Reconstruction of spectra from truncated free induction decays by deep learning in proton magnetic resonance spectroscopy.
convolutional neural network
deep learning
free induction decay
proton magnetic resonance spectroscopy
truncation
Journal
Magnetic resonance in medicine
ISSN: 1522-2594
Titre abrégé: Magn Reson Med
Pays: United States
ID NLM: 8505245
Informations de publication
Date de publication:
08 2020
08 2020
Historique:
received:
04
09
2019
revised:
21
11
2019
accepted:
14
12
2019
pubmed:
9
1
2020
medline:
15
5
2021
entrez:
9
1
2020
Statut:
ppublish
Résumé
To explore the applicability of convolutional neural networks (CNNs) in the reconstruction of spectra from truncated FIDs (tFIDs) in Rat brain FIDs were simulated at 9.4 T based on in vivo data (N = 11) and randomly truncated by retaining 8, 16, 32, 64, 128, 256, 512, and 1024 (null truncation) points (denoted as tFID The best result on the simulated data was obtained with Upon the availability of more realistically simulated training data, CNNs can also be used in the reconstruction of spectra from truncated FIDs.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
559-568Informations de copyright
© 2020 International Society for Magnetic Resonance in Medicine.
Références
Stern AS, Donoho DL, Hoch JC. NMR data processing using iterative thresholding and minimum l1-norm reconstruction. J Magn Reson. 2007;188:295-300.
Hoch JC, Stern AS. Maximum entropy reconstruction in NMR. In: Grant DM, Harris RK, eds. Encyclopedia of NMR. New York: John Wiley and Sons; 1996:2980-2988.
Newman RH. Maximization of entropy and minimization of area as criteria for NMR signal processing. J Magn Reson. 1988;79:448-460.
Barkhuijsen H, de Beer R, Bovee WMMJ, Creyghton JHN, van Ormondt D. Application of linear prediction and singular value decomposition (LPSVD) to determine NMR frequencies and intensities from the FID. Magn Reson Med. 1985;2:86-89.
LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436-444.
Wang S, Su Z, Ying L, et al. Accelerating magnetic resonance imaging via deep learning. In: Proceedings of the IEEE 13th International Symposium on Biomedical Imaging (ISBI), Prague, Czech Republic, 2016. pp 514-517.
Lee D, Yoo J, Ye JC. Deep residual learning for compressed sensing MRI. In: Proceedings of IEEE 14th International Symposium on Biomedical Imaging (ISBI), Melbourne, Australia, 2017. pp 15-18.
Akçakaya M, Moeller S, Weingärtner S, Uğurbil K. Scan-specific robust artificial-neural-networks for k-space interpolation (RAKI) reconstruction: database-free deep learning for fast imaging. Magn Reson Med. 2019;81:439-453.
Han Y. Sunwoo L, Ye JC. k-Space deep learning for accelerated MRI. arXiv:1805.03779v3.
Zhu B, Liu J, Cauley SF, Rosen BR, Rosen MS. Image reconstruction by domain-transform manifold learning. Nature. 2018;555:487-492.
Mlynárik V, Gambarota G, Frenkel H, Gruetter R. Localized short-echo-time proton MR spectroscopy with full signal-intensity acquisition. Magn Reson Med. 2006;56:965-970.
Lee HH, Kim H. Parameterization of spectral baseline directly from short echo time full spectra in 1H-MRS. Magn Reson Med. 2017;78:836-847.
Slotboom J, Creyghton JH, Korbee D, Mehlkopf AF, Bovee WM. Spatially selective RF pulses and the effects of digitization on their performance. Magn Reson Med. 1993;30:732-740.
Tkác I, Starčuk Z, Choi I-Y, Gruetter R. In vivo 1H NMR spectroscopy of rat brain at 1 ms echo time. Magn Reson Med. 1999;41:649-656.
Tkác I, Gruetter R. Methodology of 1H NMR spectroscopy of the human brain at very high magnetic fields. Appl Magn Reson. 2005;29:139-157.
Cabanes E, Confort-Gouny S, Le Fur Y, Simond G, Cozzone P. Optimization of residual water signal removal by HLSVD on simulated short echo time proton MR spectra of the human brain. J Magn Reson. 2001;150:116-125.
Smith S, Levante T, Meier BH, Ernst RR. Computer simulations in magnetic resonance. An object-oriented programming approach. J Magn Reson A. 1994;106:75-105.
Govindaraju V, Young K, Maudsley AA. Proton NMR chemical shifts and coupling constants for brain metabolites. NMR Biomed. 2000;13:129-153.
Lee HH, Kim H. Intact metabolite spectrum mining by deep learning in proton magnetic resonance spectroscopy of the brain. Magn Reson Med. 2019;82:33-48.
Pfeuffer J, Tkac I, Provencher SW, Gruetter R. Toward an in vivo neurochemical profile: quantification of 18 metabolites in short-echo-time 1H NMR spectra of the rat brain. J Magn Reson. 1999;141:104-120.
Perry TL. Cerebral amino-acid pools. In: Lajtha A, ed. Chemical and Cellular Architecture. Boston: Springer; 1982:151-180.
De Graaf RA. In Vivo NMR Spectroscopy: Principles and Techniques. New York: John Wiley & Sons; 2019.
