Cited by 185Advanced MRI Techniques and ApplicationsAdvanced Neuroimaging Techniques and ApplicationsImage and Signal Denoising Methods

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 access

Yoon, Jaeyeon, Gong, Enhao, Chatnuntawech, Itthi, Bilgic, Berkin, Lee, Jingu, Jung, Woojin, Ko, Jingyu, Jung, Hosan, Setsompop, Kawin, Zaharchuk, Greg, Kim, Eung Yeop, Pauly, John, Lee, Jongho·2018·NeuroImage

DOI 10.1016/j.neuroimage.2018.06.030

Open this paper in CoChat — free account Open PDF

Abstract

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.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1016/j.neuroimage.2018.06.030

Cites

69 of 69 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate ShiftPreprint
Ioffe, S. (2024) · arXiv (Cornell University) · doi:10.57702/o9raffed · Cited by 15,273
Untitled work
(2010) · Cited by 14,134
Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images
Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002) · NeuroImage · doi:10.1006/nimg.2002.1132 · Cited by 11,888
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
Johnson, J., Alahi, A., & Fei-Fei, L. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46475-6_43 · Cited by 11,283
Fast robust automated brain extraction
Smith, S. M. (2002) · Human Brain Mapping · doi:10.1002/hbm.10062 · Cited by 10,496
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
Kim, J., Lee, J. K., & Lee, K. M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.182 · Cited by 7,870
A global optimisation method for robust affine registration of brain images
Jenkinson, M., & Smith, S. M. (2001) · Medical Image Analysis · doi:10.1016/s1361-8415(01)00036-6 · Cited by 7,018

Show all 66 references

Research works printed but not linked: 1

Marques, 2003, Evaluation of a Fourier based method for calculating susceptibility induced magnetic field perturbations, Proc ISMRM, 11, 1020

