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http://echo.ismrm.org/p/ISMRM2020/0158
0158
ISMRM Abstract
Multimodal quantitative arterial-venous segmentation of the human brain at 7T: structure, susceptibility and flow
Accepted
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1. [13] Y. P. Du, Z. Jin, Y. Hu, and J. Tanabe, “Multi-echo acquisition of MR angiography and venography of the brain at 3 Tesla,” J. Magn. Reson. Imaging, vol. 30, no. 2, pp. 449–454, Aug.
2. [9] N. Zaretskaya, B. Fischl, M. Reuter, V. Renvall, and J. R. Polimeni, “Advantages of cortical surface reconstruction using submillimeter 7 T MEMPRAGE,” Neuroimage, vol. 165, pp. 11–26, Jan.
3. [14] T. Dunas, A. Wahlin, K. Ambarki, L. Zarrinkoob, J. Malm, and A. Eklund, “A Stereotactic Probabilistic Atlas for the Major Cerebral Arteries,” Neuroinformatics, vol. 15, no. 1, pp. 101–110, Jan.
4. [4] A. A. Harteveld et al., “High-resolution postcontrast time-of-flight MR angiography of intracranial perforators at 7.0 Tesla.,” PLoS One, vol. 10, no. 3, p. e0121051, 2015. [5] A. H. Kuo, P. Nagpal, B. B. Ghoshhajra, and S. S. Hedgire, “Vascular magnetic resonance angiography techniques.,” Cardiovasc. Diagn. Ther., vol. 9, no. Suppl 1, pp. S28–S36, Aug.
5. [17] J. Huck et al., “High resolution atlas of the venous brain vasculature from 7 T quantitative susceptibility maps,” Brain Struct. Funct., vol. 224, no. 7, pp. 2467–2485, Sep.
6. [7] P. G. D. Ward et al., “Improved Quantification of Cerebral Vein Oxygenation Using Partial Volume Correction,” Front. Neurosci., vol. 11, p. 89, Feb.
7. [3] W. Chang et al., “Velocity measurements in the middle cerebral arteries of healthy volunteers using 3D radial phase-contrast HYPRFlow: comparison with transcranial Doppler sonography and 2D phase-contrast MR imaging.,” AJNR. Am. J. Neuroradiol., vol. 32, no. 1, pp. 54–9, Jan.
8. [15] R. Viviani, “A Digital Atlas of Middle to Large Brain Vessels and Their Relation to Cortical and Subcortical Structures.,” Front. Neuroanat., vol. 10, p. 12, 2016. [16] N. D. Forkert, J. Fiehler, S. Suniaga, H. Wersching, S. Knecht , and A. Kemmling, “A Statistical Cerebroarterial Atlas Derived from 700 MRA Datasets,” Methods Inf. Med., vol. 52, no. 6, pp. 467–474, Nov.
9. [8] A. F. Stalder, M. F. Russe, A. Frydrychowicz, J. Bock, J. Hennig, and M. Markl, “Quantitative 2D and 3D phase contrast MRI: Optimized analysis of blood flow and vessel wall parameters,” Magn. Reson. Med., vol. 60, no. 5, pp. 1218–1231, Nov.
10. [2] M.-C. Hsieh, C.-Y. Tsai, M.-C. Liao, J.-L. Yang, C.-H. Su, and J.-H. Chen, “Quantitative Susceptibility Mapping-Based Microscopy of Magnetic Resonance Venography (QSM-mMRV) for In Vivo Morphologically and Functionally Assessing Cerebromicrovasculature in Rat Stroke Model,” PLoS One, vol. 11, no. 3, p. e0149602, Mar.
11. [10] K. Eckstein et al., “Computationally Efficient Combination of Multi-channel Phase Data From Multi-echo Acquisitions (ASPIRE),” Magn. Reson. Med., vol. 79, no. 6, pp. 2996–3006, Jun.
12. [11] D. Polak et al., “Nonlinear Dipole Inversion (NDI) enables Quantitative Susceptibility Mapping (QSM) without parameter tuning,” Sep.
13. [12] E. M. Haacke, Y. Xu, Y.-C. N. Cheng, and J. R. Reichenbach, “Susceptibility weighted imaging (SWI),” Magn. Reson. Med., vol. 52, no. 3, pp. 612–618, Sep.
14. [6] H. J. Kuijf et al., “Quantification of deep medullary veins at 7 T brain MRI,” Eur. Radiol., vol. 26, no. 10, pp. 3412–3418, Oct.
15. [18] P. G. D. Ward et al., “Combining images and anatomical knowledge to improve automated vein segmentation in MRI,” Neuroimage, vol. 165, pp. 294–305, Jan. 2018.