A data and knowledge driven approach for SPECT using convolutional neural networks and iterative algorithms

DOI10.1515/jiip-2020-0056zbMath1473.92023OpenAlexW3144480604MaRDI QIDQ2048222

Wenbin Li, Wenqi Ao, Jianliang Qian

Publication date: 5 August 2021

Published in: Journal of Inverse and Ill-Posed Problems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1515/jiip-2020-0056

zbMATH Keywords

integral equationsiterative algorithmsconvolutional neural networksdata and knowledge driven

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Biomedical imaging and signal processing (92C55) Numerical methods for inverse problems for integral equations (65R32)

Related Items (1)

Uniformly convex neural networks and non-stationary iterated network Tikhonov (iNETT) method

Uses Software

• ImageNet
• Adam
• U-Net
• AlexNet

Cites Work

• A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
• A unified primal-dual algorithm framework based on Bregman iteration
• Two-dimensional tomography problems and the theory of \(A\)-analytic functions
• An inversion formula for the attenuated \(X\)-ray transformation
• Adjoint State Method for the Identification Problem in SPECT: Recovery of Both the Source and the Attenuation in the Attenuated X-Ray Transform
• Edge-preserving and scale-dependent properties of total variation regularization
• Solving ill-posed inverse problems using iterative deep neural networks
• Deep Convolutional Neural Network for Inverse Problems in Imaging
• Unnamed Item

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