Calculation of susceptibility through multiple orientation sampling (COSMOS): A method for conditioning the inverse problem from measured magnetic field map to susceptibility source image in MRI

By Tian Liu, P. Spincemaille, L. de Rochefort, B. Kressler, Yi Wang. 2009

485 citations recorded by the source corpus.

Subfields: Radiology, Nuclear Medicine and Imaging, Spectroscopy

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Radiology, Nuclear Medicine and Imaging

1. Arvid Lundervold
2. Berkin Bilgic
3. Adolf Pfefferbaum
4. Edith V. Sullivan
5. Tian Liu
6. Elfar Adalsteinsson
7. Torsten Rohlfing
8. Chunlei Liu

In Spectroscopy

1. Arvid Lundervold

Citations in the corpus

• An overview of deep learning in medical imaging focusing on MRI (2018, methodology): “The authors generated training data by using the gold-standard for QSM: the so-called COSMOS method [147].”
• Toward in vivo histology: A comparison of quantitative susceptibility mapping (QSM) with magnitude-, phase-, and R2⁎-imaging at ultra-high magnetic field strength (2013, unknown): “Recently, a novel post-processingmethod, referred to as quantitative magnetic susceptibility mapping (QSM) (Liu et al., 2009; Schweser et al., 2011b; Shmueli et al., 2009;Wharton and Bowtell, 2010) has been introduced that overcomes the non-locality of the magnetic field distribution and provides qu”
• MRI Estimates of Brain Iron Concentration in Normal Aging Using Quantitative Susceptibility Mapping (2011, methodology): “This approach was introduced by Liu et al. (2009) and also investigated by others such as Wharton and Bowtell (2010) and Schweser et al. (2011b).”
• Nonlinear formulation of the magnetic field to source relationship for robust quantitative susceptibility mapping (2013, methodology): “Currently, unreliable phase values are either ignored by masking out (16) or approximated as contaminated by normally distributed random noise (6,7,14), leading to artifacts in the reconstructed QSM.”
• Morphology Enabled Dipole Inversion for Quantitative Susceptibility Mapping Using Structural Consistency Between the Magnitude Image and the Susceptibility Map (2011, unknown): “Previous approaches for overcoming the ill-posedness include sampling at multiple orientations (Liu et al., 2009), altering the susceptibility reconstruction by truncating the dipole kernel (Shmueli et al.”
• Quantitative Susceptibility Mapping of Human Brain Reflects Spatial Variation in Tissue Composition (2011, background): “A more effective but less efficient way to address the issue is to increase the number of sampling orientations by rotating the object in the scanner (Liu et al., 2009; Wharton et al., 2010).”
• A method for estimating and removing streaking artifacts in quantitative susceptibility mapping (2014, unknown): “Rochefort et al., 2010; Kressler et al., 2010; Li et al., 2011; Liu et al., 2009, 2011b; Schweser et al., 2011b; Shmueli et al., 2009; Wharton et al., 2010;Wu et al., 2012).”