Author

Christian Langkammer

Medical University of GrazORCID 0000-0002-7097-9707Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 3 Assay:multiome· 3 Approach:deep learning· 2 Approach:simulation· 1 Approach:statistical model· 1

Frequent Co-Authors

Berkin BilgiçiD· 2 shared Carlos MiloviciD· 2 shared Ferdinand SchweseriD· 2 shared Jakob MeinekeiD· 2 shared José P. MarquesiD· 2 shared Kieran O’BrieniD· 2 shared Lasse Riis ØstergaardiD· 2 shared Markus BarthiD· 2 shared

Activity by Year

2019-2021 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 20 mutual co-author links

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2 shared papersNeuroscience

Berkin BilgiçORCID

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4 papers

Recent·Most cited

QSM Reconstruction Challenge 2.0: Design and Report of Results

Berkin Bilgiç, Christian Langkammer, José P. Marques, Jakob Meineke, Carlos Milovic, Ferdinand Schweser

The paper reports results from a QSM reconstruction challenge testing algorithm accuracy in simulated brain data, finding iterative methods and Total Variation-based approaches perform best.

Magnetic Resonance in Medicine · Mar 30, 2021Peer-reviewedNeuroscience

QSM Reconstruction Challenge 2.0: a realistic in silico head phantom for MRI data simulation and evaluation of susceptibility mapping procedures

José P. Marques, Jakob Meineke, Carlos Milovic, Berkin Bilgiç, Kwok‐Shing Chan, Renaud Hédouin, Wietske van der Zwaag, Christian Langkammer, Ferdinand Schweser

Magnetic Resonance in Medicine · Feb 26, 2021Peer-reviewedNeuroscience

DeepQSM - Using Deep Learning to Solve the Dipole Inversion for MRI Susceptibility Mapping

Steffen Bollmann, Kasper Gade Bøtker-Rasmussen, Mads Kristensen, Rasmus Guldhammer Blendal, Lasse Riis Østergaard, Maciej Plocharski, Kieran O’Brien, Christian Langkammer, Andrew L. Janke, Markus Barth

DeepQSM uses deep learning to solve the dipole inversion in MRI for susceptibility mapping, improving the identification of brain substructures and their magnetic properties in diseases like multiple sclerosis.

NeuroImage · Mar 29, 2019Peer-reviewedNeuroscience

SHARQnet - Sophisticated Harmonic Artifact Reduction in Quantitative Susceptibility Mapping using a Deep Convolutional Neural Network

Steffen Bollmann, Matilde Holm Kristensen, Morten Skaarup Larsen, Mathias Vassard Olsen, Mads Jozwiak Pedersen, Lasse Riis Østergaard, Kieran O’Brien, Christian Langkammer, Amir Fazlollahi, Markus Barth

SHARQnet improves background field removal in QSM by using a deep convolutional neural network, outperforming current methods without parameter tuning.

Zeitschrift für Medizinische Physik · Feb 14, 2019Peer-reviewedNeuroscience