Fast deep learning reconstruction techniques for preclinical magnetic resonance fingerprinting.
MR fingerprinting
deep learning
neural networks
quantitative MRI
Journal
NMR in biomedicine
ISSN: 1099-1492
Titre abrégé: NMR Biomed
Pays: England
ID NLM: 8915233
Informations de publication
Date de publication:
Jan 2024
Jan 2024
Historique:
revised:
05
07
2023
received:
18
05
2023
accepted:
27
07
2023
medline:
11
12
2023
pubmed:
6
9
2023
entrez:
5
9
2023
Statut:
ppublish
Résumé
We propose a deep learning (DL) model and a hyperparameter optimization strategy to reconstruct T
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e5028Subventions
Organisme : INFN next_AIM project
Organisme : M. P. thanks ALSA
ID : 20-IIA-525
Informations de copyright
© 2023 The Authors. NMR in Biomedicine published by John Wiley & Sons Ltd.
Références
Cooper G, Hirsch S, Scheel M, et al. Quantitative multi-parameter mapping optimized for the clinical routine. Front Neurosci. 2020;14:611194. doi:10.3389/fnins.2020.611194
Weiskopf N, Suckling J, Williams G, et al. Quantitative multi-parameter mapping of R1, PD*, MT, and R2* at 3T: a multi-center validation. Front Neurosci. 2013;7:95. doi:10.3389/fnins.2013.00095
Granziera C, Wuerfel J, Barkhof F, et al. Quantitative magnetic resonance imaging towards clinical application in multiple sclerosis. Brain. 2021;144(5):1296-1311. doi:10.1093/brain/awab029
Reitz SC, Hof S-M, Fleischer V, et al. Multi-parametric quantitative MRI of normal appearing white matter in multiple sclerosis, and the effect of disease activity on T2. Brain Imaging Behav. 2017;11:744-753. doi:10.1007/s11682-016-9550-5
Syed AK, Whisenant JG, Barnes SL, Sorace AG, Yankeelov TE. Multiparametric analysis of longitudinal quantitative MRI data to identify distinct tumor habitats in preclinical models of breast cancer. Cancer. 2020;12(6):1682. doi:10.3390/cancers12061682
Salerno M, Kramer CM. Advances in parametric mapping with CMR imaging. JACC Cardiovasc Imaging. 2013;6(7):806-822. doi:10.1016/j.jcmg.2013.05.005
Iles L, Pfluger H, Phrommintikul A, et al. Evaluation of diffuse myocardial fibrosis in heart failure with cardiac magnetic resonance contrast-enhanced T1 mapping. J Am Coll Cardiol. 2008;52(19):1574-1580. doi:10.1016/j.jacc.2008.06.049
Wolf M, de Boer A, Sharma K, et al. Magnetic resonance imaging T1-and T2-mapping to assess renal structure and function: a systematic review and statement paper. Nephrol Dial Transplant. 2018;33(suppl 2):ii41-ii50. doi:10.1093/ndt/gfy198
Girometti R, Cereser L, Bonato F, Zuiani C. Evolution of prostate MRI: from multiparametric standard to less-is-better and different-is better strategies. Eur Radiol Exp. 2019;3:1-14. doi:10.1186/s41747-019-0088-3
Sherrer RL, Glaser ZA, Gordetsky JB, Nix JW, Porter KK, Rais-Bahrami S. Comparison of biparametric MRI to full multiparametric MRI for detection of clinically significant prostate cancer. Prostate Cancer Prostatic Dis. 2019;22(2):331-336. doi:10.1038/s41391-018-0107-0
Ma D, Gulani V, Seiberlich N, et al. Magnetic resonance fingerprinting. Nature. 2013;495(7440):187-192. doi:10.1038/nature11971
Bipin Mehta B, Coppo S, Frances McGivney D, et al. Magnetic resonance fingerprinting: a technical review. Magn Reson Med. 2019;81(1):25-46. doi:10.1002/mrm.27403
Poorman ME, Martin MN, Ma D, et al. Magnetic resonance fingerprinting part 1: potential uses, current challenges, and recommendations. J Magn Reson Imaging. 2020;51(3):675-692. doi:10.1002/jmri.26836
Panda A, Mehta BB, Coppo S, et al. Magnetic resonance fingerprinting-an overview. Curr Opin Biomed. 2017;3:56-66. doi:10.1016/j.cobme.2017.11.001
Weigel M. Extended phase graphs: dephasing, RF pulses, and echoes-pure and simple. J Magn Reson Imaging. 2015;41(2):266-295. doi:10.1002/jmri.24619
Barbieri M, Brizi L, Giampieri E, et al. A deep learning approach for magnetic resonance fingerprinting: scaling capabilities and good training practices investigated by simulations. Phys Med. 2021;89:80-92. doi:10.1016/j.ejmp.2021.07.013
McGivney DF, Pierre E, Ma D, et al. SVD compression for magnetic resonance fingerprinting in the time domain. IEEE Trans Med Imaging. 2014;33(12):2311-2322. doi:10.1109/TMI.2014.2337321
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. GMDS. 2017;243:202-206.
