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Found 9 papers, 1 papers with code
MB-DECTNet: A Model-Based Unrolled Network for Accurate 3D DECT Reconstruction
Therefore, to improve its convergence, we have embedded DECT SIR into a deep learning model-based unrolled network for 3D DECT reconstruction (MB-DECTNet) that can be trained in an end-to-end fashion.
A Metal Artifact Reduction Scheme For Accurate Iterative Dual-Energy CT Algorithms
We compared DEAM with the proposed method to the original DEAM and vendor reconstructions with and without metal-artifact reduction for orthopedic implants (O-MAR).
A Machine-learning Based Initialization for Joint Statistical Iterative Dual-energy CT with Application to Proton Therapy
Dual-energy CT (DECT) has been widely investigated to generate more informative and more accurate images in the past decades.
Highly accurate model for prediction of lung nodule malignancy with CT scans
Here we present NoduleX, a systematic approach to predict lung nodule malignancy from CT data, based on deep learning convolutional neural networks (CNN).
Laplacian Prior Variational Automatic Relevance Determination for Transmission Tomography
In the classic sparsity-driven problems, the fundamental L-1 penalty method has been shown to have good performance in reconstructing signals for a wide range of problems.
Joint System and Algorithm Design for Computationally Efficient Fan Beam Coded Aperture X-ray Coherent Scatter Imaging
In x-ray coherent scatter tomography, tomographic measurements of the forward scatter distribution are used to infer scatter densities within a volume.
Spectrally Grouped Total Variation Reconstruction for Scatter Imaging Using ADMM
We pursue an optimization transfer approach where convex decompositions are used to lift the problem such that all hyper-voxels can be updated in parallel and in closed-form.
Multiresolution Approach to Acceleration of Iterative Image Reconstruction for X-Ray Imaging for Security Applications
As the iterations proceed, the wavelet tree on which the updates are made is expanded based on a criterion and detail coefficients at each level are updated and the tree is expanded this way.
Alternating Minimization Algorithm with Automatic Relevance Determination for Transmission Tomography under Poisson Noise
We propose a globally convergent alternating minimization (AM) algorithm for image reconstruction in transmission tomography, which extends automatic relevance determination (ARD) to Poisson noise models with Beer's law.
