References

When you use SEPIA in your research, please cite the method(s) that you used:

Brain extraction

FSL bet Smith, S. M. Fast robust automated brain extraction. Hum. Brain Mapp. 17, 143–155 (2002).

Phase unwrapping

Laplacian-based method Schofield, M. A. & Zhu, Y. Fast phase unwrapping algorithm for interferometric applications. Opt Lett 28, 1194–1196 (2003).

Li, W., Wu, B. & Liu, C. Quantitative susceptibility mapping of human brain reflects spatial variation in tissue composition. Neuroimage 55, 1645–1656 (2011).

3D best path Abdul-Rahman, H. S. et al. Fast and robust three-dimensional best path phase unwrapping algorithm. Applied Optics 46, 6623–6635 (2007).

Graphcut Dong, J. et al. Simultaneous phase unwrapping and removal of chemical shift (SPURS) using graph cuts: application in quantitative susceptibility mapping. IEEE Transactions on Medical Imaging 34, 531–540 (2015).

SEGUE Karsa and Shmueli. SEGUE: A Speedy rEgion-Growing Algorithm for Unwrapping Estimated Phase. IEEE Transactions on Medical Imaging 38, 1347-1357 (2018).

Echo phase combination - Optimum weights Robinson, S. D. et al. An illustrated comparison of processing methods for MR phase imaging and QSM: combining array coil signals and phase unwrapping. NMR Biomed 30, e3601 (2017).

Echo phase combination - MEDI nonlinear fit Liu, T. et al. Nonlinear formulation of the magnetic field to source relationship for robust quantitative susceptibility mapping. Magn Reson Med 69, 467–476 (2012).

ROMEO ROMEO Dymerska, B., Eckstein, K., Bachrata, B., Siow, B., Trattnig, S., Shmueli, K., Robinson, S.D., 2020. Phase Unwrapping with a Rapid Opensource Minimum Spanning TreE AlgOrithm (ROMEO). Magnetic Resonance in Medicine.

MCPC-3D-S Coil Combination: Eckstein, K., Dymerska, B., Bachrata, B., Bogner, W., Poljanc, K., Trattnig, S., Robinson, S.D., 2018. Computationally Efficient Combination of Multi-channel Phase Data From Multi-echo Acquisitions (ASPIRE). Magnetic Resonance in Medicine 79, 2996–3006.

Background field removal

LBV Zhou, D., Liu, T., Spincemaille, P. & Wang, Y. Background field removal by solving the Laplacian boundary value problem. NMR Biomed 27, 312–319 (2014).

PDF Liu, T. et al. A novel background field removal method for MRI using projection onto dipole fields (PDF). NMR Biomed 24, 1129–1136 (2011).

RESHARP Sun, H. & Wilman, A. H. Background field removal using spherical mean value filtering and Tikhonov regularization. Magn Reson Med 71, 1151–1157 (2014).

SHARP Schweser, F., Deistung, A., Lehr, B. W. & Reichenbach, J. R. Quantitative imaging of intrinsic magnetic tissue properties using MRI signal phase: an approach to in vivo brain iron metabolism? Neuroimage 54, 2789–2807 (2011).

VSHARP Li, W., Wu, B. & Liu, C. Quantitative susceptibility mapping of human brain reflects spatial variation in tissue composition. Neuroimage 55, 1645–1656 (2011).

iHARPERELLA Li, W., Avram, A. V., Wu, B., Xiao, X. & Liu, C. Integrated Laplacian-based phase unwrapping and background phase removal for quantitative susceptibility mapping. NMR Biomed 27, 219–227 (2014).

BFRnet Xuanyu Zhu, Yang Gao, Feng Liu, Stuart Crozier, Hongfu Sun, 2022. BFRnet: A deep learning-based MR background field removal method for QSM of the brain containing significant pathological susceptibility sources

QSM

TKD Wharton, S., Schäfer, A. & Bowtell, R. Susceptibility mapping in the human brain using threshold-based k-space division. Magn Reson Med 63, 1292–1304 (2010).

Shmueli, K. et al. Magnetic susceptibility mapping of brain tissue in vivo using MRI phase data. Magn Reson Med 62, 1510–1522 (2009).

Closed-form solution Bilgic, B. et al. Fast image reconstruction with L2‐regularization. J Magn Reson Imaging 40, 181–191 (2014).

LSQR Li, W., Wu, B. & Liu, C. Quantitative susceptibility mapping of human brain reflects spatial variation in tissue composition. Neuroimage 55, 1645–1656 (2011).

Star-QSM Wei, H. et al. Streaking artifact reduction for quantitative susceptibility mapping of sources with large dynamic range. NMR Biomed 28, 1294–1303 (2015).

Wei, H. et al. Imaging whole-brain cytoarchitecture of mouse with MRI-based quantitative susceptibility mapping. Neuroimage 137, 107–115 (2016).