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(10):2364-2374. doi:10.1109/TMI.2019.2899328
Balsiger F, Konar AS, Chikop S, et al. Magnetic resonance fingerprinting reconstruction via spatiotemporal convolutional neural networks. In: Machine Learning for Medical Image Reconstruction: First International Workshop, MLMIR 2018, held in conjunction with MICCAI 2018, Granada, Spain, September 16, 2018; Proceedings 1. Springer; 2018:39-46.
Cohen O, Zhu B, Rosen MS. MR fingerprinting Deep RecOnstruction NEtwork (DRONE). Magn Reson Med. 2018;80(3):885-894. doi:10.1002/mrm.27198
Liao C, Wang K, Cao X, et al. Detection of lesions in mesial temporal lobe epilepsy by using MR fingerprinting. Radiology. 2018;288(3):804-812. doi:10.1148/radiol.2018172131
Ma D, Jones SE, Deshmane A, et al. Development of high-resolution 3D MR fingerprinting for detection and characterization of epileptic lesions. J Magn Reson Imaging. 2019;49(5):1333-1346. doi:10.1002/jmri.26319
Keil VC, Bakoeva SP, Jurcoane A, et al. MR fingerprinting as a diagnostic tool in patients with frontotemporal lobe degeneration: a pilot study. NMR Biomed. 2019;32(11):e4157. doi:10.1002/nbm.4157
Keil VC, Bakoeva SP, Jurcoane A, et al. A pilot study of magnetic resonance fingerprinting in Parkinson's disease. NMR Biomed. 2020;33(11):e4389. doi:10.1002/nbm.4389
Chen Y, Jiang Y, Pahwa S, et al. MR fingerprinting for rapid quantitative abdominal imaging. Radiology. 2016;279(1):278-286. doi:10.1148/radiol.2016152037
Hamilton JI, Jiang Y, Eck B, Griswold M, Seiberlich N. Cardiac cine magnetic resonance fingerprinting for combined ejection fraction, T1 and T2 quantification. NMR Biomed. 2020;33(8):e4323. doi:10.1002/nbm.4323
Gao Y, Chen Y, Ma D, et al. Preclinical MR fingerprinting (MRF) at 7 T: effective quantitative imaging for rodent disease models. NMR Biomed. 2015;28(3):384-394. doi:10.1002/nbm.3262
Zhao B, Haldar JP, Liao C, et al. Optimal experiment design for magnetic resonance fingerprinting: Cramer-Rao bound meets spin dynamics. IEEE Trans Med Imaging. 2018;38(3):844-861. doi:10.1109/TMI.2018.2873704
Hoppe E, Thamm F, Körzdörfer G, et al. Magnetic resonance fingerprinting reconstruction using recurrent neural networks. Stud Health Technol Inform. 2019;267:126-133. doi:10.3233/SHTI190816
Abadi M, Barham P, Chen J, et al. TensorFlow: a system for large-scale machine learning. In: 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16); 2016:265-283.
Bergstra J, Yamins D, Cox DD, et al. Hyperopt: a python library for optimizing the hyperparameters of machine learning algorithms. In: Proceedings of the 12th Python in Science Conference. Vol. 13. Citeseer; 2013:20.
Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process. 2004;13(4):600-612. doi:10.1109/TIP.2003.819861
Satopaa V, Albrecht J, Irwin D, Raghavan B. Finding a “kneedle” in a haystack: Detecting knee points in system behavior. In: 2011 31st International Conference on Distributed Computing Systems Workshops. IEEE; 2011:166-171.
Glorot X, Bengio Y. Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics. JMLR Workshop and Conference Proceedings; 2010:249-256.
Kingma DP, Ba J. Adam: a method for stochastic optimization. arXiv. Preprint posted online December 22, 2014. Preprint arXiv:1412.6980.
Hong J-S, Hermann I, Zollner FG, et al. Acceleration of magnetic resonance fingerprinting reconstruction using denoising and self-attention pyramidal convolutional neural network. Sensors. 2022;22(3):1260. doi:10.3390/s22031260
Rieger B, Zimmer F, Zapp J, Weingartner S, Schad LR. Magnetic resonance fingerprinting using echo-planar imaging: joint quantification of T1 and T2* relaxation times. Magn Reson Med. 2017;78(5):1724-1733. doi:10.1002/mrm.26561
Rieger B, Akçakaya M, Pariente JC, et al. Time efficient whole-brain coverage with MR fingerprinting using slice-interleaved echoplanar-imaging. Sci Rep. 2018;8(1):6667. doi:10.1038/s41598-018-24920-z
Hermann I, Chacon-Caldera J, Brumer I, et al. Magnetic resonance fingerprinting for simultaneous renal T1 and T2* mapping in a single breath-hold. Magn Reson Med. 2020;83(6):1940-1948. doi:10.1002/mrm.28160
Yu AC, Badve C, Ponsky LE, et al. Development of a combined mr fingerprinting and diffusion examination for prostate cancer. Radiology. 2017;283(3):729-738. doi:10.1148/radiol.2017161599
Su P, Mao D, Liu P, et al. Multiparametric estimation of brain hemodynamics with MR fingerprinting ASL. Magn Reson Med. 2017;78(5):1812-1823. doi:10.1002/mrm.26587
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(12):e3786. doi:10.1002/nbm.3786