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. `_