Sorted by citations

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
Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformers
Korkmaz, Y., Dar, S. U. H., Yurt, M., Özbey, M., & Çukur, T. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3147426 · Cited by 232
Recommended implementation of quantitative susceptibility mapping for clinical research in the brain: A consensus of the ISMRM electro‐magnetic tissue properties study group
Bilgiç, B., Costagli, M., Chan, K., Duyn, J. H., Langkammer, C., Lee, J., Li, X., Liu, C., Marques, J. P., Milovic, C., et al. (2024) · Magnetic Resonance in Medicine · doi:10.1002/mrm.30006 · Cited by 160
Artificial Intelligence Applications in Stroke
Mouridsen, K., Thurner, P., & Zaharchuk, G. (2020) · Stroke · doi:10.1161/strokeaha.119.027479 · Cited by 139
DeepQSM - using deep learning to solve the dipole inversion for quantitative susceptibility mapping
Bollmann, S., Bøtker-Rasmussen, K. G., Kristensen, M., Blendal, R. G., Østergaard, L. R., Plocharski, M., O’Brien, K., Langkammer, C., Janke, A. L., & Barth, M. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.03.060 · Cited by 131
Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging: Combining physics and machine learning for improved medical imaging
Hammernik, K., Küstner, T., Yaman, B., Huang, Z., Rueckert, D., Knöll, F., & Akçakaya, M. (2023) · IEEE Signal Processing Magazine · doi:10.1109/msp.2022.3215288 · Cited by 122
SEPIA—Susceptibility mapping pipeline tool for phase images
Chan, K., & Marques, J. P. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117611 · Cited by 93
Multi-atlas tool for automated segmentation of brain gray matter nuclei and quantification of their magnetic susceptibility
Li, X., Chen, L., Kutten, K. S., Ceritoglu, C., Li, Y., Kang, N., Hsu, J. T., Qiao, Y., Wei, H., Liu, C., et al. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.02.016 · Cited by 96
Overview of quantitative susceptibility mapping using deep learning: Current status, challenges and opportunities
Jung, W., Bollmann, S., & Lee, J. (2020) · NMR in Biomedicine · doi:10.1002/nbm.4292 · Cited by 83
Training data distribution significantly impacts the estimation of tissue microstructure with machine learning
Gyori, N. G., Palombo, M., Clark, C. A., Zhang, H. G., & Alexander, D. C. (2021) · Magnetic Resonance in Medicine · doi:10.1002/mrm.29014 · Cited by 76
Neuroimaging of Parkinson's disease by quantitative susceptibility mapping
Guan, X., Lancione, M., Ayton, S., Dušek, P., Langkammer, C., & Zhang, M. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120547 · Cited by 73
Exploring linearity of deep neural network trained QSM: QSMnet+
Jung, W., Yoon, J., Ji, S., Choi, J. Y., Kim, J. M., Nam, Y., Kim, E. Y., & Lee, J. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116619 · Cited by 71
QSMGAN: Improved Quantitative Susceptibility Mapping using 3D Generative Adversarial Networks with increased receptive field
Chen, Y., Jakary, A., Avadiappan, S., Hess, C. P., & Lupo, J. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116389 · Cited by 68
Quantitative Susceptibility Mapping: Technical Considerations and Clinical Applications in Neuroimaging
Vinayagamani, S., Sheelakumari, R., Sabarish, S., Senthilvelan, S., Ros, R., Thomas, B., & Kesavadas, C. (2020) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.27058 · Cited by 65
QSM reconstruction challenge 2.0: Design and report of results
Bilgiç, B., Langkammer, C., Marques, J. P., Meineke, J., Milovic, C., & Schweser, F. (2021) · Magnetic Resonance in Medicine · doi:10.1002/mrm.28754 · Cited by 65
Nonlinear dipole inversion (NDI) enables robust quantitative susceptibility mapping (QSM)
Polak, D., Chatnuntawech, I., Yoon, J., Iyer, S., Milovic, C., Lee, J., Bachert, P., Elfar Adalsteinsson, A., Setsompop, K., & Bilgiç, B. (2020) · NMR in Biomedicine · doi:10.1002/nbm.4271 · Cited by 60
Fidelity imposed network edit (FINE) for solving ill-posed image reconstruction
Zhang, J., Liu, Z., Zhang, S., Zhang, H., Spincemaille, P., Nguyen, T. D., Sabuncu, M. R., & Wang, Y. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116579 · Cited by 58
Electrical Properties Tomography: A Methodological Review
