Accelerated white matter lesion analysis based on simultaneous
None
None
deep learning reconstruction
magnetic resonance fingerprinting
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:
07 2021
07 2021
Historique:
revised:
27
12
2020
received:
18
09
2020
accepted:
28
12
2020
pubmed:
7
2
2021
medline:
21
5
2021
entrez:
6
2
2021
Statut:
ppublish
Résumé
To develop an accelerated postprocessing pipeline for reproducible and efficient assessment of white matter lesions using quantitative magnetic resonance fingerprinting (MRF) and deep learning. MRF using echo-planar imaging (EPI) scans with varying repetition and echo times were acquired for whole brain quantification of Deep learning-based postprocessing reduced reconstruction and image processing times from hours to a few seconds while maintaining high accuracy, reliability, and precision. Mean absolute error performed the best for MRF is a fast and robust tool for quantitative
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
471-486Informations de copyright
© 2021 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.
Références
Bonnier G, Roche A, Romascano D, et al. Advanced MRI unravels the nature of tissue alterations in early multiple sclerosis. Ann Clin Trans Neurol. 2014;1:423-432.
Trip SA, Miller DH. Imaging in multiple sclerosis. J Neurol Neurosurg Psychiatry. 2015;76:iii11-iii18.
Blystad I, Håkansson I, Tisell A, et al. Quantitative MRI for analysis of active multiple sclerosis lesions without gadolinium-based contrast agent. Am J Neuroradiol. 2016;37:94-100.
Bauer S, Wagner M, Seiler A, et al. Quantitative T2′-mapping in acute ischemic stroke. Stroke. 2014;45:3280-3286.
Hernández-Torres E, Wiggermann V, Machan L, et al. Increased mean R2∗ in the deep gray matter of multiple sclerosis patients: have we been measuring atrophy? J Magn Reson Imaging. 2019;50:201-208.
Ma D, Gulani V, Seiberlich N, et al. Magnetic resonance fingerprinting. Nature. 2013;495:187-192.
Panda A, Mehta BB, Coppo S, et al. Magnetic resonance fingerprinting-an overview. Curr Opin Biomed Eng. 2017;3:56-66.
Rieger B, Akçakaya M, Pariente JC, et al. Time efficient whole-brain coverage with MR fingerprinting using slice-interleaved echo-planar-imaging. Sci Rep. 2018;8:2045-2322.
Rieger B, Zimmer F, Zapp J, Weingartner S, Schad LR. Magnetic resonance fingerprinting using echo planar imaging joint quantification of T1 and relaxation times. Magn Reson Med. 2017;78:1724-1733.
Hermann I, Chacon-Caldera J, Brumer I, et al. Magnetic resonance fingerprinting for simultaneous renal T1 and mapping in a single breath-hold. Magn Reson Med. 2020;83:1940-1948.
Mohan J, Krishnaveni V, Guo Y. A survey on the magnetic resonance image denoising methods. Biomed Signal Process Control. 2014;9:56-69.
Nowak RN. Wavelet-based Rician noise removal for magnetic resonance imaging. IEEEE Trans Image Process. 1999;8:1408-1419.
Saladi S, Amutha PN. Analysis of denoising filters on MRI brain images. Int J Imaging Syst Technol. 2017;27:201-208.
Veraart J, Novikov DS, Christiaens D, Ades-aron B, Sijbers J, Fieremans E. Denoising of diffusion MRI using random matrix theory. Neuroimage. 2016;15:394-406.
Bipin MB, Coppo S, Frances MD, et al. Magnetic resonance fingerprinting: a technical review. Magn Reson Med. 2019;81:25-46.
Lundervold AS, Lundervold A. An overview of deep learning in medical imaging focusing on MRI. Z Med Phys. 2019;29:102-127.Special Issue: Deep Learning in Medical Physics.
Maier A, Syben C, Lasser T, Riess C. A gentle introduction to deep learning in medical image processing. Z Med Phys. 2019;29:86-101.Special Issue: Deep Learning in Medical Physics.
Hoppe E, Thamm F, Körzdörfer G, et al. Magnetic resonance fingerprinting reconstruction using recurrent neural networks. Stud Health Technol Inform. 2019;267:126-133.
Fang Z, Chen Y, Liu M, et al. Deep learning for fast and spatially constrained tissue quantification from highly accelerated data in magnetic resonance fingerprinting. IEEE Trans Med Imaging. 2019;38:2364-2374.
Akcakaya M, Moeller S, Weingärtner S, Ugurbil 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.
Zhang C, Hosseini SAH, Weingärtner S, Ugurbil K, Moeller S, Akçakaya M. Optimized fast GPU implementation of robust artificial-neural-networks for k-space interpolation (RAKI) reconstruction. PLOS ONE. 2019;14:1-14.