Lopez-Kolkovsky AL, Meriaux S, Boumezbeur F. Metabolite and macromolecule T1 and T2 relaxation times in the rat brain in vivo at 17.2T. Magn Reson Med. 2015;75:503-514.
Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. In: Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Munich, Germany, 2015. pp 234-241.
Jin KH, McCann MT, Froustey E, Unser M. Deep convolutional neural network for inverse problems in imaging. IEEE Trans Image Process. 2017;26:4509-4522.
Hyun CM, Kim HP, Lee SM, Lee S, Seo JK. Deep learning for undersampled MRI reconstruction. Phys Med Biol. 2018;63:135007-135021.
Souza R, Frayne R.A hybrid frequency-domain/image-domain deep network for magnetic resonance image reconstruction. arXiv:1810.12473.
Snoek J, Larochelle H, Adams RP. Practical Bayesian optimization of machine learning algorithms. In: Proceedings of International Conference on Neural Information Processing Systems, Lake Tahoe, Nevada, 2012. pp 2951-2959.
Eo T, Jun Y, Kim T, Jang J, Lee HJ, Hwang D. KIKI-net: cross-domain convolutional neural networks for reconstructing undersampled magnetic resonance images. Magn Reson Med. 2018;80:2188-2201.
Schlemper J, Caballero J, Hajnal JV, Price AN, Rueckert D. A deep cascade of convolutional neural networks for dynamic MR image reconstruction. IEEE Trans Med Imaging. 2018;37:491-503.
Provencher SW. Estimation of metabolite concentrations from localized in vivo proton NMR spectra. Magn Reson Med. 1993;30:672-679.
Wang CY, Griswold MA, Yu X. Efficient quantification of metabolite concentration and T1 relaxation by 31P spectroscopic magnetic resonance fingerprinting. In: Proceedings of International Society of Magnetic Resonance in Medicine, Toronto, Canada, 2015. p 56.
Wang CY, Liu Y, Huang S, Griswold MA, Seiberlich N, Yu X. 31P magnetic resonance fingerprinting for rapid quantification of creatine kinase reaction rate in vivo. NMR Biomed. 2017;30:e3786.
Kulpanovich A, Tal A. The application of magnetic resonance fingerprinting to single voxel proton spectroscopy. NMR Biomed. 2018;31:e4001.
Kirov II, Tal A. Potential clinical impact of multiparametric quantitative MR spectroscopy in neurological disorders: a review and analysis. Magn Reson Med. 2020;83:22-44.
Dong C, Loy CC, He K, Tang X.Image super-resolution using deep convolutional networks. arXiv:1501.00092v3.
Xu L, Ren JSJ, Liu C, Jia J. Deep convolutional neural network for image deconvolution. In: Proceedings of 27th International Conference on Neural Information Processing Systems (NIPS), Montréal, Canada, 2014. pp 1790-1798.
Slotboom J, Boesch C, Kreis R. Versatile frequency domain fitting using time domain models and prior knowledge. Magn Reson Med. 1998;39:899-911.
Goodfellow I, Bengio Y, Courville A. Deep Learning. Cambridge, MA: MIT Press; 2016.
Neyshabur B, Bhojanapalli S, McAllester D, Srebro N. Exploring generalization in deep learning. arXiv:1706.08947v2.
Kornblith S, Shlens J, Le QV.Do better ImageNet models transfer better? arXiv:1805.08974v3.
Iqbal Z, Nguyen D, Thomas MA, Jiang S.Acceleration and quantitation of localized correlated spectroscopy using deep learning: a pilot simulation study. arXiv:1806.11068.
Das D, Coello E, Schulte RF, Menze BH. Quantification of metabolites in magnetic resonance spectroscopic imaging using machine learning. In: Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention, Quebec, Canada, 2017. pp 462-470.
Hatami N, Sdika M, Ratiney H.Magnetic resonance spectroscopy quantification using deep learning. arXiv:1806.07237v1.
Gurbani S, Sheriff S, Maudsley AA, Shim H, Cooper LAD. Incorporation of a spectral model in a convolutional neural network for accelerated spectral fitting. Magn Reson Med. 2019;81:3346-3357.
Gurbani SS, Schreibmann E, Maudsley AA, et al. A convolutional neural network to filter artifacts in spectroscopic MRI. Magn Reson Med. 2018;80:1765-1775.
Kyathanahally SP, Mocioiu V, Pedrosa de Barros N, et al. Quality of clinical brain tumor MR spectra judged by humans and machine learning tools. Magn Reson Med. 2018;79:2500-2510.
Kyathanahally SP, Doring A, Kreis R. Deep learning approaches for detection and removal of ghosting artifacts in MR spectroscopy. Magn Reson Med. 2018;80:851-863.
Pedrosa de Barros N, McKinley R, Knecht U, Wiest R, Slotboom J. Automatic quality control in clinical 1H MRSI of brain cancer. NMR Biomed. 2016;29:563-575.
Cole E, Pauly J, Vasanawala S, Cheng J. Complex-valued convolutional neural networks for MRI reconstruction. In: Proceedings of International Society of Magnetic Resonance in Medicine, Montreal, Canada, 2019. p 4714.
Trabelsi C, Bilaniuk O, Zhang Y, et al. Deep complex networks. arXiv:1705.09792v4.