Wei, H. et al. Investigating magnetic susceptibility of human knee joint at 7 Tesla. Magn Reson Med 78, 1933–1943 (2017).

FANSI Milovic, C., Bilgic, B., Zhao, B., Acosta-Cabronero, J. & Tejos, C. Fast nonlinear susceptibility inversion with variational regularization. Magn Reson Med 80, 814–821 (2018).

Bilgic, B. et al. Fast quantitative susceptibility mapping with L1‐regularization and automatic parameter selection. Magn Reson Med 72, 1444–1459 (2014).

Bilgic, B., Chatnuntawech, I., Langkammer, C. & Setsompop, K. Sparse methods for Quantitative Susceptibility Mapping. in (eds. Papadakis, M., Goyal, V. K. & Van De Ville, D.) 9597, 959711 (SPIE, 2015).

MEDI Liu, T. et al. Morphology enabled dipole inversion (MEDI) from a single-angle acquisition: Comparison with COSMOS in human brain imaging. Magn Reson Med 66, 777–783 (2011).

Liu, J. et al. Morphology enabled dipole inversion for quantitative susceptibility mapping using structural consistency between the magnitude image and the susceptibility map. Neuroimage 59, 2560–2568 (2012).

Liu, Z., Spincemaille, P., Yao, Y., Zhang, Y. & Wang, Y. MEDI+0: Morphology enabled dipole inversion with automatic uniform cerebrospinal fluid zero reference for quantitative susceptibility mapping. Magn Reson Med 79, 2795–2803 (2018).

NDI Polak D., Chatnuntawech I., Yoon J., Srinivasan Iyer S., Lee J., Setsompop K., and Bilgic B. VaNDI: Variational Nonlinear Dipole Inversion enables QSM without free parameters (program number 0319). Proceedings of the International Society for Magnetic Resonance in Medicine 2019, Montreal Canada

MRI Susceptibility Calculation Methods For the TKD software implementation, the following citation shall be included in the acknowledgements: Shmueli, K et al. (2009). Magnetic susceptibility mapping of brain tissue in vivo using MRI phase data, Magnetic Resonance in Medicine vol 62 issue 6, 1510-1522 and Schweser, F et al. (2013). Toward online reconstruction of quantitative susceptibility maps: superfast dipole inversion, Magnetic Resonance in Medicine vol 69 issue 6, 1581-1593.

For the dirTik and iterTik software implementations in the package, the following citation shall be included in the acknowledgements: Karsa, A et al. (2019). High Repeatability of Quantitative Susceptibility Mapping (QSM) in the Head and Neck With a View to Detecting Hypoxic Cancer Sites, In Proceedings of the 27th Annual Meeting of the ISMRM, Montreal, p. 4939 and Schweser, F et al. (2013). Toward online reconstruction of quantitative susceptibility maps: superfast dipole inversion, Magnetic Resonance in Medicine vol 69 issue 6, 1581-1593.

QSMnet+ J. Yoon, E. Gong, I. Chatnuntawech, B. Bilgic, J. Lee, W. Jung, J. Ko, H. Jung, K. Setsompop, G. Zaharchuk, E.Y. Kim, J. Pauly, J. Lee. Quantitative susceptibility mapping using deep neural network: QSMnet. Neuroimage. 2018 Oct;179:199-206.

W. Jung, J. Yoon, S. Ji, J. Choi, J. Kim, Y. Nam, E. Kim, J. Lee. Exploring linearity of deep neural network trained QSM: QSMnet+. Neuroimage. 2020 May; 116619.

W. Jung, S. Bollmann, J. Lee. Overview of quantitative susceptibility mapping using deep learning: Current status, challenges and opportunities. NMR in Biomedicine. 2020 Mar; e4292.

LP-CNN Kuo-Wei Lai, Manisha Aggarwal, Peter van Zijl, Xu Li & Jeremias Sulam, 2020. Learned Proximal Networks for Quantitative Susceptibility Mapping

xQSM Yang Gao, Xuanyu Zhu, Bradford A. Moffat, Rebecca Glarin, Alan H. Wilman, G. Bruce Pike, Stuart Crozier, Feng Liu, Hongfu Sun, 2020. xQSM: quantitative susceptibility mapping with octave convolutional and noise-regularized neural networks.

SWI/SMWI

CLEAR-SWI Eckstein, K., Bachrata, B., Hangel, G., Widhalm, G., Enzinger, C., Barth, M., Trattnig, S., Robinson, S.D., 2021. Improved susceptibility weighted imaging at ultra-high field using bipolar multi-echo acquisition and optimized image processing: CLEAR-SWI. Neuroimage 237, 118175–118175.

SMWI Gho, S.-M., Liu, C., Li, W., Jang, U., Kim, E.Y., Hwang, D., Kim, D.-H., 2014. Susceptibility map-weighted imaging (SMWI) for neuroimaging. Magnetic resonance in medicine 72, 337–346.