Fast, Precise Myelin Water Quantification using DESS MRI and Kernel Learning

Fast, Precise Myelin Water Quantification
using DESS MRI and Kernel Learning

Gopal Nataraj, Jon-Fredrik Nielsen, Mingjie Gao, and Jeffrey A. Fessler
Dept. of Electrical Engineering and Computer Science, University of Michigan
Dept. of Biomedical Engineering, University of Michigan


Submitted to Magnetic Resonance in Medicine

1 Introduction

2 Theory

3 Methods

4 Results

5 Discussion

6 Conclusion




  • [1] P. Morell. Myelin. Springer, 1984.
  • [2] M. M. Goldenberg. Multiple sclerosis review. Pharmacy and Therapeutics, 37(3):175–84, March 2012.
  • [3] V. Vasilescu, E. Katona, V. Simplaceanu, and D. Demco. Water compartments in the myelinated nerve. III. Pulsed NMR results. Experientia, 34(11):1443–4, November 1978.
  • [4] R. S. Menon and P. S. Allen. Application of continuous relaxation time distributions to the fitting of data from model systems and excised tissue. Mag. Res. Med., 20(2):214–27, August 1991.
  • [5] W. A. Stewart, A. L. Mackay, K. P. Whittall, G. R. W. Moore, and D. W. Paty. Spin-spin relaxation in experimental allergic encephalomyelitis. Analysis of CPMG data using a non-linear least-squares method and linear inverse theory. Mag. Res. Med., 29(6):767–75, June 1993.
  • [6] A. Mackay, K. Whittall, J. Adler, D. Li, D. Paty, and D. Graeb. In vivo visualization of myelin water in brain by magnetic resonance. Mag. Res. Med., 31(6):673–7, June 1994.
  • [7] P. J. Gareau, B. K. Rutt, S. J. Karlik, and J. R. Mitchell. Magnetization transfer and multicomponent T2 relaxation measurements with histopathologic correlation in an experimental model of MS. J. Mag. Res. Im., 11(6):586–95, June 2000.
  • [8] S. Webb, C. A. Munro, R. Midha, and G. J. Stanisz. Is multicomponent T2 a good measure of myelin content in peripheral nerve? Mag. Res. Med., 49(4):628–45, April 2003.
  • [9] C. Laule, I. M. Vavasour, G. R. W. Moore, J. Oger, D. K. B. Li, D. W. Paty, and A. L. MacKay. Water content and myelin water fraction in multiple sclerosis. J. Neurol., 251(3):284–93, March 2004.
  • [10] C. Laule, E. Leung, D. K. B. Li, A. L. Traboulsee, D. W. Paty, A. L. MacKay, and G. R. W. Moore. Myelin water imaging in multiple sclerosis: quantitative correlations with histopathology. Multiple Sclerosis J., 12(6):747–53, November 2006.
  • [11] H. Y. Carr and E. M. Purcell. Effects of diffusion on free precession in nuclear magnetic resonance experiments. Phys. Rev., 94(3):630–8, May 1954.
  • [12] M. D. Does and J. C. Gore. Rapid acquisition transverse relaxometric imaging. J. Mag. Res., 147(1):116–20, November 2000.
  • [13] T. Prasloski, A. Rauscher, A. L. MacKay, M. Hodgson, I. M. Vavasour, C. Laule, and B. Mädler. Rapid whole cerebrum myelin water imaging using a 3D GRASE sequence. NeuroImage, 63(1):533–9, October 2012.
  • [14] D. A. Feinberg and K. Oshio. GRASE (gradient- and spin-echo) MR imaging: a new fast clinical imaging technique. Radiology, 181(2):597–602, November 1991.
  • [15] E. Alonso-Ortiz, I. R. Levesque, and G. B. Pike. MRI-based myelin water imaging: A technical review. Mag. Res. Med., 73(1):70–81, January 2015.
  • [16] M. D. Does. Inferring brain tissue composition and microstructure via MR relaxometry. J. Neuroimag., 2018.
  • [17] S. C. L. Deoni, B. K. Rutt, T. Arun, C. Pierpaoli, and D. K. Jones. Gleaning multicomponent T1 and T2 information from steady-state imaging data. Mag. Res. Med., 60(6):1372–87, December 2008.
  • [18] S. C. L. Deoni. Correction of main and transmit magnetic field (B0 and B1) inhomogeneity effects in multicomponent-driven equilibrium single-pulse observation of T1 and T2. Mag. Res. Med., 65(4):1021–35, April 2011.
  • [19] S. C. L. Deoni, L. Matthews, and S. H. Kolind. One component? Two components? Three? The effect of including a nonexchanging ”free” water component in multicomponent driven equilibrium single pulse observation of T1 and T2. Mag. Res. Med., 70(1):147–54, July 2013.
