DREAM-Net: Deep Residual Error iterAtive Minimization Network for Sparse-View CT Reconstruction

Sparse-view Computed Tomography (CT) has the ability to reduce radiation dose and shorten the scan time, while the severe streak artifacts will compromise anatomical information. How to reconstruct high-quality images from sparsely sampled projections is a challenging ill-posed problem. In this cont...

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Veröffentlicht in:	IEEE journal of biomedical and health informatics 2023-01, Vol.27 (1), p.1-12
Hauptverfasser:	Zhang, Yikun, Hu, Dianlin, Hao, Shilei, Liu, Jin, Quan, Guotao, Zhang, Yi, Ji, Xu, Chen, Yang
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Artificial neural networks Computed tomography Deep learning Domains Humans Ill posed problems Image Processing, Computer-Assisted - methods Image quality Image reconstruction Iterative methods Machine learning Medical imaging Modules Neural networks Neural Networks, Computer Optimization Phantoms, Imaging Radiation Radiation dosage residual error minimization sparse-view CT Synergism Tomography, X-Ray Computed - methods unrolled iterative reconstruction
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