Statistical image reconstruction for polyenergetic X-ray computed tomography

By Idris A. Elbakri, Jeffrey A. Fessler. IEEE Transactions on Medical Imaging, 2002

737 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/b28583f733944d598c5c5cf63f5585b8e7dd7aa9

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In Biomedical Engineering

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

MoDL: Model-Based Deep Learning Architecture for Inverse Problems (2017, background): “MODEL-BASED recovery of images from noisy and sparse multichannel measurements is now a mature area with success in several application areas such as MRI [1], CT [2], PET [3], microscopy [4].”
CNN-Based Projected Gradient Descent for Consistent CT Image Reconstruction (2017, methodology): “In the case where the CT measurements are corrupted by Poisson noise, the data-fidelity term in (3) should be replaced by weighted least squares [35], [56], [57].”
Tensor-Based Formulation and Nuclear Norm Regularization for Multienergy Computed Tomography (2013, methodology): “The higher-order generalization of the singular value decomposition (SVD), which is also referred to as multi-dimensional SVD [28], has been used for image processing applications such as facial recognition [29].”
Performance comparison between total variation (TV)-based compressed sensing and statistical iterative reconstruction algorithms (2009, background): “…can effectively incorporate the knowledge of photon statistics into the image reconstruction process to reduce the noise variance in the ﬁnal reconstructed images (Sukovic and Clinthorne 2000, De Man et al 2001, Elbakri and Fessler 2002, Li et al 2005, Thibault et al 2007, Wang et al 2008, 2009).”
Convolutional Neural Network based Metal Artifact Reduction in X-ray Computed Tomography (2017, background): “Approximately, the measured data follow the Poisson distribution: where r j is the mean number of background events and read-out noise variance, which is assumed as a known nonnegative constant [42], [43].”
Deep Learning Techniques for Inverse Problems in Imaging (2020, background): “resolution, and image inpainting [2], to a wide variety of tomographic imaging applications, including magnetic resonance imaging [3], X-ray computed tomography [4], and radar imaging [5].”
Iterative reconstruction methods in X-ray CT. (2012, background): “Various approaches have been presented in order to approximate the energy-dependent attenuation and the problem of insufficient information on the material compositions [47,48,49].”
Low-Dose X-ray CT Reconstruction via Dictionary Learning (2012, positive): “Since the updating formula (16) monotonically minimizes the objective function (15) [34], we have”