  • [20] J. Zhang, S. H. Kolind, C. Laule, and A. L. MacKay. Comparison of myelin water fraction from multiecho T2 decay curve and steady-state methods. Mag. Res. Med., 73(1):223–32, January 2015.
  • [21] C. L. Lankford and M. D. Does. On the inherent precision of mcDESPOT. Mag. Res. Med., 69(1):127–36, January 2013.
  • [22] G. Nataraj, J-F. Nielsen, and J. A. Fessler. Optimizing MR scan design for model-based T1, T2 estimation from steady-state sequences. IEEE Trans. Med. Imag., 36(2):467–77, February 2017.
  • [23] Y. Zur, M. L. Wood, and L. J. Neuringer. Spoiling of transverse magnetization in steady-state sequences. Mag. Res. Med., 21(2):251–63, October 1991.
  • [24] T. W. Redpath and R. A. Jones. FADE-A new fast imaging sequence. Mag. Res. Med., 6(2):224–34, February 1988.
  • [25] H. Bruder, H. Fischer, R. Graumann, and M. Deimling. A new steady-state imaging sequence for simultaneous acquisition of two MR images with clearly different contrasts. Mag. Res. Med., 7(1):35–42, May 1988.
  • [26] G. Nataraj, J-F. Nielsen, and J. A. Fessler. Dictionary-free MRI parameter estimation via kernel ridge regression. In Proc. IEEE Intl. Symp. Biomed. Imag., pages 5–9, 2017.
  • [27] G. Nataraj, J-F. Nielsen, and J. A. Fessler. Myelin water fraction estimation from optimized steady-state sequences using kernel ridge regression. In Proc. Intl. Soc. Mag. Res. Med., page 5076, 2017.
  • [28] G. Nataraj, J-F. Nielsen, C. D. Scott, and J. A. Fessler. Dictionary-free MRI PERK: Parameter estimation via regression with kernels. IEEE Trans. Med. Imag., 37(9):2103–14, September 2018.
  • [29] G. Nataraj. Advances in quantitative MRI: acquisition, estimation, and application. PhD thesis, Univ. of Michigan, Ann Arbor, MI, 48109-2122, Ann Arbor, MI, 2018.
  • [30] R. G. Spencer and K. W. Fishbein. Measurement of spin-lattice relaxation times and concentrations in systems with chemical exchange using the one-pulse sequence: breakdown of the Ernst model for partial saturation in nuclear magnetic resonance spectroscopy. J. Mag. Res., 142(1):120–35, January 2000.
  • [31] S. C. L. Deoni, B. K. Rutt, and D. K. Jones. Investigating exchange and multicomponent relaxation in fully-balanced steady-state free precession imaging. J. Mag. Res. Im., 27(6):1421–9, June 2008.
  • [32] R. D. Gill and B. Y. Levit. Applications of the van Trees inequality: A Bayesian Cramér-rao bound. Bernoulli, 1(1/2):59–79, 1995.
  • [33] M. Akcakaya, S. Weingartner, Sebastien Roujol, and R. Nezafat. On the selection of sampling points for myocardial T1 mapping. Mag. Res. Med., 73(5):1741–53, May 2015.
  • [34] C. M. Lewis, S. A. Hurley, M. E. Meyerand, and C. G. Koay. Data-driven optimized flip angle selection for T1 estimation from spoiled gradient echo acquisitions. Mag. Res. Med., 76(3):792–802, September 2016.
  • [35] H. Cramer. Mathematical methods of statistics. Princeton Univ. Press, Princeton, 1946.
  • [36] P. Virtue, S. X. Yu, and M. Lustig. Better than real: Complex-valued neural nets for MRI fingerprinting. In Proc. IEEE Intl. Conf. on Image Processing, pages 3953–7, 2017.
  • [37] O. Cohen, B. Zhu, and M. S. Rosen. MR fingerprinting Deep RecOnstruction NEtwork (DRONE). Mag. Res. Med., 80(3):885–94, September 2018.
  • [38] G. Nataraj, M. Gao, J. Asslander, C. Scott, and J. A. Fessler. Shallow learning with kernels for dictionary-free magnetic resonance fingerprinting. In ISMRM Workshop on MR Fingerprinting, 2017.
  • [39] H. Gudbjartsson and S. Patz. The Rician distribution of noisy MRI data. Mag. Res. Med., 34(6):910–4, December 1995.
  • [40] L. I. Sacolick, F. Wiesinger, I. Hancu, and M. W. Vogel. B1 mapping by Bloch-Siegert shift. Mag. Res. Med., 63(5):1315–22, May 2010.
  • [41] H. Sun, W. A. Grissom, and J. A. Fessler. Regularized estimation of Bloch-Siegert B1+ Maps in MRI. In Proc. IEEE Intl. Conf. on Image Processing, pages 3646–50, 2014.
