Improving MR image quality with a multi-task model, using convolutional losses.

"to improve the reproducibility of our approach we have made all trained models and source code openly accessible 1 https://fastmri med nyu edu/ 2 https://zenodo org/record/583096 3 https://github com/brudfors/unires4 http://www simpleitk org 5 https://pypi org/project/bm3d/ publisher's note springer nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.; for evaluating the pelvic model we have used the publicly available gold atlas dataset [ 31 ] ( https://zenodo org/record/583096 ). availability of data and materials the fastmri dataset [ 29 30 ] ( https://fastmri med nyu edu/ ) is publically available. for evaluating the pelvic model we have used the publicly available gold atlas dataset [ 31 ] ( https://zenodo"

"our proposed model and source code were made publicly available.; to improve the reproducibility of our approach we have made all trained models and source code openly accessible 1 https://fastmri med nyu edu/ 2 https://zenodo org/record/583096 3 https://github com/brudfors/unires4 http://www simpleitk org 5 https://pypi org/project/bm3d/ publisher's note springer nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.; the source code used for the conclusions of this article is available on github ( https://github com/attilasimko/mri-ac )."

"Declarations Ethics approval and consent to participateThe pelvic model was trained and validated on an in-house dataset. The scans were captured using a GE Signa 3T PET/MR (GE Healthcare, Chicago, Illinois, United States) at the University Hospital of Umeå, Sweden. All experimental protocols were approved by the Swedish Ethical Review Authority (ethical approval Dnr: 2019-02666), and all methods were carried out in accordance with relevant guidelines and regulations. All participants were adults and their informed consent was obtained. Consent for publicationNot applicable. Competing interestsThe qualitative evaluations were performed using Hero Imaging, NONPI Medical AB, where Attila Simkó is an employee and Tommy Löfstedt, Anders Garpebring, Tufve Nyholm, and Joakim Jonsson are co-owners. Simone Ruiter and Mikael Bylund declare that they have no competing interests. Competing interests The qualitative evaluations were performed using Hero Imaging, NONPI Medical AB, where Attila Simkó is an employee and Tommy Löfstedt, Anders Garpebring, Tufve Nyholm, and Joakim Jonsson are co-owners. Simone Ruiter and Mikael Bylund declare that they have no competing interests."

"Funding Open access funding provided by Umea University. We are grateful for the financial support obtained from the Cancer Research Foundation in Northern Sweden (LP 18-2182, AMP 18-912, AMP 20-1014, LP 22-2319), the Västerbotten regional county, and from Karin and Krister Olsson. The computations were performed on resources provided by the Swedish National Infrastructure for Computing (SNIC) at the High Performance Computing Center North (HPC2N) in Umeå, Sweden, partially funded by the Swedish Research Council through grant agreement no. 2018-05973."