Using spatial prior knowledge in the spectral fitting of MRS images

NMR in Biomedicine

Volumn 25, Issue 1, 2012, Pages 1-13

  Kelm, B Michael ^a,b   Kaster, Frederik O ^a,c   Henning, Anke ^d   Weber, Marc André ^c,e   Bachert, Peter ^c   Boesiger, Peter ^d   Hamprecht, Fred A ^a   Menze, Bjoern H ^a,f,g  

^a UNIVERSITY OF HEIDELBERG   (Germany)

^b SIEMENS AG   (Germany)

^c GERMAN CANCER RESEARCH CENTER DKFZ   (Germany)

^d ETH ZURICH   (Switzerland)

^e UNIVERSITY HOSPITAL HEIDELBERG   (Germany)

^f INRIA   (France)

^g MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

MRSI; Quantification; Spatial prior knowledge; Spectral fitting

Indexed keywords

BRAIN TUMORS; ESTIMATED PARAMETER; FREE INDUCTION DECAY; FREQUENCY DOMAINS; GAUSSIAN MARKOV RANDOM FIELD; HIGH SPATIAL RESOLUTION; LOWER BOUNDS; MONTE CARLO STUDY; MRSI; OVERLAPPING PEAKS; PHASE MAPS; PRIOR KNOWLEDGE; QUANTIFICATION; SIGNAL TO NOISE; SIMULATED DATA; SINGLE VOXEL; SPATIAL FEATURES; SPATIAL PRIORS; SPECTRAL FITTING;

MEDICINE; SPECTROSCOPY;

SIGNAL TO NOISE RATIO;

CHOLINE; CREATINE;

ARTICLE; BRAIN MAPPING; BRAIN TUMOR; HUMAN; KNOWLEDGE; MONTE CARLO METHOD; NEUROIMAGING; NUCLEAR MAGNETIC RESONANCE IMAGING; NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY; PRIORITY JOURNAL; SIGNAL NOISE RATIO; VARIANCE;

BRAIN NEOPLASMS; COMPUTER SIMULATION; DATABASES AS TOPIC; HUMANS; KNOWLEDGE; MAGNETIC RESONANCE IMAGING; MAGNETIC RESONANCE SPECTROSCOPY; MODELS, BIOLOGICAL;

EID: 84855910304     PISSN: 09523480     EISSN: 10991492     Source Type: Journal    
DOI: 10.1002/nbm.1704     Document Type: Article

References (45)