  • [42] J. P. Wansapura, S. K. Holland, R. S. Dunn, and W. S. Ball. NMR relaxation times in the human brain at 3.0 Tesla. J. Mag. Res., 9(4):531–8, April 1999.
  • [43] D. L. Collins, A. P. Zijdenbos, V. Kollokian, J. G. Sled, N. J. Kabani, C. J. Holmes, and A. C. Evans. Design and construction of a realistic digital brain phantom. IEEE Trans. Med. Imag., 17(3):463–8, June 1998.
  • [44] J-F. Nielsen and D. C. Noll. TOPPE: A framework for rapid prototyping of MR pulse sequences. Mag. Res. Med., 79(6):3128–34, June 2018.
  • [45] J. Pauly, P. Le Roux, D. Nishimura, and A. Macovski. Parameter relations for the Shinnar-Le Roux selective excitation pulse design algorithm. IEEE Trans. Med. Imag., 10(1):53–65, March 1991.
  • [46] L. Ying and J. Sheng. Joint image reconstruction and sensitivity estimation in SENSE (JSENSE). Mag. Res. Med., 57(6):1196–1202, June 2007.
  • [47] G. Golub and V. Pereyra. Separable nonlinear least squares: the variable projection method and its applications. Inverse Prob., 19(2):R1–26, April 2003.
  • [48] V. V. Itskovich, D. D. Samber, V. Mani, J. G. S. Aguinaldo, J. T. Fallon, C. Y. Tang, V. Fuster, and Z. A. Fayad. Quantification of human atherosclerotic plaques using spatially enhanced cluster analysis of multicontrast-weighted magnetic resonance images. Mag. Res. Med., 52(3):515–23, September 2004.
  • [49] R. Symons, T. E. Cork, M. N. Lakshmanan, R. Evers, C. Davies-Venn, K. A. Rice, M. L. Thomas, C-Y. Liu, S. Kappler, S. Ulzheimer, V. Sandfort, D. A. Bluemke, and A. Pourmorteza. Dual-contrast agent photon-counting computed tomography of the heart: initial experience. Int. J. Cardiovasc. Imaging, 33(8):1253–61, August 2017.
  • [50] A. S. Shatil, M. N. Uddin, K. M. Matsuda, and C. R. Figley. Quantitative ex vivo MRI changes due to progressive formalin fixation in whole human brain specimens: longitudinal characterization of diffusion, relaxometry, and myelin water fraction measurements at 3T. Frontiers in Medicine, 5(31):1–15, February 2018.
  • [51] S. Ahn and J. A. Fessler. Standard errors of mean, variance, and standard deviation estimators. Technical Report 413, Comm. and Sign. Proc. Lab., Dept. of EECS, Univ. of Michigan, Ann Arbor, MI, 48109-2122, July 2003.
  • [52] N. Aronszajn. Theory of reproducing kernels. Trans. Amer. Math. Soc., 68(3):337–404, May 1950.
  • [53] I. Steinwart and A. Christmann. Support vector machines. Springer, 2008.
  • [54] A. Rahimi and B. Recht. Random features for large-scale kernel machines. In NIPS, 2007.
  • [55] M. A. Woodbury. Inverting modified matrices, 1950. Tech. Report 42, Stat. Res. Group, Princeton Univ.
  • [56] G. Nataraj, J-F. Nielsen, M. Gao, and J. A. Fessler. Fast, precise myelin water quantification using DESS MRI and kernel learning, 2018. In preparation.
  • [57] M. M. Siddiqui. Statistical inference for Rayleigh distributions. RADIO SCIENCE Journal of Research NBS/USNC-URSI, 68D(9):1005–10, September 1964.
  • [58] D. Arthur and S. Vassilvitskii. K-means++: The advantages of careful seeding. In Proc. 18th Annual ACM-SIAM Symp. Disc. Alg. (SODA), pages 1027–35, 2007.
  • [59] C. L. Lawson and R. J. Hanson. Solving least squares problems. Prentice-Hall, 1974.
  • [60] R. M. Kroeker and R. M. Henkelman. Analysis of biological NMR relaxation data with continuous distributions of relaxation data. J. Mag. Res., 69(2):218–35, September 1986.
  • [61] K. P. Whittall and A. L. MacKay. Quantitative interpretation of NMR relaxation data. J. Mag. Res., 84(1):134–52, August 1989.
  • [62] T. Prasloski, B. Mädler, Q-S. Xiang, A. MacKay, and C. Jones. Applications of stimulated echo correction to multicomponent T2 analysis. Mag. Res. Med., 67(6):1803–14, June 2012.
  • [63] J. Hennig. Multiecho imaging sequences with low refocusing flip angles. J. Mag. Res., 88(3):397–407, July 1988.

