High-quality multiple T2(*) contrast MR images from low-quality multi-echo images using temporal-domain denoising methods

Medical Physics

Volumn 39, Issue 1, 2012, Pages 468-474

  Jang, Ung ^a   Hwang, Dosik ^a  

^a Yonsei University   (South Korea)

Author keywords

low pass filter; magnetic resonance imaging; multiple contrasts; noise reduction; non negative least squares algorithm; relaxation model; temporal filtering

Indexed keywords

BIOMEDICAL SIGNAL PROCESSING; IMAGE ACQUISITION; LOW PASS FILTERS; MAGNETIC RESONANCE IMAGING; MEAN SQUARE ERROR; MEDIAN FILTERS; NOISE ABATEMENT; PIXELS; SIGNAL TO NOISE RATIO;

ANISOTROPIC DIFFUSION FILTERS; CONTRAST TO NOISE RATIO; ECHO MAGNETIC RESONANCE; LEAST SQUARES ALGORITHM; MULTIPLE CONTRASTS; POSTPROCESSING METHODS; RELAXATION MODELS; TEMPORAL FILTERING;

IMAGE DENOISING;

EID: 84855420371     PISSN: 00942405     EISSN: None     Source Type: Journal    
DOI: 10.1118/1.3671934     Document Type: Article

References (23)

1. A. M. Wink and J. B. Roderick, Denoising functional MR images: A comparison of wavelet denoising and Gaussian smoothing., IEEE Trans. Med. Imaging 23, 374-387 (2004). 10.1109/TMI.2004.824234
2. K. Ying, B. D. Clymer, and P. Schmalbrock, Adaptive filtering for high resolution magnetic resonance images., J. Magn. Reson. Imaging 6, 367-377 (1996). 10.1002/jmri.v6:2
3. A. A. Samsonov and C. R. Johnson, Noise-adaptive nonlinear diffusion filtering of MR images with spatially varying noise levels., Magn. Reson. Med. 52, 798-806 (2004). 10.1002/mrm.v52:4
4. 2 relaxation at 3 tesla-A preliminary post-mortem study., Magn. Reson. Med. 58, 865-870 (2007). 10.1002/mrm.v58:5
5. 2 decay., Neuroimage 52, 198-204 (2010). 10.1016/j.neuroimage.2010.04.023
6. V. Brainovicha, U. Sabatinib, and G. E. Hagberga, Advantages of using multiple-echo image combination and asymmetric triangular phase masking in magnetic resonance venography at 3 T., Magn. Reson. Imaging 27, 23-37 (2009). 10.1016/j.mri.2008.05.006
7. 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 30, 449-454 (2009). 10.1002/jmri.21833
8. U. Dorenbeck, A. G. Schreyer, J. Schlaier, P. Held, S. Feuerbach, and J. Seitz, Degenerative disease of the cervical spine: Comparison of a multiecho data image combination sequence with a magnetization transfer saturation pulse and cervical myelography and CT., Neuroradiology 46, 306-309 (2004). 10.1007/s00234-004-1175-5
9. M. R. Schmid, C. W. A. Pfirrmann, P. Koch, M. Zanetti, B. Kuehn, and J. Hodler, Imaging of patellar cartilage with a 2D multiple-echo data image combination sequence., AJR, Am. J. Roentgenol. 184, 1744-1748 (2005).
10. M.A. Griswold, P. M. Jakob, R. M. Heidemann, M. Nittka, V. Jellus, J. Wang, B. Kiefer, and A. Haase, Generalized autocalibrating partially parallel acquisition (GRAPPA)., Magn. Reson. Med. 47, 1202-1210 (2002). 10.1002/mrm.v47:6
11. 2 in rat brain and trigeminal nerve in vivo., Magn. Reson. Med. 47, 274-283 (2002). 10.1002/mrm.v47:2
12. J. L. Lancaster, T. Andrews, L. J. Hardies, S. Dodd, P. T. Fox, Three-pool model of white matter., J. Magn. Reson. Imaging. 17, 1-10 (2003). 10.1002/jmri.v17:1
13. 2 relaxometry., Magn. Reson. Med. 54, 449-454 (2005). 10.1002/mrm.v54:2
14. 2 components of amphibian peripheral nerve to their microanatomical compartments., Magn. Reson. Med. 47, 239-245 (2002). 10.1002/mrm.v47:2
15. 2 analysis of dithiocarbamate-mediated peripheral nerve demyelination., Neurotoxicology 28, 645-654 (2007). 10.1016/j.neuro.2007.01.009
16. D. S. Hwang and Y. P. Du, Improved myelin water quantification using spatially regularized non-negative least squares algorithm., J. Magn. Reson. Imaging 30, 203-208 (2009). 10.1002/jmri.21783
17. C. Laule, E. Leung, D. K. Li, A. L. Traboulsee, D. W. Paty, A. L. MacKay, and G. R. Moore, Myelin water imaging in multiple sclerosis: Quantitative correlations with histopathology., Mult. Scler. 12, 747-753 (2006). 10.1177/1352458506070928
18. C. L. Lawson and R. J. Hanson, Solving Least Squares Problems (Prentice-Hall, Englewood Cliffs, New Jersey, 1974).
19. S. W. Provencher, A constrained regularization method for inverting data represented by linear algebraic or integral equations., Comput. Phys. 27, 213-227 (1982). 10.1016/0010-4655(82)90173-4
20. C. R. Haider, H. H. Hu, N. G. Campeau, J. Huston III, and S. J. Riederer, 3D high temporal and spatial resolution contrast enhanced MR angiography of the whole brain., Magn. Reson. Med. 60, 749-760 (2008). 10.1002/mrm.21675
21. D. A. Yablonskiy, Quantitation of intrinsic magnetic susceptibility related effects in a tissue matrix: Phantom study., Magn. Reson. Med. 39, 417-428 (1998). 10.1002/mrm.v39:3
22. R. Wirestam, O. Thilmann, L. Knutsson, I. M. Bjorkman-Burtscher, E. M. Larsson, and F. Stahlberg, Comparison of quantitative dynamic susceptibility-contrast MRI perfusion estimates obtained using different contrast-agent administration schemes at 3T., Eur. J. Radiol. 75, e86-e91 (2010). 10.1016/j.ejrad.2009.07.038
23. 2 measurements., Magn. Reson. Med. 44, 358-366 (2000). 10.1002/1522-2594(200009)44:3<>1.0. CO;2-Q