Quantitative susceptibility mapping: Report from the 2016 reconstruction challenge.
Christian Langkammer,Ferdinand Schweser,Karin Shmueli,Christian Kames,Xu Li,Li Guo,Carlos Milovic,Jinsuh Kim,Hongjiang Wei,Kristian Bredies,Sagar Buch,Yihao Guo,Zhe Liu,Jakob Meineke,Alexander Rauscher,José P. Marques,Berkin Bilgic +16 more
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TLDR
The aim of the 2016 quantitative susceptibility mapping (QSM) reconstruction challenge was to test the ability of various QSM algorithms to recover the underlying susceptibility from phase data faithfully.Abstract:
Purpose
The aim of the 2016 quantitative susceptibility mapping (QSM) reconstruction challenge was to test the ability of various QSM algorithms to recover the underlying susceptibility from phase data faithfully.
Methods
Gradient-echo images of a healthy volunteer acquired at 3T in a single orientation with 1.06 mm isotropic resolution. A reference susceptibility map was provided, which was computed using the susceptibility tensor imaging algorithm on data acquired at 12 head orientations.
Susceptibility maps calculated from the single orientation data were compared against the reference susceptibility map. Deviations were quantified using the following metrics: root mean squared error (RMSE), structure similarity index (SSIM), high-frequency error norm (HFEN), and the error in selected white and gray matter regions.
Results
Twenty-seven submissions were evaluated. Most of the best scoring approaches estimated the spatial frequency content in the ill-conditioned domain of the dipole kernel using compressed sensing strategies. The top 10 maps in each category had similar error metrics but substantially different visual appearance.
Conclusion
Because QSM algorithms were optimized to minimize error metrics, the resulting susceptibility maps suffered from over-smoothing and conspicuity loss in fine features such as vessels. As such, the challenge highlighted the need for better numerical image quality criteria. Magn Reson Med, 2017. © 2017 International Society for Magnetic Resonance in Medicine.read more
Citations
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Journal ArticleDOI
Quantitative Susceptibility Mapping using Deep Neural Network: QSMnet
Jaeyeon Yoon,Enhao Gong,Itthi Chatnuntawech,Berkin Bilgic,Jingu Lee,Woojin Jung,Jingyu Ko,Hosan Jung,Kawin Setsompop,Greg Zaharchuk,Eung Yeop Kim,John M. Pauly,Jongho Lee +12 more
TL;DR: An MRI reconstruction algorithm, which is referred to as quantitative susceptibility mapping (QSM), has been developed using a deep neural network in order to perform dipole deconvolution, which restores magnetic susceptibility source from an MRI field map.
Journal ArticleDOI
Iron, Myelin, and the Brain: Neuroimaging Meets Neurobiology.
Harald E. Möller,L. Bossoni,James R. Connor,Robert R. Crichton,Mark D. Does,Roberta J. Ward,Luigi Zecca,Luigi Zecca,Fabio A. Zucca,Itamar Ronen +9 more
TL;DR: Interdisciplinary collaborations will be key to advance beyond simple correlative analyses in the biological interpretation of MRI data and to gain deeper insights into key factors leading to iron accumulation and/or redistribution associated with neurodegeneration.
Journal ArticleDOI
The influence of brain iron and myelin on magnetic susceptibility and effective transverse relaxation - A biochemical and histological validation study.
Simon Hametner,Simon Hametner,Verena Endmayr,Andreas Deistung,Andreas Deistung,Pilar Palmrich,Max Prihoda,Evelin Haimburger,Christian Menard,Xiang Feng,Thomas Haider,Marianne Leisser,Ulrike Köck,Alexandra Kaider,Romana Höftberger,Simon Robinson,Jürgen R. Reichenbach,Hans Lassmann,Hannes Traxler,Siegfried Trattnig,Günther Grabner +20 more
TL;DR: A significant relationship was observed between quantitative iron values and QSM, confirming the applicability of the latter in this brain region for iron quantification and a diamagnetic effect of myelin on susceptibility.
Journal ArticleDOI
DeepQSM - using deep learning to solve the dipole inversion for quantitative susceptibility mapping.
Steffen Bollmann,Kasper Gade Bøtker Rasmussen,Mads Ruben Burgdorff Kristensen,Rasmus Guldhammer Blendal,Lasse Riis Østergaard,Maciej Plocharski,Kieran O'Brien,Christian Langkammer,Andrew L. Janke,Markus Barth +9 more
TL;DR: DeepQSM can invert the magnetic dipole kernel convolution and delivers robust solutions to this ill-posed problem, enabling identification of deep brain substructures and provide information on their respective magnetic tissue properties.
Journal ArticleDOI
Relationship between cortical iron and tau aggregation in Alzheimer’s disease
Nicola Spotorno,Nicola Spotorno,Julio Acosta-Cabronero,Erik Stomrud,Björn Lampinen,Olof Strandberg,Danielle van Westen,Oskar Hansson +7 more
TL;DR: Using MR-based quantitative susceptibility mapping and tau-PET, Spotorno et al. provide in vivo evidence for an association between iron deposition and t Tau accumulation in subjects on the Alzheimer's disease continuum.
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