Z
Zhengrong Liang
Researcher at Stony Brook University
Publications - 394
Citations - 10447
Zhengrong Liang is an academic researcher from Stony Brook University. The author has contributed to research in topics: Iterative reconstruction & Virtual colonoscopy. The author has an hindex of 54, co-authored 394 publications receiving 9404 citations. Previous affiliations of Zhengrong Liang include City University of New York & State University of New York System.
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Journal ArticleDOI
Penalized weighted least-squares approach to sinogram noise reduction and image reconstruction for low-dose X-ray computed tomography
TL;DR: This work investigated a penalized weighted least-squares (PWLS) approach to address this problem in two dimensions, where the WLS considers first- and second-order noise moments and the penalty models signal spatial correlations.
PatentDOI
System and method for performing a three-dimensional virtual examination of objects, such as internal organs
TL;DR: In this paper, a volume visualization technique for generating a 3D visualization image of an internal organ using volume visualization techniques is presented. But this technique is not suitable for the visualization of complex 3D objects.
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Nonlinear sinogram smoothing for low-dose X-ray CT
TL;DR: A relatively accurate statistical model for the sinogram data was investigated, which led to a set of nonlinear equations that can be solved by iterated conditional mode (ICM) algorithm within a reasonable computing time and demonstrated a significant noise suppression without noticeable sacrifice of the spatial resolution.
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Adaptive-weighted total variation minimization for sparse data toward low-dose x-ray computed tomography image reconstruction.
TL;DR: An adaptive-weighted TV (AwTV) minimization algorithm is presented that can yield images with several notable gains, in terms of noise-resolution tradeoff plots and full-width at half-maximum values, as compared to the corresponding conventional TV-POCS algorithm.
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Parameter estimation and tissue segmentation from multispectral MR images
TL;DR: A statistical method is developed to classify tissue types and to segment the corresponding tissue regions from relaxation time T(1), T(2), and proton density P(D) weighted magnetic resonance images.