Leijsen, R., Brink, W. M., van den Berg, C. A. T., WEBB, A. G., & Remis, R. (2021) · Diagnostics · doi:10.3390/diagnostics11020176 · Cited by 60
Learning-based single-step quantitative susceptibility mapping reconstruction without brain extraction
Wei, H., Cao, S., Zhang, Y., Guan, X., Yan, F., Yeom, K. W., & Liu, C. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116064 · Cited by 53
Rapid MR relaxometry using deep learning: An overview of current techniques and emerging trends
Feng, L., Ma, D., & Liu, F. (2020) · NMR in Biomedicine · doi:10.1002/nbm.4416 · Cited by 55
Developing Robust Flood Susceptibility Model with Small Numbers of Parameters in Highly Fertile Regions of Northwest Bangladesh for Sustainable Flood and Agriculture Management
Sarkar, S. K., Ansar, S. B., Ekram, K. M. M., Khan, M. H., Talukdar, S., Naikoo, M. W., Islam, A. R. M. T., Rahman, A., & Mosavi, A. H. (2022) · Sustainability · doi:10.3390/su14073982 · Cited by 50
Deep Learning in MR Image Processing
Lee, D., Lee, J., Ko, J., Yoon, J., Ryu, K., & Nam, Y. (2019) · Investigative Magnetic Resonance Imaging · doi:10.13104/imri.2019.23.2.81 · Cited by 50
Deep model-based magnetic resonance parameter mapping network (DOPAMINE) for fast T1 mapping using variable flip angle method
Jun, Y., Shin, H., Eo, T., Kim, T., & Hwang, D. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102017 · Cited by 50
Distortion correction of single-shot EPI enabled by deep-learning
Hu, Z., Wang, Y., Zhang, Z., Zhang, J., Zhang, H., Guo, C., Sun, Y., & Guo, H. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117170 · Cited by 48
xQSM: quantitative susceptibility mapping with octave convolutional and noise‐regularized neural networks
Gao, Y., Zhu, X., Moffat, B. A., Glarin, R. K., Wilman, A. H., Pike, G. B., Crozier‬, ‪., Liu, F., & Sun, H. (2020) · NMR in Biomedicine · doi:10.1002/nbm.4461 · Cited by 48
MoDL-QSM: Model-based deep learning for quantitative susceptibility mapping
Feng, R., Zhao, J., Wang, H., Yang, B., Feng, J., Shi, Y., Zhang, M., Liu, C., Zhang, Y., Zhuang, J., et al. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118376 · Cited by 46
Knowledge‐driven deep learning for fast MR imaging: Undersampled MR image reconstruction from supervised to un‐supervised learning
Wang, S., Wu, R., Jia, S., Diakite, A., Li, C., Liu, Q., Zheng, H., & Ying, L. L. (2024) · Magnetic Resonance in Medicine · doi:10.1002/mrm.30105 · Cited by 43
Artificial neural network for myelin water imaging
Lee, J., Lee, D., Choi, J. Y., Shin, D., Shin, H., & Lee, J. (2019) · Magnetic Resonance in Medicine · doi:10.1002/mrm.28038 · Cited by 39
Learned Proximal Networks for Quantitative Susceptibility Mapping
Lai, K.-W., Aggarwal, M., van Zijl, P. C. M., Li, X., & Sulam, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59713-9_13 · Cited by 40
Deep MRI Reconstruction: Unrolled Optimization Algorithms Meet Neural NetworksPreprint
Liang, D., Cheng, J., Ke, Z., & Ying, L. L. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.11711 · Cited by 41
Arterial spin labeling MR image denoising and reconstruction using unsupervised deep learning
Gong, K., Han, P. K., Fakhri, G. E., Ma, C., & Li, Q. (2019) · NMR in Biomedicine · doi:10.1002/nbm.4224 · Cited by 36
QQ‐NET – using deep learning to solve quantitative susceptibility mapping and quantitative blood oxygen level dependent magnitude (QSM+qBOLD or QQ) based oxygen extraction fraction (OEF) mapping
Cho, J., Zhang, J., Spincemaille, P., Zhang, H., Hubertus, S., Wen, Y., Jafari, R., Zhang, S., Nguyen, T. D., Dimov, A., et al. (2021) · Magnetic Resonance in Medicine · doi:10.1002/mrm.29057 · Cited by 33
SHARQnet – Sophisticated harmonic artifact reduction in quantitative susceptibility mapping using a deep convolutional neural network
Bollmann, S., Kristensen, M. H., Larsen, M. S., Olsen, M. V., Pedersen, M. J., Østergaard, L. R., O’Brien, K., Langkammer, C., Fazlollahi, A., & Barth, M. (2019) · Zeitschrift für Medizinische Physik · doi:10.1016/j.zemedi.2019.01.001 · Cited by 31
Using an artificial neural network for fast mapping of the oxygen extraction fraction with combined QSM and quantitative BOLD
