Penalized weighted least-squares approach to sinogram noise reduction and image reconstruction for low-dose X-ray computed tomography

By Jing Wang, Tianfang Li, Hongbing Lu, Zhengrong Liang. IEEE Transactions on Medical Imaging, 2006

570 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/c6dc80c832d6760f8a5d6a01678474e534c0258b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

A Sparse-View CT Reconstruction Method Based on Combination of DenseNet and Deconvolution (2018, background): “The ﬁrst strategy is to lower the x-ray exposure in each view [8]–[10], by adjusting the tube current or exposure time of an x-ray source.”
3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Network (2018, unknown): “Noise reduction algorithms for LDCT can be categorized into (i) sinogram filtration [10]–[12], (ii) iterative reconstruction [13]–[19], and (iii) post-processing technique [5]–[9].”
Performance comparison between total variation (TV)-based compressed sensing and statistical iterative reconstruction algorithms (2009, methodology): “In addition to the Q -factor versus β curve, we also presented the often used (Stayman and Fessler 2000, Wang et al 2006) noise-resolution tradeoff curve in ﬁgure 14.”
Low-Dose X-ray CT Reconstruction via Dictionary Learning (2012, unknown): “tistical weights [7], [35], [36], in this feasibility study we use”
Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction (2018, methodology): “To address this challenge, various noise reduction algorithms were proposed for LDCT, which can be categorized into the following categories: 1) sinogram filtration [8, 9, 10], 2) iterative reconstruction [11, 12, 13], and 3) image post-processing [14, 15, 16, 17].”
An unsupervised convolutional neural network-based algorithm for deformable image registration (2018, methodology): “Previously reported electron density transformation values were used to rescale the intensity distribution of the image using a piecewise approach ( were simulated by adding Poisson and Gaussian noise (Li et al 2004, Wang et al 2006).”