State of the Art: Iterative CT Reconstruction Techniques.

By L. Geyer, U. Schoepf, F. Meinel, J. Nance, G. Bastarrika, J. Leipsic, N. Paul, M. Rengo, A. Laghi, C. D. Cecco. Radiology, 2015

640 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/f7600099e90e8100a587ea6392204de684f459f4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Mannudeep K. Kalra
2. Yi Zhang
3. Uwe Kruger
4. Hongming Shan
5. Ling Sun
6. Qingsong Yang
7. Wenxiang Cong
8. Ge Wang

In Radiology, Nuclear Medicine and Imaging

1. Yi Zhang
2. Mannudeep K. Kalra
3. Uwe Kruger
4. Hongming Shan
5. Ling Sun
6. Qingsong Yang
7. Wenxiang Cong
8. Ge Wang

Citations in the corpus

• Generative Adversarial Networks for Noise Reduction in Low-Dose CT (2017, methodology): “Iterative reconstruction (IR) techniques have recently been adopted by all major CT vendors [6], [7].”
• 3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Network (2018, positive): “Noise reduction algorithms for LDCT can be categorized into (i) sinogram filtration [10]–[12], (ii) iterative reconstruction [13]–[19], and (iii) post-processing technique [5]–[9].”
• A Perspective on Deep Imaging (2016, result): “This is in sharp contrast to many traditional regularizers that are linear, local, or ad hoc [17].”