Hubertus, S., Thomas, S., Cho, J., Zhang, S., Wang, Y., & Schad, L. R. (2019) · Magnetic Resonance in Medicine · doi:10.1002/mrm.27882 · Cited by 31
Instant tissue field and magnetic susceptibility mapping from MRI raw phase using Laplacian enhanced deep neural networks
Gao, Y., Xiong, Z., Fazlollahi, A., Nestor, P. J., Vegh, V., Nasrallah, F. A., Winter, C. D., Pike, G. B., Crozier‬, ‪., Liu, F., et al. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119410 · Cited by 31
Data‐driven synthetic MRI FLAIR artifact correction via deep neural network
Ryu, K., Nam, Y., Gho, S., Jang, J., Lee, H., Cha, J., Baek, H. J., Park, J., & Kim, D. (2019) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.26712 · Cited by 29
Mapping tissue microstructure of brain white matter in vivo in health and disease using diffusion MRI
Liao, Y., Coelho, S., Chen, J., Ades‐Aron, B., Pang, M., Stepanov, V., Osorio, R. S., Shepherd, T. M., Lui, Y. W., Novikov, D. S., et al. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00102 · Cited by 29
Learning Fourier-Constrained Diffusion Bridges for MRI Reconstruction
Mirza, M. U., Dalmaz, O., Bedel, H. A., Elmas, G., Korkmaz, Y., Güngör, A., Dar, S. U. H., Oguz, K. K., & Çukur, T. (2026) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2026.3673956 · Cited by 38
Parallel-stream fusion of scan-specific and scan-general priors for learning deep MRI reconstruction in low-data regimes
Dar, S. U. H., Öztürk, Ş., Özbey, M., Oğuz, K. K., & Çukur, T. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107610 · Cited by 30
So You Want to Image Myelin Using MRI: Magnetic Susceptibility Source Separation for Myelin Imaging
Lee, J., Ji, S., & Oh, S. (2024) · Magnetic Resonance in Medical Sciences · doi:10.2463/mrms.rev.2024-0001 · Cited by 28
Accelerating quantitative susceptibility and R2* mapping using incoherent undersampling and deep neural network reconstruction
Gao, Y., Cloos, M. A., Liu, F., Crozier‬, ‪., Pike, G. B., & Sun, H. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118404 · Cited by 27
DIFFnet: Diffusion Parameter Mapping Network Generalized for Input Diffusion Gradient Schemes and b-Value
Park, J., Jung, W., Choi, E.-J., Oh, S.-H., Jang, J., Shin, D., An, H., & Lee, J. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3116298 · Cited by 28
QSMART: Quantitative susceptibility mapping artifact reduction technique
Yaghmaie, N., Syeda, W. T., Wu, C., Zhang, Y., Zhang, T. D., Burrows, E. L., Brodtmann, A. G., Moffat, B. A., Wright, D. K., Glarin, R. K., et al. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2020.117701 · Cited by 25
Classifying intracranial stenosis disease severity from functional MRI data using machine learning
Waddle, S. L., Juttukonda, M. R., Lants, S. K., Davis, L. T., Chitale, R. V., Fusco, M. R., Jordan, L. C., & Donahue, M. J. (2019) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x19848098 · Cited by 25
Acute Post-Concussive Assessments of Brain Tissue Magnetism Using Magnetic Resonance Imaging
Koch, K. M., Nencka, A. S., Swearingen, B., Bauer, A., Meier, T. B., & McCrea, M. (2020) · Journal of Neurotrauma · doi:10.1089/neu.2020.7322 · Cited by 22
χ ‐sepnet: Deep Neural Network for Magnetic Susceptibility Source Separation
Kim, M., Ji, S., Kim, J., Min, K., Jeong, H., Youn, J., Kim, T., Jang, J., Bilgiç, B., Shin, H., et al. (2025) · Human Brain Mapping · doi:10.1002/hbm.70136 · Cited by 21
Unsupervised resolution-agnostic quantitative susceptibility mapping using adaptive instance normalization
Oh, G., Bae, H., Ahn, H., Park, S., Moon, W., & Ye, J. C. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102477 · Cited by 21
DeepSPIO: Super Paramagnetic Iron Oxide Particle Quantification Using Deep Learning in Magnetic Resonance Imaging
della Maggiora, G., Castillo‐Passi, C., Qiu, W., Liu, S., Milovic, C., Sekino, M., Cristián Tejos, N., Uribe, S. A., & Pablo Irarrázaval, M. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3012103 · Cited by 20
Cortical Iron Accumulation as an Imaging Marker for Neurodegeneration in Clinical Cognitive Impairment Spectrum: A Quantitative Susceptibility Mapping Study
Kim, H. W., Lee, S., Yang, J. H., Moon, Y., Lee, J., & Moon, W. (2023) · Korean Journal of Radiology · doi:10.3348/kjr.2023.0490 · Cited by 20
