Deep Learning for solving ill-posed problems in Quantitative Susceptibility Mapping: What can possibly go wrong?

Pia Christine Høy, Kristine Storm Sørensen, Lasse Riis Østergaard, Kieran O'Brien, Markus Barth, Steffen Bollmann

Research output: Contribution to book/anthology/report/conference proceedingConference abstract in proceedingResearchpeer-review

Original languageEnglish
Title of host publicationProceedings of the International Society for Magnetic Resonance in Medicine, ISMRM 27th Annual Meeting & Exhibition
Publication date2019
Article number0321
Publication statusPublished - 2019
EventAnnual Meeting of the International Society for Magnetic Resonance in Medicine, ISMRM - Montreal, Canada
Duration: 11 May 201916 May 2019
Conference number: 27

Conference

ConferenceAnnual Meeting of the International Society for Magnetic Resonance in Medicine, ISMRM
Number27
CountryCanada
CityMontreal
Period11/05/201916/05/2019

Cite this

Høy, P. C., Storm Sørensen, K., Østergaard, L. R., O'Brien, K., Barth, M., & Bollmann, S. (2019). Deep Learning for solving ill-posed problems in Quantitative Susceptibility Mapping: What can possibly go wrong? In Proceedings of the International Society for Magnetic Resonance in Medicine, ISMRM 27th Annual Meeting & Exhibition [0321]
Høy, Pia Christine ; Storm Sørensen, Kristine ; Østergaard, Lasse Riis ; O'Brien, Kieran ; Barth, Markus ; Bollmann, Steffen. / Deep Learning for solving ill-posed problems in Quantitative Susceptibility Mapping : What can possibly go wrong?. Proceedings of the International Society for Magnetic Resonance in Medicine, ISMRM 27th Annual Meeting & Exhibition. 2019.
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Høy, PC, Storm Sørensen, K, Østergaard, LR, O'Brien, K, Barth, M & Bollmann, S 2019, Deep Learning for solving ill-posed problems in Quantitative Susceptibility Mapping: What can possibly go wrong? in Proceedings of the International Society for Magnetic Resonance in Medicine, ISMRM 27th Annual Meeting & Exhibition., 0321, Annual Meeting of the International Society for Magnetic Resonance in Medicine, ISMRM, Montreal, Canada, 11/05/2019.

Deep Learning for solving ill-posed problems in Quantitative Susceptibility Mapping : What can possibly go wrong? / Høy, Pia Christine; Storm Sørensen, Kristine; Østergaard, Lasse Riis; O'Brien, Kieran; Barth, Markus; Bollmann, Steffen.

Proceedings of the International Society for Magnetic Resonance in Medicine, ISMRM 27th Annual Meeting & Exhibition. 2019. 0321.

Research output: Contribution to book/anthology/report/conference proceedingConference abstract in proceedingResearchpeer-review

TY - ABST

T1 - Deep Learning for solving ill-posed problems in Quantitative Susceptibility Mapping

T2 - What can possibly go wrong?

AU - Høy, Pia Christine

AU - Storm Sørensen, Kristine

AU - Østergaard, Lasse Riis

AU - O'Brien, Kieran

AU - Barth, Markus

AU - Bollmann, Steffen

PY - 2019

Y1 - 2019

UR - https://www.ismrm.org/19/program_files/O46.htm

M3 - Conference abstract in proceeding

BT - Proceedings of the International Society for Magnetic Resonance in Medicine, ISMRM 27th Annual Meeting & Exhibition

ER -

Høy PC, Storm Sørensen K, Østergaard LR, O'Brien K, Barth M, Bollmann S. Deep Learning for solving ill-posed problems in Quantitative Susceptibility Mapping: What can possibly go wrong? In Proceedings of the International Society for Magnetic Resonance in Medicine, ISMRM 27th Annual Meeting & Exhibition. 2019. 0321