Hammernik K, Klatzer T, Kobler E, et al. Learning a variational network for reconstruction of accelerated MRI data. Magn Reson Med. 2018;79:3055-3071.
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.
Knoll F, Hammernik K, Kobler E, Pock T, Recht MP, Sodickson DK. Assessment of the generalization of learned image reconstruction and the potential for transfer learning. Magn Reson Med. 2019;81:116-128.
Benou A, Veksler R, Friedman A, Riklin Raviv T. Ensemble of expert deep neural networks for spatio-temporal denoising of contrast-enhanced MRI sequences. Med Image Anal. 2017;42:145-159.
Bermudez C, Plassard AJ, Davis LT, Newton AT, Resnick SM, Landman BA. Learning implicit brain MRI manifolds with deep learning. CoRR. 2018.abs/1801.01847.
Manjon JV, Coupe P. MRI denoising using deep learning and non-local averaging. ArXiv. 2019. https://arxiv.org/abs/1911.04798. Accessed January 27, 2021.
You X, Cao N, Lu H, Mao M, Wang W. Denoising of MR images with Rician noise using a wider neural network and noise range division. Magn Reson Imaging. 2019;64:154-159.Artificial Intelligence in MRI.
Jog A, Roy S, Carass A, Prince JL. Magnetic resonance image synthesis through patch regression. In 2013 IEEE 10th International Symposium on Biomedical Imaging, San Francisco, CA. 2013:350-353.
Jog A, Carass A, Roy S, Pham DL, Prince JL. Random forest regression for magnetic resonance image synthesis. Neuroimaging. 2017;35:475-488.
Cao X, Yang J, Zhang J, Wang Q, Yap P, Shen D. Deformable image registration using a cue-aware deep regression network. IEEE Trans Biomed Eng. 2018;65:1900-1911.
Vos BD, Berendsen FF, Viergever MA, Sokooti H, Staring M, Isgum I. A Deep Learning Framework for Unsupervised Affine and Deformable Image Registration. 2018. https://arxiv.org/abs/1809.06130. Accessed January 27, 2021.
Ghosal S, Ray N. Deep deformable registration: enhancing accuracy by fully convolutional neural net. Pattern Recogn Lett. 2017;94:81-86.
Golbabaee M, Chen D, Gómez PA, Menzel MI, Davies ME. A deep learning approach for magnetic resonance fingerprinting. ArXiv. 2018.abs/1809.01749.
Cohen O, Zhu B, Rosen MS. MR fingerprinting deep reconstruction network (DRONE). Magn Reson Med. 2018;80:885-894.
Hoppe E, Körzdörfer G, Würfl T, et al. Deep learning for magnetic resonance fingerprinting: a new approach for predicting quantitative parameter values from time series. Stud Health Technol Inform. 2017;243:202-206.
Oksuz I, Cruz G, Clough J, et al. Magnetic resonance fingerprinting using recurrent neural networks. IEEE Comput Soc. 2019;1537-1540.
Chen Y, Fang Z, Hung S-C, Chang W-T, Shen D, Lin W. High-resolution 3D MR Fingerprinting using parallel imaging and deep learning. NeuroImage. 2020;206:116329.
Hamilton JI, Seiberlich N. Machine learning for rapid magnetic resonance fingerprinting tissue property quantification. Proc IEEE. 2020;108:69-85.
Balsiger F, Shridhar KA, Chikop S, et al. Magnetic resonance fingerprinting reconstruction via spatiotemporal convolutional neural networks. In F. Knoll, A. Maier, D. Rueckert (Eds.), Machine Learning for Medical Image Reconstruction; Cham: Springer International Publishing. 2018:39-46.
Balsiger F, Scheidegger O, Carlier PG, Marty B, Reyes M. On the spatial and temporal influence for the reconstruction of magnetic resonance fingerprinting, 102 of Proceedings of Machine Learning Research (London, United Kingdom). PMLR. 2019:27-38.
Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention (MICCAI), 9351 of LNCS; Springer, 2015:234-241.(available on arXiv:1505.04597 [cs.CV]).
Ding PLK, Li Z, Zhou Y, Li B. Deep residual dense U-net for resolution enhancement in accelerated MRI acquisition. In E. D. Angelini, E. D. Angelini, E. D. Angelini, B. A. Landman (Eds.), Medical Imaging 2019. Progress in Biomedical Optics and Imaging-Proceedings of SPIE, Hangzhou, China. 2019.
Han C, Gesser F, Milacski Z. CNN-based MRI regression using u-net. 2018. https://www.semanticscholar.org/paper/CNN-based-MRI-Regression-Using-U-Net-Han-Gesser/c545b4c8f2cb5c5dccf83bf3d5b3b49e17da8d29. Accessed January 27, 2021.
