Why do commercial CT scanners still employ traditional, filtered back-projection for image reconstruction?
Xiaochuan Pan,
E. Sidky,
M. Vannier
|
9 |
2009 |
9
2009
|
Inverse scattering transform for the Camassa–Holm equation
A. Constantin,
V. Gerdjikov,
R. Ivanov
|
8 |
2006 |
8
2006
|
Multi-energy CT based on a prior rank, intensity and sparsity model (PRISM)
Hao Gao,
Hengyong Yu,
S. Osher,
Ge Wang
|
7 |
2011 |
7
2011
|
NETT: solving inverse problems with deep neural networks
Housen Li,
Johannes Schwab,
Stephan Antholzer,
M. Haltmeier
|
7 |
2018 |
7
2018
|
Computed tomography reconstruction using deep image prior and learned reconstruction methods
D. O. Baguer,
Johannes Leuschner,
Maximilian Schmidt
|
7 |
2020 |
7
2020
|
High-order total variation minimization for interior tomography
Jiansheng Yang,
Hengyong Yu,
M. Jiang,
Ge Wang
|
7 |
2010 |
7
2010
|
Contrast-enhanced microwave imaging of breast tumors: a computational study using 3D realistic numerical phantoms
J. D. Shea,
P. Kosmas,
B. D. Veen,
S. Hagness
|
7 |
2010 |
7
2010
|
Joint reconstruction of PET-MRI by exploiting structural similarity
7 auth.
Matthias Joachim Ehrhardt,
K. Thielemans,
L. Pizarro,
D. Atkinson,
S. Ourselin,
B. Hutton,
...
S. Arridge
|
7 |
2014 |
7
2014
|
Solving the interior problem of computed tomography using a priori knowledge
M. Courdurier,
F. Noo,
M. Defrise,
H. Kudo
|
7 |
2008 |
7
2008
|
Perturbation resilience and superiorization of iterative algorithms
Y. Censor,
R. Davidi,
G. Herman
|
7 |
2010 |
7
2010
|
Learning the invisible: a hybrid deep learning-shearlet framework for limited angle computed tomography
7 auth.
T. Bubba,
Gitta Kutyniok,
M. Lassas,
M. März,
W. Samek,
S. Siltanen,
...
Vignesh Srinivasan
|
7 |
2018 |
7
2018
|
Ensemble Kalman inversion: a derivative-free technique for machine learning tasks
Nikola B. Kovachki,
A. Stuart
|
7 |
2018 |
7
2018
|
The sliding Frank–Wolfe algorithm and its application to super-resolution microscopy
Quentin Denoyelle,
V. Duval,
G. Peyr'e,
Emmanuel Soubies
|
6 |
2018 |
6
2018
|