Quantitative susceptibility mapping through model-based deep image prior (MoDIP)
Xiong, Z., Gao, Y., Liu, Y., Fazlollahi, A., Nestor, P. J., Liu, F., & Sun, H. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120583 · Cited by 20
Synthesizing T1 weighted MPRAGE image from multi echo GRE images via deep neural network
Ryu, K., Shin, N.-Y., Kim, D., & Nam, Y. (2019) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2019.04.002 · Cited by 20
Opportunities for Understanding MS Mechanisms and Progression With MRI Using Large-Scale Data Sharing and Artificial Intelligence
Vrenken, H., Jenkinson, M., Pham, D. L., Guttmann, C. R. G., Pareto, D., Paardekooper, M., de Sitter, A., Rocca, M. A., Wottschel, V., Cardoso, M. J., et al. (2021) · Neurology · doi:10.1212/wnl.0000000000012884 · Cited by 20
Affine transformation edited and refined deep neural network for quantitative susceptibility mapping
Xiong, Z., Gao, Y., Liu, F., & Sun, H. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119842 · Cited by 19
XSIM : A structural similarity index measure optimized for MRI QSM
Milovic, C., Cristián Tejos, N., Silva, J., Shmueli, K., & Pablo Irarrázaval, M. (2024) · Magnetic Resonance in Medicine · doi:10.1002/mrm.30271 · Cited by 19
Towards in vivo ground truth susceptibility for single-orientation deep learning QSM: A multi-orientation gradient-echo MRI dataset
Shi, Y., Feng, R., Li, Z., Zhuang, J., Zhang, Y., & Wei, H. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119522 · Cited by 18
Deep learning–based quantitative susceptibility mapping (QSM) in the presence of fat using synthetically generated multi‐echo phase training data
Hanspach, J., Bollmann, S., Grigo, J., Karius, A., Uder, M., & Laun, F. B. (2022) · Magnetic Resonance in Medicine · doi:10.1002/mrm.29265 · Cited by 17
Quantitative Susceptibility Mapping
Shmueli, K. (2020) · Advances in magnetic resonance technology and applications · doi:10.1016/b978-0-12-817057-1.00033-0 · Cited by 20
Deep Learning Staging of Liver Iron Content From Multiecho MR Images
Positano, V., Meloni, A., Santarelli, M. F., Pistoia, L., Spasiano, A., Cuccia, L., Casini, T., Gamberini, M. R., Allò, M., Bitti, P. P., et al. (2022) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.28300 · Cited by 16
IMPULSED model based cytological feature estimation with U‐Net : Application to human brain tumor at 3T
Wu, J., Kang, T., Lan, X., Chen, X., Wu, Z., Wang, J., Lin, L., Cai, C., Lin, J., Ding, X., et al. (2022) · Magnetic Resonance in Medicine · doi:10.1002/mrm.29429 · Cited by 16
A subject-specific unsupervised deep learning method for quantitative susceptibility mapping using implicit neural representation
Zhang, M., Feng, R., Li, Z., Feng, J., Wu, Q., Zhang, Z., Ma, C., Wu, J., Yan, F., Liu, C., et al. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103173 · Cited by 17
Efficient Folded Attention for Medical Image Reconstruction and Segmentation
Zhang, H., Zhang, J., Wang, R., Zhang, Q., Spincemaille, P., Nguyen, T. D., & Wang, Y. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i12.17298 · Cited by 16
Comparison of quantitative susceptibility mapping methods for iron-sensitive susceptibility imaging at 7T: An evaluation in healthy subjects and patients with Huntington's disease
Yao, J., Morrison, M. A., Jakary, A., Avadiappan, S., Chen, Y., Luitjens, J., Glueck, J. A., Driscoll, T., Geschwind, M. D., Nelson, A., et al. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119788 · Cited by 14
msQSM: Morphology-based self-supervised deep learning for quantitative susceptibility mapping
He, J., Peng, Y., Fu, B., Zhu, Y., Wang, L., & Wang, R. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120181 · Cited by 14
Choice of training label matters: how to best use deep learning for quantitative MRI parameter estimation
Epstein, S. C., Bray, T., Hall-Craggs, M. A., & Zhang, H. G. (2024) · The Journal of Machine Learning for Biomedical Imaging · doi:10.59275/j.melba.2024-geb5 · Cited by 12
qMTNet: Accelerated quantitative magnetization transfer imaging with artificial neural networks
Luu, H. M., Kim, D., Kim, J., Choi, S. H., & Park, S. (2020) · Magnetic Resonance in Medicine · doi:10.1002/mrm.28411 · Cited by 12