Moeskops P, de Bresser J, Kuijf HJ, et al. Evaluation of a deep learning approach for the segmentation of brain tissues and white matter hyperintensities of presumed vascular origin in MRI. NeuroImage. 2018;17:251-262.
Golbabaee M, Chen D, Gómez PA, Menzel M, Davies M. Geometry of deep learning for magnetic resonance fingerprinting. ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK. 2019:7825-7829.
Fang Z, Chen Y, Hung S-C, Zhang X, Lin W, Shen D. Submillimeter MR fingerprinting using deep learning-based tissue quantification. Magn Reson Med. 2020;84:579-591.
Wang S, Peterson DJ, Gatenby JC, Li W, Grabowski TJ, Madhyastha TM. Evaluation of field map and nonlinear registration methods for correction of susceptibility artifacts in diffusion MRI. Front Neuroinform. 2017;11:17.
Klein A, Andersson J, Ardekani BA, et al. Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. NeuroImage. 2009;46:786-802.
Ashburner J, Balbastre Y, Barnes G, Brudfors M. SPM12. Wellcome Trust Centre for Neuroimaging. UCL. 2014. https://www.fil.ion.ucl.ac.uk/spm/software/spm12/. Accessed January 27, 2021.
Bojorquez JZ, Bricq S, Acquitter C, Brunotte F, Walker PM, Lalande A. What are normal relaxation times of tissues at 3 T? Magn Reson Imaging. 2017;35:69-80.
Feng X, Deistung A, Reichenbach JR. Quantitative susceptibility mapping (QSM) and R2∗ in the human brain at 3T: Evaluation of intra-scanner repeatability. Z Med Phys. 2018;28:36-48.
Gracien R-M, Maiworm M, Brüche N, et al. How stable is quantitative MRI? - assessment of intra- and inter-scanner-model reproducibility using identical acquisition sequences and data analysis programs. NeuroImage. 2020;207:116364.
Abbas Z, Gras V, Müllenhoff K, Keil F, Oros-Peusquens A-M, Shah NJ. Analysis of proton-density bias corrections based on T1 measurement for robust quantification of water content in the brain at 3 Tesla. Magn Reson Med. 2014;72:1735-1745.
Baudrexel S, Volz S, Preibisch C, et al. Rapid single-scan T-mapping using exponential excitation pulses and image-based correction for linear background gradients. Magn Reson Med. 2009;62:263-268.
Wardlaw JM, Valdés Hernández MC, Munoz-Maniega S. What are white matter hyperintensities made of? J Am Heart Assoc. 2015;4:e001140.
Cho S, Jones D, Reddick WE, Ogg RJ, Steen RG. Establishing norms for age-related changes in proton T1 of human brain tissue in vivo. Magn Reson Imaging. 1997;15:1133-1143.
Kupeli A, Kocak M, Goktepeli M, Karavas E, Danisan G. Role of T1 mapping to evaluate brain aging in a healthy population. Clin Imaging. 2020;59:56-60.
Demirel ÖB, Weingärtner S, Moeller S, Akçakaya M. Improved Regularized reconstruction for simultaneous multi-slice cardiac MRI T1 mapping. In 2019 27th European Signal Processing Conference (EUSIPCO), A Coruña, Spain. 2019:1-5.
Yogananda CGB, Wagner BC, Murugesan GK, Madhuranthakam A. Maldjian JA. A deep learning pipeline for automatic skull stripping and brain segmentation. In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), Venice, Italy. 2019:727-731.
Mahbod A, Chowdhury M, Smedby Ö, Wang C. Automatic brain segmentation using artificial neural networks with shape context. Pattern Recognit Lett. 2018;101:74-79.
Palumbo L, Bosco P, Fantacci ME, et al. Evaluation of the intra- and inter-method agreement of brain MRI segmentation software packages: a comparison between SPM12 and FreeSurfer v6.0. Phys Med. 2019;64:261-272.
Choi U-S, Kawaguchi H, Matsuoka Y, Kober T, Kida I. Brain tissue segmentation based on MP2RAGE multi-contrast images in 7 T MRI. PLOS ONE. 2019;14:1-13.
Dar SUH, Özbey M, Çatli AB, Çukur T. A transfer-learning approach for accelerated MRI using deep neural networks. Magn Reson Med. 2020;84:663-685.
Narayana PA, Coronado I, Sujit SJ, Wolinsky JS, Lublin FD, Gabr RE. Deep-learning-based neural tissue segmentation of MRI in multiple sclerosis: Effect of training set size. J Magn Reson Imaging. 2020;51:1487-1496.
Cho J, Lee K, Shin E, Choy G, Do S. How much data is needed to train a medical image deep learning system to achieve necessary high accuracy? arXiv.org [Internet]. 2015. https://scholar.harvard.edu/synho/publications/how-much-data-needed-train-medical-image-deep-learning-system-achieve-necessary. Accessed January 27, 2021.