The DOI matches its Crossref record; 69 of the 69 references it cites were found in a registry or an index.
Quantitative susceptibility mapping using deep neural network: QSMnet
Open accessAbstract
Deep neural networks have demonstrated promising potential for the field of medical image reconstruction, successfully generating high quality images for CT, PET and MRI. In this work, 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. Previous approaches of QSM require multiple orientation data (e.g. Calculation of Susceptibility through Multiple Orientation Sampling or COSMOS) or regularization terms (e.g. Truncated K-space Division or TKD; Morphology Enabled Dipole Inversion or MEDI) to solve an ill-conditioned dipole deconvolution problem. Unfortunately, they either entail challenges in data acquisition (i.e. long scan time and multiple head orientations) or suffer from image artifacts. To overcome these shortcomings, a deep neural network, which is referred to as QSMnet, is constructed to generate a high quality susceptibility source map from single orientation data. The network has a modified U-net structure and is trained using COSMOS QSM maps, which are considered as gold standard. Five head orientation datasets from five subjects were employed for patch-wise network training after doubling the training data using a model-based data augmentation. Seven additional datasets of five head orientation images (i.e. total 35 images) were used for validation (one dataset) and test (six datasets). The QSMnet maps of the test dataset were compared with the maps from TKD and MEDI for their image quality and consistency with respect to multiple head orientations. Quantitative and qualitative image quality comparisons demonstrate that the QSMnet results have superior image quality to those of TKD or MEDI results and have comparable image quality to those of COSMOS. Additionally, QSMnet maps reveal substantially better consistency across the multiple head orientation data than those from TKD or MEDI. As a preliminary application, the network was further tested for three patients, one with microbleed, another with multiple sclerosis lesions, and the third with hemorrhage. The QSMnet maps showed similar lesion contrasts with those from MEDI, demonstrating potential for future applications.
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“Recently Yoon et al. [147] constructed a three-dimensional CNN, named QSMnet and based on the U-Net architecture, able to generate high quality susceptibility source maps from single orientation data.”
From An overview of deep learning in medical imaging focusing on MRI (2019) · cites this paper as [147]
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An overview of deep learning in medical imaging focusing on MRI
Lundervold, A. S., & Lundervold, A. (2019) · Zeitschrift für Medizinische Physik · doi:10.1016/j.zemedi.2018.11.002 · Cited by 2,058
An overview of deep learning in medical imaging focusing on MRI
Lundervold, A. S., & Lundervold, A. (2018) · NORA - Norwegian Open Research Archives · Cited by 1,012
Deep Magnetic Resonance Image Reconstruction: Inverse Problems Meet Neural Networks
Liang, D., Cheng, J., Ke, Z., & Ying, L. L. (2020) · IEEE Signal Processing Magazine · doi:10.1109/msp.2019.2950557 · Cited by 422
Choroid Plexus Volume and Permeability at Brain MRI within the Alzheimer Disease Clinical Spectrum
Choi, J. D., Moon, Y., Kim, H.-J., Yim, Y., Lee, S., & Moon, W. (2022) · Radiology · doi:10.1148/radiol.212400 · Cited by 274
Adaptive diffusion priors for accelerated MRI reconstruction
Güngör, A., Dar, S. U. H., Öztürk, Ş., Korkmaz, Y., Bedel, H. A., Elmas, G., Özbey, M., & Çukur, T. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102872 · Cited by 277
Metadata
- Authors
- Yoon, J., Gong, E., Chatnuntawech, I., Bilgic, B., Lee, J., Jung, W., Ko, J., Jung, H., Setsompop, K., Zaharchuk, G., Kim, E. Y., Pauly, J., & Lee, J.
- Affiliations
- Seoul National University (KR)
- Stanford Medicine (US)
- National Nanotechnology Center (TH)
- Harvard University (US)
- Gachon University (KR)
- Stanford University (US)
- Corresponding author
- Lee, Jongho
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2018-10
- Volume
- 179
- Pages
- 199-206
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2791695392
- Cited by
- 185
- References
- 66
- Citations by year
- 2026: 14 · 2025: 24 · 2024: 26 · 2023: 19 · 2022: 29 · 2021: 22 · 2020: 28 · 2019: 19 · 2018: 3
- FWCI
- 12.44
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- NeuroImage (publishedVersion)
- arXiv (Cornell University) (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University)
- Funders
- Seoul National University
- National Research Foundation of Korea (NRF-2017M3C7A1047864)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
- Keywords
- quantitative susceptibility mapping, modified U-Net, cosmos, microbleeds, multiple sclerosis lesions, hemorrhage
- MeSH terms
- Adult, Aged, Algorithms, Brain, Brain Mapping, Female, Humans, Image Processing, Computer-Assisted, Male, Middle Aged, Neural Networks, Computer
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