scholarly journals APIR-Net: Autocalibrated Parallel Imaging Reconstruction Using a Neural Network

Author(s):  
Chaoping Zhang ◽  
Florian Dubost ◽  
Marleen de Bruijne ◽  
Stefan Klein ◽  
Dirk H. J. Poot
2013 ◽  
Vol 71 (2) ◽  
pp. 645-660 ◽  
Author(s):  
Huajun She ◽  
Rong-Rong Chen ◽  
Dong Liang ◽  
Edward V. R. DiBella ◽  
Leslie Ying

Author(s):  
Elena Morotti ◽  
Davide Evangelista ◽  
Elena Loli Piccolomini

Deep Learning is developing interesting tools which are of great interest for inverse imaging applications. In this work, we consider a medical imaging reconstruction task from subsampled measurements, which is an active research field where Convolutional Neural Networks have already revealed their great potential. However, the commonly used architectures are very deep and, hence, prone to overfitting and unfeasible for clinical usages. Inspired by the ideas of the green-AI literature, we here propose a shallow neural network to perform an efficient Learned Post-Processing on images roughly reconstructed by the filtered backprojection algorithm. The results obtained on images from the training set and on unseen images, using both the non-expensive network and the widely used very deep ResUNet show that the proposed network computes images of comparable or higher quality in about one fourth of time.


2007 ◽  
Vol 58 (6) ◽  
pp. 1171-1181 ◽  
Author(s):  
Chunlei Liu ◽  
Roland Bammer ◽  
Michael E. Moseley

2014 ◽  
Vol 40 (5) ◽  
pp. 1022-1040 ◽  
Author(s):  
Katherine L. Wright ◽  
Jesse I. Hamilton ◽  
Mark A. Griswold ◽  
Vikas Gulani ◽  
Nicole Seiberlich

2013 ◽  
Vol 15 (Suppl 1) ◽  
pp. E34
Author(s):  
Shu Li ◽  
Gigi Galiana ◽  
Leo Tam ◽  
Sebastian Kozerke ◽  
Jason P Stockmann ◽  
...  

2016 ◽  
Vol 77 (1) ◽  
pp. 209-220 ◽  
Author(s):  
Valentina Taviani ◽  
Marcus T. Alley ◽  
Suchandrima Banerjee ◽  
Dwight G. Nishimura ◽  
Bruce L. Daniel ◽  
...  

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