular in practice because of its computational efficiency, but it suffers from low reconstruction quality when an insufficient number of projections are used. To address this issue, this paper presents a new analytical method of backprojection Wiener deconvolution (BPWD). BPWD executes backprojection first, and then applies a Wiener deconvolution to the whole backprojected image. The Wiener filter is derived from a ramp filter, enabling the proposed approach to perform reconstruction and denoising simultaneously. The use of a filter after backprojection does not differentiate between real sampled projections and interpolated ones, introducing reconstruction errors. Therefore a weighted ramp filter was applied to increase the contribution of real sampled projections in the reconstruction, thus improving reconstruction quality. Experiments on synthetic data and real phase-contrast x-ray images showed that the proposed approach yields better reconstruction quality compared to the classical filtered backprojection (FBP) method, with comparable reconstruction speed. [ABSTRACT FROM AUTHOR]

Published
2018
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2. Backprojection Wiener deconvolution for computed tomographic reconstruction

Author

William W. Guo, Jinhai Cai, Ivan Lee, David Parsons, Martin Donnelley, Zhenglin Wang, Wang, Zhenglin, Cai, Jinhai, Guo, William, Donnelley, Martin, Parsons, David, and Lee, Ivan

Subjects
Statistical Noise, Computer science, Physics::Medical Physics, Computed tomography, 02 engineering and technology, Signal-To-Noise Ratio, 030218 nuclear medicine & medical imaging, Convolution, Diagnostic Radiology, Mice, 0302 clinical medicine, Mathematical and Statistical Techniques, 0202 electrical engineering, electronic engineering, information engineering, Medicine and Health Sciences, Lung, Tomography, Numerical Analysis, Multidisciplinary, medicine.diagnostic_test, Phantoms, Imaging, Radiology and Imaging, Statistics, Wiener deconvolution, Bone Imaging, Gaussian Noise, Physical Sciences, symbols, Radiographic Image Interpretation, Computer-Assisted, Engineering and Technology, Medicine, 020201 artificial intelligence & image processing, Algorithm, Algorithms, Interpolation, Research Article, Imaging Techniques, Noise reduction, Science, Equipment, Neuroimaging, Research and Analysis Methods, 03 medical and health sciences, symbols.namesake, Diagnostic Medicine, medicine, Animals, Humans, Computer Simulation, Models, Statistical, Wiener filter, Biology and Life Sciences, Filter (signal processing), Noise Reduction, Computed Axial Tomography, X-Ray Radiography, Computer Science::Computer Vision and Pattern Recognition, Signal Processing, Particle Accelerators, Tomography, X-Ray Computed, Mathematical Functions, Mathematics, Synchrotrons, Neuroscience
Abstract

Analytical CT reconstruction is popular in practice because of its computational efficiency, but it suffers from low reconstruction quality when an insufficient number of projections are used. To address this issue, this paper presents a new analytical method of backprojection Wiener deconvolution (BPWD). BPWD executes backprojection first, and then applies a Wiener deconvolution to the whole backprojected image. The Wiener filter is derived from a ramp filter, enabling the proposed approach to perform reconstruction and denoising simultaneously. The use of a filter after backprojection does not differentiate between real sampled projections and interpolated ones, introducing reconstruction errors. Therefore a weighted ramp filter was applied to increase the contribution of real sampled projections in the reconstruction, thus improving reconstruction quality. Experiments on synthetic data and real phase-contrast x-ray images showed that the proposed approach yields better reconstruction quality compared to the classical filtered backprojection (FBP) method, with comparable reconstruction speed Refereed/Peer-reviewed

Published
2018

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