Reinforcement learning optimizes adaptive angle selection and dose allocation in sparse-view CT reconstruction, yielding better quality and defect detectability than uniform strategies under limited projections or dose.
Foam-like phantoms for comparing tomography algorithms,
2 Pith papers cite this work. Polarity classification is still indexing.
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Equivariance2Inverse merges equivariant imaging and sparse reconstruction into a self-supervised CT method that remains effective under scintillator blurring and limited-angle geometries, outperforming prior methods on real 2DeteCT data.
citing papers explorer
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Deep Reinforcement Learning for Optimizing Angle Selection and Dose Allocation in CT Reconstruction
Reinforcement learning optimizes adaptive angle selection and dose allocation in sparse-view CT reconstruction, yielding better quality and defect detectability than uniform strategies under limited projections or dose.
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Equivariance2Inverse: A Practical Self-Supervised CT Reconstruction Method Benchmarked on Real, Limited-Angle, and Blurred Data
Equivariance2Inverse merges equivariant imaging and sparse reconstruction into a self-supervised CT method that remains effective under scintillator blurring and limited-angle geometries, outperforming prior methods on real 2DeteCT data.