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doi
/
2023_Kalantari_AIDAqc
フォーク元 Aswendt_Lab/2023_Kalantari_AIDAqc
DOI
10.12751/g-node.q82cjj
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ツリー: e41b3a0331
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2023_Kalanta...
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117_m_Ra
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README.md

Species: Mouse C57BL/6

Animal model: Demyelination by curpizone (2%) feeding

Study groups: Control (healthy), Cuprizone treated, cuprizone-clemastine treated mice

Scanner: Bruker USR117/16 with advance III electronics

Field: 11.7 T

RF transmission: Bruker volume coil (RF 72mm Vol Coil 500 1H , Rat Body)

RF reception: Bruker surface coil ( RF 500 1H – Mouse Brain SUC) / Neoos Biotech mouse Brain surface coil

datacite.yml
Title Automated quality control of small animal MR neuroimaging data
Authors Aswendt,Markus;University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Neurology, Cologne, Germany ;ORCID:0000-0003-1423-0934
Kalantari,Aref;University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Neurology, Cologne, Germany ;ORCID:0000-0003-3050-0930
Description This dataset is a super-dataset containing all datasets used to develop the AIDAqc pipeline and corresponds to the following Publication (DOI): tbd
License CC BY-NC-SA 4.0 (https://creativecommons.org/licenses/by-nc-sa/4.0/)
References Aref Kalantari, Mehrab Shahbazi, Marc Schneider, Victor Vera Frazão, Avnish Bhattrai, Lorenzo Carnevale, Yujian Diao, Bart A. A. Franx, Francesco Gammaraccio, Lisa-Marie Goncalves, Susan Lee, Esther M. van Leeuwen, Annika Michalek, Susanne Mueller, Alejandro Rivera Olvera, Daniel Padro, Adam C. Raikes, Mohamed Kotb Selim, Annette van der Toorn, Federico Varriano, Roël Vrooman, Patricia Wenk, H Elliott Albers, Philipp Boehm-Sturm, Eike Budinger, Santiago Canals, Silvia De Santis, Roberta Diaz Brinton, Rick M. Dijkhuizen, Elisenda Eixarch, Gianluigi Forloni, Joanes Grandjean, Khan Hekmatyar, Russell E. Jacobs, Ileana Jelescu, Nyoman D. Kurniawan, Giuseppe Lembo, Dario Livio Longo, Naomi S. Sta Maria, Edoardo Micotti, Emma Muñoz-Moreno, Pedro Ramos-Cabrer, Wilfried Reichardt, Guadalupe Soria, Giovanna D. Ielacqua, Markus Aswendt [doi:tba] (IsSupplementTo)
Funding DFG; 431549029 – SFB 1451
Friebe Foundation; T0498/28960/16
Keywords Quality-control
MRI
Preclinical-imaging
DataLad
Resource Type Dataset