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

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 proceeding › Conference abstract in proceeding › Research › peer-review

Original language	English
Title of host publication	Proceedings of the International Society for Magnetic Resonance in Medicine, ISMRM 27th Annual Meeting & Exhibition
Publication date	2019
Article number	0321
Publication status	Published - 2019
Event	Annual Meeting of the International Society for Magnetic Resonance in Medicine, ISMRM - Montreal, Canada Duration: 11 May 2019 → 16 May 2019 Conference number: 27

Conference

Conference	Annual Meeting of the International Society for Magnetic Resonance in Medicine, ISMRM
Number	27
Country/Territory	Canada
City	Montreal
Period	11/05/2019 → 16/05/2019

AUB Link

Search for the material in Aalborg University Library's search engine

Cite this

@inbook{5fa18439fd55447ebaacbba45e754dbb,

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

author = "H{\o}y, {Pia Christine} and {Storm S{\o}rensen}, Kristine and {\O}stergaard, {Lasse Riis} and Kieran O'Brien and Markus Barth and Steffen Bollmann",

year = "2019",

language = "English",

booktitle = "Proceedings of the International Society for Magnetic Resonance in Medicine, ISMRM 27th Annual Meeting & Exhibition",

note = "Annual Meeting of the International Society for Magnetic Resonance in Medicine, ISMRM ; Conference date: 11-05-2019 Through 16-05-2019",

}

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, Quebec, 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 et al.
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 proceeding › Conference abstract in proceeding › Research › peer-review

TY - ABST

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

T2 - Annual Meeting of the International Society for Magnetic Resonance in Medicine, ISMRM

AU - Høy, Pia Christine

AU - Storm Sørensen, Kristine

AU - Østergaard, Lasse Riis

AU - O'Brien, Kieran

AU - Barth, Markus

AU - Bollmann, Steffen

N1 - Conference code: 27

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

Y2 - 11 May 2019 through 16 May 2019

ER -

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

Conference

AUB Link

Other files and links

Cite this