Author
Jinsuh Kim
Bio: Jinsuh Kim is an academic researcher from University of Illinois at Chicago. The author has contributed to research in topics: Quantitative susceptibility mapping. The author has an hindex of 1, co-authored 1 publications receiving 120 citations.
Papers
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Medical University of Graz1, State University of New York System2, University College London3, University of British Columbia4, Kennedy Krieger Institute5, Southern Medical University6, Pontifical Catholic University of Chile7, University of Illinois at Chicago8, University of California, Berkeley9, University of Graz10, Cornell University11, Philips12, Radboud University Nijmegen13, Harvard University14
TL;DR: 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.
152 citations
Cited by
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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.
147 citations
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.
110 citations
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.
106 citations
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.
100 citations
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.
Abstract: A growing body of evidence suggests that the dysregulation of neuronal iron may play a critical role in Alzheimer's disease. Recent MRI studies have established a relationship between iron accumulation and amyloid-β aggregation. The present study provides further insight demonstrating a relationship between iron and tau accumulation using magnetic resonance-based quantitative susceptibility mapping and tau-PET in n = 236 subjects with amyloid-β pathology (from the Swedish BioFINDER-2 study). Both voxel-wise and regional analyses showed a consistent association between differences in bulk magnetic susceptibility, which can be primarily ascribed to an increase in iron content, and tau-PET signal in regions known to be affected in Alzheimer's disease. Subsequent analyses revealed that quantitative susceptibility specifically mediates the relationship between tau-PET and cortical atrophy measures, thus suggesting a modulatory effect of iron burden on the disease process. We also found evidence suggesting the relationship between quantitative susceptibility and tau-PET is stronger in younger participants (age ≤ 65). Together, these results provide in vivo evidence of an association between iron deposition and both tau aggregation and neurodegeneration, which help advance our understanding of the role of iron dysregulation in the Alzheimer's disease aetiology.
77 citations