DeepResp: Deep learning solution for respiration-induced B0 fluctuation artifacts in multi-slice GRE
An, H., Shin, H., Ji, S., Jung, W., Sehong, O., Shin, D., Park, J.-H., & Lee, J. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117432 · Cited by 12
Deep Reinforcement Learning Designed Shinnar-Le Roux RF Pulse Using Root-Flipping: DeepRFSLR
Shin, D., Ji, S., Lee, D., Lee, J., Oh, S., & Lee, J. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3018508 · Cited by 11
Emerging deep learning techniques using magnetic resonance imaging data applied in multiple sclerosis and clinical isolated syndrome patients (Review)
Kontopodis, E., Papadaki, E. Z., Trivzakis, E., Maris, T. G., Simos, P. G., Papadakis, G. Z., Tsatsakis, A. M., Spandidos, D. A., Karantanas, A. H., & Kostas Marias, A. (2021) · Experimental and Therapeutic Medicine · doi:10.3892/etm.2021.10583 · Cited by 11
S2Q-Net: Mining the High-Pass Filtered Phase Data in Susceptibility Weighted Imaging for Quantitative Susceptibility Mapping
Lu, Z., Li, J., Wang, C., Ge, R., Chen, L., He, H., & Shi, J. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3156548 · Cited by 11
Early differentiation of neurodegenerative diseases using the novel QSM technique: what is the biomarker of each disorder?
Nikparast, F., Ganji, Z., & Zare, H. (2022) · BMC Neuroscience · doi:10.1186/s12868-022-00725-9 · Cited by 11
Basal Ganglia Iron Content Increases with Glioma Severity Using Quantitative Susceptibility Mapping: A Potential Biomarker of Tumor Severity
Reith, T. P., Prah, M. A., Choi, E., Lee, J., Wujek, R. T., Al-Gizawiy, M. M., Chitambar, C. R., Connelly, J., & Schmainda, K. M. (2022) · Tomography · doi:10.3390/tomography8020065 · Cited by 11
DeepSTI: Towards tensor reconstruction using fewer orientations in susceptibility tensor imaging
Fang, Z., Lai, K.-W., van Zijl, P. C. M., Li, X., & Sulam, J. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102829 · Cited by 13
Sensitivity of Quantitative Susceptibility Mapping for Clinical Research in Deep Gray Matter
Salman, F., Ramesh, A., Jochmann, T., Prayer, M., Adegbemigun, A., Reeves, J. A., Wilding, G. E., Cho, J., Jakimovski, D., Bergsland, N., et al. (2025) · Human Brain Mapping · doi:10.1002/hbm.70187 · Cited by 12
A narrative review of medical image processing by deep learning models: origin to COVID-19
(2022) · International Journal of Advanced Technology and Engineering Exploration · doi:10.19101/ijatee.2021.874887 · Cited by 11
Plug-and-Play latent feature editing for orientation-adaptive quantitative susceptibility mapping neural networks
Gao, Y., Xiong, Z., Shan, S., Liu, Y., Rong, P., Li, M., Wilman, A. H., Pike, G. B., Liu, F., & Sun, H. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103160 · Cited by 9
Super-resolution Reconstruction Using Multiconnection Deep Residual Network Combined an Improved Loss Function for Single-frame Image
Peng, Y. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-7544-1 · Cited by 9
Quad-Contrast Imaging: Simultaneous Acquisition of Four Contrast-Weighted Images (PD-Weighted, T₂-Weighted, PD-FLAIR and T₂-FLAIR Images) With Synthetic T₁-Weighted Image, T₁- and T₂-Maps
Ji, S., Jeong, J., Oh, S.-H., Nam, Y., Choi, S. H., Shin, H., Shin, D., Jung, W., & Lee, J. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3093617 · Cited by 9
Bayesian Learning of Probabilistic Dipole Inversion for Quantitative Susceptibility MappingPreprint
Zhang, J., Zhang, H., Sabuncu, M. R., Spincemaille, P., Nguyen, T. D., & Wang, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.12573 · Cited by 9
NeXtQSM—A complete deep learning pipeline for data-consistent Quantitative Susceptibility Mapping trained with hybrid data
Cognolato, F., O’Brien, K., Jin, J., Robinson, S., Laun, F. B., Barth, M., & Bollmann, S. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102700 · Cited by 8
Meta-Learning Based Interactively Connected Clique U-Net for Quantitative Susceptibility Mapping
Li, Z., Li, J., Wang, C., Lu, Z., Wang, J., He, H., & Shi, J. (2021) · IEEE Transactions on Computational Imaging · doi:10.1109/tci.2021.3133692 · Cited by 8
Learn Less, Infer More: Learning in the Fourier Domain for Quantitative Susceptibility Mapping
