Learning residual motion correction for fast and robust 3D multiparametric MRI

Author:

Pirkl Carolin M.ORCID,Cencini MatteoORCID,Kurzawski Jan W.ORCID,Waldmannstetter Diana,Li Hongwei,Sekuboyina AnjanyORCID,Endt Sebastian,Peretti Luca,Donatelli GraziellaORCID,Pasquariello Rosa,Costagli MauroORCID,Buonincontri GuidoORCID,Tosetti Michela,Menzel Marion I.,Menze Bjoern H.

Funder

DFG

Ministero della Salute

Horizon 2020

Regione Toscana

International Graduate School of Science and Engineering

GE Healthcare

Publisher

Elsevier BV

Subject

Computer Graphics and Computer-Aided Design,Health Informatics,Computer Vision and Pattern Recognition,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Reference53 articles.

1. Three dimensional MRF obtains highly repeatable and reproducible multi-parametric estimations in the healthy human brain at 1.5T and 3T;Buonincontri;Neuroimage,2021

2. Deep CNN for outlier detection: a complementary tool to low-rank based methods for reducing motion artefacts;Bydder,2019

3. Magnetic resonance multitasking for motion-resolved quantitative cardiovascular imaging;Christodoulou;Nat. Biomed. Eng.,2018

4. Rigid motion-corrected magnetic resonance fingerprinting;Cruz;Magn. Reson. Med.,2019

5. Deep learning motion compensation for Cartesian and spiral trajectories;Dou,2019

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