Supporting Information for

Fast, Precise Myelin Water Quantification

using DESS MRI and Kernel Learning

Gopal Nataraj, Jon-Fredrik Nielsen, Mingjie Gao, and Jeffrey A. Fessler

Dept. of Electrical Engineering and Computer Science, University of Michigan

Dept. of Biomedical Engineering, University of Michigan

This supplement elaborates upon methodology details and presents additional results that were excluded from the main body of the manuscript [56] due to word limits. §S-I details our implementations of PERK and three other estimators used in myelin water imaging experiments. §S-II describes additional simulation studies that investigate reasons for differences between the conventional and proposed myelin water imaging methods. §S-III discusses additional advantages demonstrated by these extended simulations.

Appendix S-I Parameter Estimation Implementation Details

Appendix S-II Extensions to Simulation Studies

Appendix S-III Further Discussion

Comments 0
Request Comment
You are adding the first comment!
How to quickly get a good reply:
  • Give credit where it’s due by listing out the positive aspects of a paper before getting into which changes should be made.
  • Be specific in your critique, and provide supporting evidence with appropriate references to substantiate general statements.
  • Your comment should inspire ideas to flow and help the author improves the paper.

The better we are at sharing our knowledge with each other, the faster we move forward.
The feedback must be of minimum 40 characters and the title a minimum of 5 characters
Add comment
Loading ...
This is a comment super asjknd jkasnjk adsnkj
The feedback must be of minumum 40 characters
The feedback must be of minumum 40 characters

You are asking your first question!
How to quickly get a good answer:
  • Keep your question short and to the point
  • Check for grammar or spelling errors.
  • Phrase it like a question
Test description