1. Schrder L, Lowery TJ, Hilty C, Wemmer DE, Pines A. Molecular imaging using a targeted magnetic resonance hyperpolarized biosensor. Science, 2006; 314: 446-449.
2. 1H-magnetic resonance spectroscopy and a gallery of artifacts. NMR Biomed. 2004; 17: 361-381.
3. Menze BH, Kelm BM, Weber MA, Bachert P, Hamprecht FA. Mimicking the human expert: pattern recognition for an automated assessment of data quality in MRSI. Magn. Reson. Med. 2008; 59: 1457-1466.
4. 1H spectroscopic imaging analysis. Magn. Reson. Med. 2004; 52: 1246-1254.
5. Vanhamme L, van den Boogaart A, Van Huffel S. Improved method for accurate and efficient quantification of MRS data with use of prior knowledge. J. Magn. Reson. 1997; 129: 35-43.
6. 1H MR spectra of brain using AMARES. NMR Biomed. 1998; 11: 32-39.
7. Cavassila S, Deval S, Huegen C, van Ormondt D, Graveron-Demilly D. Cramér-Rao bounds: an evaluation tool for quantitation. NMR Biomed. 2001; 14: 278-283.
8. Soher BJ, Young K, Govindaraju V, Maudsley AA. Automated spectral analysis III: application to in vivo proton MR spectroscopy and spectroscopic imaging. Magn. Reson. Med. 1998; 40: 822-831.
9. 1H spectra with LCModel. NMR Biomed. 2001; 14: 260-264.
10. Ratiney H, Sdika M, Coenradie Y, Cavassila S, van Ormondt D, Graveron-Demilly D. Time-domain semi-parametric estimation based on a metabolite basis set. NMR Biomed. 2005; 18: 1-13.
11. Poullet JB, Sima DM, Simonetti AW, De Neuter B, Vanhamme L, Lemmerling P, Van Huffel S. An automated quantitation of short echo time MRS spectra in an open source software environment: AQSES. NMR Biomed. 2006; 20: 493-504.
12. Sima DM, Van Huffel S. Regularized semiparametric model identification with application to nuclear magnetic resonance signal quantification with unknown macromolecular base-line. J. R. Stat. Soc. B: Met. 2006; 68: 383-409.
13. Schulte R, Boesiger P. ProFit: two-dimensional prior-knowledge fitting of J-resolved spectra. NMR Biomed. 2006; 19: 255-263.
14. Hu X, Levin DN, Lauterbur PC, Spraggins T. SLIM: Spectral localization by imaging. Magn. Reson. Med. 1988; 8: 314-322.
15. Liang ZP, Lauterbur PC. A generalized series approach to MR spectroscopic imaging. IEEE T. Med. Imaging, 1991; 10: 132-137.
16. Plevritis SK, Macovski A. MRS imaging using anatomically based k-space sampling and extrapolation. Magn. Reson. Med. 1995; 34: 686-693.
17. Bao Y, Maudsley AA. Improved reconstruction for MR spectroscopic imaging. IEEE T. Med. Imaging, 2007; 26: 686-695.
18. Haldard JP, Hernando D, Song SK, Liang ZP. Anatomically constrained reconstruction from noisy data. Magn. Reson. Med. 2008; 59: 810-818.
19. Kornak J, Young K, Soher BJ, Maudsley AA. Bayesian k-space-time reconstruction of MR spectroscopic imaging for enhanced resolution. IEEE T. Med. Imaging, 2010; 29: 1333-1350.
20. Gindi G, Lee M, Rangarajan A, Zubal IG. Bayesian reconstruction of functional images using anatomical information as priors. IEEE T. Med. Imaging, 1993; 12: 670-680.
21. Baillet S, Garnero L. A Bayesian approach to introducing anatomo-functional priors in the EEG/MEG inverse problem. IEEE T. Bio-Med. Eng. 1997; 44: 374-385.
22. Kelm BM, Menze BH, Nix O, Zechmann C, Hamprecht FA. Estimating kinetic parameter maps from dynamic contrast-enhanced MRI using spatial prior knowledge. IEEE T. Med. Imaging, 2009; 28: 1534-1547.
23. Kelm BM, Muller N, Menze BH, Hamprecht FA. Bayesian estimation of smooth parameter maps for dynamic contrast-enhanced MR images with Block-ICM. Proceedings of the 7th IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA), New York, USA, 2006; 96-103.
24. Schmid V, Whitcher B, Padhani A, Taylor N, Yang G. Bayesian methods for pharmacokinetic models in dynamic contrast-enhanced magnetic resonance imaging. IEEE T. Med. Imaging, 2006; 25: 1627-1636.
25. Kelm BM. Evaluation of vector-valued clinical image data using probabilistic graphical models: quantification and pattern recognition. PhD Thesis, University of Heidelberg, 2007.
26. Provencher S. LCModel & LCMgui User's Manual, 6.2 edn., 2010. Accessed 15 November 2010.
27. Bretthorst GL. Automatic phasing of MR images. Part I: linearly varying phase. J. Magn. Reson. 2008; 191: 184-192.
28. Sava AC, Sima DM, Poullet JB, Wright AJ, Heerschap A, Huffel SV. Exploiting spatial information to estimate metabolite levels in 2D MRSI of heterogeneous brain lesions. Internal Report 09-182. ESAT-SISTA, K.U. Leuven: Leuven; 2009.
29. Smith SA, Levante TO, Meier BH, Ernst RR. Computer simulations in magnetic resonance. An object-oriented programming approach. J. Magn. Reson. Ser. A, 1994; 106: 75-105.
30. Besag JE. On the statistical analysis of dirty pictures. J. R. Stat. Soc. B: Met. 1986; 48: 259-302.
31. Bouman CA, Sauer K. A unified approach to statistical tomography using coordinate descent optimization. IEEE T. Image Process. 1996; 5: 480-492.
32. Winkler G. Image Analysis, Random Fields and Dynamic Monte Carlo Methods. Springer: Berlin; 2003.
33. Wu Z, Chung HW, Wehrli FW. A Bayesian approach to subvoxel tissue classification in NMR microscopic images of trabecular bone. Magn. Reson. Med. 1994; 3: 302-308.
34. Toselli A, Widlund O. Domain Decomposition Methods - Algorithms and Theory. Springer: Berlin; 2005.
35. Trottenberg U, Oosterlee CW, Schuller A. Multigrid. Academic Press: San Diego, CA; 2000.
36. Weber MA, Zoubaa S, Schlieter M, Juttler E, Huttner HB, Geletneky K, Ittrich C, Lichy MP, Kroll A, Debus J, Giesel FL, Hartmann M, Essig M. Diagnostic performance of spectroscopic and perfusion MRI for distinction of brain tumors. Neurology, 2006; 66: 1899-1906.
37. Henning A, Schar M, Schulte RF, Wilm B, Pruessmann KP, Boesiger P. SELOVS: Brain MRSI localization based on highly selective T1- and B1-insensitive outer-volume suppression at 3T. Magn. Reson. Med. 2008; 59: 40-51.
38. Kelm BM, Menze BH, Neff T, Zechmann C, Hamprecht FA. CLARET: a tool for fully automated evaluation of MRSI with pattern recognition methods. Proceedings of Bildverarbeitung fur die Medizin (BVM), Hamburg, Germany, 2006; 51-55.
39. Naressi A, Couturier C, Castang I, de Beer R, Graveron-Demilly D. Java-based graphical user interface for MRUI, a software package for quantitation of in vivo/medical magnetic resonance spectroscopy signals. Comput. Biol. Med. 2001; 31: 269-286.
40. Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning. Springer: New York; 2001.
41. Sima DM, Sava AC, Huffel SV. Adaptive alternating minimization for fitting magnetic resonance spectroscopic imaging signals. Internal Report 09-232. ESAT-SISTA, K.U. Leuven: Leuven; 2009.
42. Nelson SJ. Analysis of volume MRI and MR spectroscopic imaging data for the evaluation of patients with brain tumors. Magn. Reson. Med. 2001; 46: 228-239.
43. Simonetti AW, Melssen WJ, van der Graaf M, Postma GJ, Heerschap A, Buydens LMC. A chemometric approach for brain tumor classification using magnetic resonance imaging and spectroscopy. Anal. Chem. 2003; 75: 5352-5361.
44. Luts J, Laudadio T, Idema AJ, Simonetti AW, Heerschap A, Vandermeulen D, Suykens JA, Van Huffel S. Nosologic imaging of the brain: segmentation and classification using MRI and MRSI. NMR Biomed. 2009; 22: 374-390.
45. Gruber S, Mlynárik V, Moser E. High-resolution 3D proton spectroscopic imaging of the human brain at 3T: SNR issues and application for anatomy-matched voxel sizes. Magn. Reson. Med. 2003; 49: 299-306.