The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Whole brain susceptibility mapping using compressed sensing
Open accessAbstract
The derivation of susceptibility from image phase is hampered by the ill‐conditioned filter inversion in certain k‐space regions. In this article, compressed sensing is used to compensate for the k‐space regions where direct filter inversion is unstable. A significantly lower level of streaking artifacts is produced in the resulting susceptibility maps for both simulated and in vivo data sets compared to outcomes obtained using the direct threshold method. It is also demonstrated that the compressed sensing based method outperforms regularization based methods. The key difference between the regularized inversions and compressed sensing compensated inversions is that, in the former case, the entire k‐space spectrum estimation is affected by the ill‐conditioned filter inversion in certain k‐space regions, whereas in the compressed sensing based method only the ill‐conditioned k‐space regions are estimated. In the susceptibility map calculated from the phase measurement obtained using a 3T scanner, not only are the iron‐rich regions well depicted, but good contrast between white and gray matter interfaces that feature a low level of susceptibility variations are also obtained. The correlation between the iron content and the susceptibility levels in iron‐rich deep nucleus regions is studied, and strong linear relationships are observed which agree with previous findings. Magn Reson Med, 2011. © 2011 Wiley‐Liss, Inc.
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Sparse MRI: The application of compressed sensing for rapid MR imaging
Lustig, M., Donoho, D., & Pauly, J. M. (2007) · Magnetic Resonance in Medicine · doi:10.1002/mrm.21391 · Cited by 6,952
Lustig, M., Donoho, D. L., Santos, J. M., & Pauly, J. M. (2008) · IEEE Signal Processing Magazine · doi:10.1109/msp.2007.914728 · Cited by 2,246
Sparsity and incoherence in compressive sampling
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Susceptibility weighted imaging (SWI)
Haacke, E. M., Xu, Y., Cheng, Y. N., & Reichenbach, J. R. (2004) · Magnetic Resonance in Medicine · doi:10.1002/mrm.20198 · Cited by 1,582
The effect of age on the non-haemin iron in the human brain
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Susceptibility-Weighted Imaging: Technical Aspects and Clinical Applications, Part 1
Haacke, E. M., Mittal, S., Wu, Z., Neelavalli, J., & Cheng, Y.-C. N. (2009) · American Journal of Neuroradiology · doi:10.3174/ajnr.a1400 · Cited by 1,113
Cited by
Quantitative susceptibility mapping (QSM): Decoding MRI data for a tissue magnetic biomarker
Wang, Y., & Liu, T. (2015) · Magnetic Resonance in Medicine · doi:10.1002/mrm.25358 · Cited by 878
Langkammer, C., Schweser, F., Krebs, N., Deistung, A., Goessler, W., Scheurer, E., Sommer, K., Reishofer, G., Yen, K., Fazekas, F., et al. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.05.049 · Cited by 801
Pros and cons of ultra-high-field MRI/MRS for human application
Ladd, M. E., Bachert, P., Meyerspeer, M., Moser, E., Nagel, A. M., Norris, D. G., Schmitter, S., Speck, O., Straub, S., & Zaiss, M. (2018) · Progress in Nuclear Magnetic Resonance Spectroscopy · doi:10.1016/j.pnmrs.2018.06.001 · Cited by 544
Quantitative susceptibility mapping: current status and future directions
Haacke, E. M., Liu, S., Buch, S., Zheng, W., Wu, D., & Ye, Y. (2015) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2014.09.004 · Cited by 511
Liu, C., Li, W., Tong, K. A., Yeom, K. W., & Kuzminski, S. (2015) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.24768 · Cited by 503
Self-citation
Metadata
- Authors
- Wu, B., Li, W., Guidon, A., & Liu, C.
- Venue
- Magnetic Resonance in Medicine
- Publisher
- Wiley
- Published
- 2011-06-10
- Volume
- 67
- Issue
- 1
- Pages
- 137-147
- ISSN
- 0740-3194 (ISSN-L)
- 1522-2594
- OpenAlex
- W2152166807
- Cited by
- 417
- References
- 36
- FWCI
- 11.72
- Type
- journal article
- Language
- en
- Open access
- green
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