A modified U-Net reduced sparse-view artifacts in simulated 3D angiography volumes, but the evaluation uses FDK reconstructions as ground truth and lacks independent validation.
Feasibility study of deep neural networks to classify intracranial aneurysms using angiographic parametric imaging
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Leveraging Convolutional Neural Networks for 3D Quantitative Angiography Reconstructions from Sparse Cone Beam CT Projections Utilizing CFD Data
A modified U-Net reduced sparse-view artifacts in simulated 3D angiography volumes, but the evaluation uses FDK reconstructions as ground truth and lacks independent validation.