GB-SVFBP: Gaussian-Based Shift-Variant FBP neural network
This paper proposes a Gaussian-Based Shift-Variant filtered backprojection (FBP) neural network, which is designed for the efficient reconstruction of non-circular trajectory cone beam computed tomography. The traditional differentiable shift-variant FBP model consists of a filtering component and a backprojection process. The filtering component includes operations such as weightings, differentiations, a 2D Radon transform, and a 2D backprojection. The proposed methods build on this framework by introducing a trainable 2D Gaussian model to represent the trajectory-related part in the filterin
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- LinkedLinked via arxiv author · 85%Chengze Ye →
“GB-SVFBP: Gaussian-Based Shift-Variant FBP neural network”
- LinkedLinked via arxiv author · 85%Linda-Sophie Schneider →
“GB-SVFBP: Gaussian-Based Shift-Variant FBP neural network”
- LinkedLinked via arxiv author · 85%Yipeng Sun →
“GB-SVFBP: Gaussian-Based Shift-Variant FBP neural network”
- LinkedLinked via arxiv author · 85%Andreas Maier →
“GB-SVFBP: Gaussian-Based Shift-Variant FBP neural network”
- FuzzyOverlapping authors or contributors · 62%google-research/google-research →
“Shared author/contributor keys: sun”
- FuzzyOverlapping authors or contributors · 62%mastra-ai/mastra →
“Shared author/contributor keys: schneider”
- FuzzyOverlapping authors or contributors · 62%Fincept-Corporation/FinceptTerminal →
“Shared author/contributor keys: schneider”