He, J., Wang, L., Cao, Y., Wang, R., & Zhu, Y. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.837721 · Cited by 8
Quantitative Susceptibility Mapping in Cognitive Decline: A Review of Technical Aspects and Applications
Verma, S., Goel, T., & Tanveer, M. (2022) · Cognitive Computation · doi:10.1007/s12559-022-10095-3 · Cited by 7
How to accurately quantify brain magnetic susceptibility in the context of Parkinson's disease: Validation on phantoms and healthy volunteers at 1.5 and 3 T
Hervouin, A., Bézy‐Wendling, J., & Noury, F. (2024) · NMR in Biomedicine · doi:10.1002/nbm.5182 · Cited by 7
Thrombus magnetic susceptibility is associated with recanalization and clinical outcome in patients with ischemic stroke
Chen, J., Zhang, Z., Nie, X., Xu, Y., Liu, C., Zhao, X., Miao, Z., Wang, Y., & Liu, L. (2022) · NeuroImage Clinical · doi:10.1016/j.nicl.2022.103183 · Cited by 7
BlindHarmony: "Blind" Harmonization for MR Images via Flow model
Jeong, H., Byun, H., Kang, D. U., & Lee, J. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01932 · Cited by 8
Deep grey matter quantitative susceptibility mapping from small spatial coverages using deep learning
Zhu, X., Gao, Y., Liu, F., Crozier‬, ‪., & Sun, H. (2021) · Zeitschrift für Medizinische Physik · doi:10.1016/j.zemedi.2021.06.004 · Cited by 7
SIMPLEX: Multiple phase-cycled bSSFP quantitative magnetization transfer imaging with physic-guided simulation learning of neural network
Luu, H. M., & Park, S. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120449 · Cited by 6
A preliminary attempt to visualize nigrosome 1 in the substantia nigra for Parkinson’s disease at 3T: An efficient susceptibility map‐weighted imaging (SMWI) with quantitative susceptibility mapping using deep neural network (QSMnet)
Jo, M., & Oh, S. (2019) · Medical Physics · doi:10.1002/mp.13999 · Cited by 6
Deep Learning and Its Application to Function Approximation for MR in Medicine: An Overview
Takeshima, H. (2021) · Magnetic Resonance in Medical Sciences · doi:10.2463/mrms.rev.2021-0040 · Cited by 7
QSM Reconstruction Challenge 2.0: Design and Report of ResultsPreprint
Bilgiç, B., Langkammer, C., Marques, J. P., Meineke, J., Milovic, C., & Schweser, F. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.25.397695 · Cited by 6
Model resolution‐based deconvolution for improved quantitative susceptibility mapping
Mathew, R. S., Paluru, N., & Yalavarthy, P. K. (2023) · NMR in Biomedicine · doi:10.1002/nbm.5055 · Cited by 5
Patents on Quantitative Susceptibility Mapping (QSM) of Tissue Magnetism
Lin, F., Prince, M. R., Spincemaille, P., & Wang, Y. (2018) · Recent Patents on Biotechnology · doi:10.2174/1872208313666181217112745 · Cited by 5
Comparison of quantitative susceptibility mapping methods on evaluating radiation‐induced cerebral microbleeds and basal ganglia at 3T and 7T
Chen, Y., Genc, O., Poynton, C. B., Banerjee, S., Hess, C. P., & Lupo, J. (2022) · NMR in Biomedicine · doi:10.1002/nbm.4666 · Cited by 5
MRI Reconstruction with Conditional Adversarial Transformers
Korkmaz, Y., Özbey, M., & Çukur, T. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-17247-2_7 · Cited by 5
Quantitative susceptibility mapping using multi-channel convolutional neural networks with dipole-adaptive multi-frequency inputs
Si, W., Guo, Y., Zhang, Q., Zhang, J., Wang, Y., & Feng, Y. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1165446 · Cited by 5
Applications of Deep Learning to Magnetic Resonance Imaging (MRI)
Meça, A. (2023) · International Conference on Computing, Electronics & Communications Engineering (iCCECE) · doi:10.1109/iccece59400.2023.10238598 · Cited by 6

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: Gong, Enhao
Chatnuntawech, Itthi
Jung, Woojin
Setsompop, Kawin
Zaharchuk, Greg
Kim, Eung Yeop
Pauly, John
Lee, Jongho
Venue: NeuroImage
Publisher: Elsevier BV
Published: 2018-10
Volume: 179
Pages: 199-206
ISSN: 1053-8119 (ISSN-L)
DOI: 10.1016/j.neuroimage.2018.06.030
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
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Quantitative susceptibility mapping using deep neural network: QSMnet

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks