A Sparse-View CT Reconstruction Method Based on Combination of DenseNet and Deconvolution

Sparse-view computed tomography (CT) holds great promise for speeding up data acquisition and reducing radiation dose in CT scans. Recent advances in reconstruction algorithms for sparse-view CT, such as iterative reconstruction algorithms, obtained high-quality image while requiring advanced comput...

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Veröffentlicht in:	IEEE transactions on medical imaging Jg. 37; H. 6; S. 1407 - 1417
Hauptverfasser:	Zhang, Zhicheng, Liang, Xiaokun, Dong, Xu, Xie, Yaoqin, Cao, Guohua
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.06.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Computed tomography CT reconstruction Data acquisition Databases, Factual Deconvolution Deep Learning DenseNet Humans Image processing Image Processing, Computer-Assisted - methods Image quality Image reconstruction Iterative methods Machine learning Medical imaging Neural networks Preservation Radiation Radiation dosage Radiography, Thoracic Reconstruction algorithms Sparse-view CT State of the art Tomography, X-Ray Computed - methods Training X-ray imaging
ISSN:	0278-0062, 1558-254X, 1558-254X
Online-Zugang:	Volltext
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