Daniel K. Sodickson — accepted AI-conference papers

2020

GrappaNet: Combining Parallel Imaging With Deep Learning for Multi-Coil MRI Reconstruction

Anuroop Sriram, Jure Zbontar, Tullie Murrell, C. Lawrence Zitnick, Aaron Defazio, Daniel K. Sodickson

CVPR 2020poster

Magnetic Resonance Image (MRI) acquisition is an inherently slow process which has spurred the development of two different acceleration methods: acquiring multiple correlated samples simultaneously (parallel imaging) and acquiring fewer samples than necessary for traditional signal processing metho…

Cited by 136 PDF ↗code ↗Scholar ↗

2018

Variational Deep Learning for Low-Dose Computed Tomography

Erich Kobler, Matthew J. Muckley, Baiyu Chen, Florian Knoll, Kerstin Hammernik, Thomas Pock +2

ICASSP 2018accepted

In this work, we propose a learning-based variational network (VN) approach for reconstruction of low-dose 3D computed tomography data. We focus on two methods to decrease the radiation dose: (1) x-ray tube current reduction, which reduces the signal-to-noise ratio, and (2) x-ray beam interruption,…

Cited by 0 Source ↗Scholar ↗