A ray-driven neural base-material field model parameterizes attenuation coefficients as continuous implicit functions and uses auto-differentiation to solve spectral CT reconstruction.
Coil: Coordinate-based internal learning for tomographic imaging,
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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.
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Ray-driven Spectral CT Reconstruction Based on Neural Base-Material Fields
A ray-driven neural base-material field model parameterizes attenuation coefficients as continuous implicit functions and uses auto-differentiation to solve spectral CT reconstruction.
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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.