Neue Methoden in der Neuroradiologie: MR-Fingerprinting und synthetische Bildgebung

Vera Catharina Keil

doi:10.1055/a-2181-0117

Neurologie up2date, Table of Contents

Neurologie up2date 2023; 06(04): 325-341
DOI: 10.1055/a-2181-0117

Neuroonkologie

Neue Methoden in der Neuroradiologie: MR-Fingerprinting und synthetische Bildgebung

Authors

Vera Catharina Keil

Abstract

All articles of this category

Was bedeutet „synthetische Bildgebung“? Ist es eine bestimmte Form der Akquisition oder kann man auch durch Postprocessing von Standard-MRTAufnahmen synthetische Bilder erzeugen? Welche Rolle spielt künstliche Intelligenz hierbei? Antworten auf die Fragen, welche synthetischen Verfahren es gibt und wofür diese bereits bei neuroradiologischen Fragestellungen genutzt werden, liefert dieser Übersichtsartikel.

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References

Literatur
1 Warntjes JBM, Dahlqvist O, Lundberg P. Novel method for rapid, simultaneous T1, T2*, and proton density quantification. Magn Reson Med 2007; 57: 528-537
2 Bobman SA, Riederer SJ, Lee JN. et al. Cerebral magnetic resonance image synthesis. AJNR Am J Neuroradiol 1985; 6: 265-269
3 Ma D, Gulani V, Seiberlich N. et al. Magnetic resonance fingerprinting. Nature 2013; 495: 187-192
4 Warntjes JBM, Leinhard OD, West J. et al. Rapid magnetic resonance quantification on the brain: Optimization for clinical usage. Magn Reson Med 2008; 60: 320-329
5 Tanenbaum LN, Tsiouris AJ, Johnson AN. et al. Synthetic MRI for Clinical Neuroimaging: Results of the Magnetic Resonance Image Compilation (MAGiC) Prospective, Multicenter, Multireader Trial. AJNR Am J Neuroradiol 2017; 38: 1103-1110
6 Kim E, Cho H-H, Cho SH. et al. Accelerated Synthetic MRI with Deep Learning-Based Reconstruction for Pediatric Neuroimaging. AJNR Am J Neuroradiol 2022; 43: 1653-1659
7 Ouellette R, Mangeat G, Polyak I. et al. Validation of Rapid Magnetic Resonance Myelin Imaging in Multiple Sclerosis. Ann Neurol 2020; 87: 710-724
8 Ma D, Pierre EY, Jiang Y. et al. Music-based magnetic resonance fingerprinting to improve patient comfort during MRI examinations. Magn Reson Med 2016; 75: 2303-2314
9 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: 675-692
10 Springer E, Cardoso PL, Strasser B. et al. MR Fingerprinting – A Radiogenomic Marker for Diffuse Gliomas. Cancers 2022; 14
11 Keil VC, Bakoeva SP, Jurcoane A. et al. A pilot study of magnetic resonance fingerprinting in Parkinsonʼs disease. NMR Biomed 2020; 33: e4389
12 Cohen O, Yu VY, Tringale KR. et al. CEST MR fingerprinting (CEST-MRF) for brain tumor quantification using EPI readout and deep learning reconstruction. Magn Reson Med 2023; 89: 233-249
13 Fan H, Su P, Lin DDM. et al. Simultaneous Hemodynamic and Structural Imaging of Ischemic Stroke With Magnetic Resonance Fingerprinting Arterial Spin Labeling. Stroke 2022; 53: 2016-2025
14 Wicaksono KP, Fushimi Y, Nakajima S. et al. Accuracy, repeatability, and reproducibility of T1 and T2 relaxation times measurement by 3D magnetic resonance fingerprinting with different dictionary resolutions. Eur Radiol 2022;
15 McGivney DF, Boyacıoğlu R, Jiang Y. et al. Magnetic resonance fingerprinting review part 2: Technique and directions. J Magn Reson Imaging 2020; 51: 993-1007
16 Kara D, Fan M, Hamilton J. et al. Parameter map error due to normal noise and aliasing artifacts in MR fingerprinting. Magn Reson Med 2019; 81: 3108-3123
17 Shridhar Konar A, Qian E, Geethanath S. et al. Quantitative imaging metrics derived from magnetic resonance fingerprinting using ISMRM/NIST MRI system phantom: An international multicenter repeatability and reproducibility study. Med Phys 2021; 48: 2438-2447
18 Badve C, Yu A, Rogers M. et al. Simultaneous T1 and T2 Brain Relaxometry in Asymptomatic Volunteers Using Magnetic Resonance Fingerprinting. Tomography 2015; 1: 136-144
19 Sbrizzi A, Heide O van der, Cloos M. et al. Fast quantitative MRI as a nonlinear tomography problem. Magn Reson Imaging 2018; 46: 56-63
20 Kleinloog JPD, Mandija S, DʼAgata F. et al. Synthetic MRI with Magnetic Resonance Spin TomogrAphy in Time-Domain (MRSTAT): Results from a Prospective Cross-Sectional Clinical Trial. J Magn Reson Imaging 2022;
21 Ji S, Yang D, Lee J. et al. Synthetic MRI: Technologies and Applications in Neuroradiology. J Magn Reson Imaging 2022; 55: 1013-1025
22 Kleesiek J, Morshuis JN, Isensee F. et al. Can Virtual Contrast Enhancement in Brain MRI Replace Gadolinium?: A Feasibility Study. Invest Radiol 2019; 54: 653-660
23 Jayachandran Preetha C, Meredig H, Brugnara G. et al. Deeplearning-based synthesis of post-contrast T1-weighted MRI for tumour response assessment in neuro-oncology: a multicentre, retrospective cohort study. Lancet Digit Health 2021; 3: e784-e794
24 Boulanger M, Nunes J-C, Chourak H. et al. Deep learning methods to generate synthetic CT from MRI in radiotherapy: A literature review. Phys Med 2021; 89: 265-281
25 Wang R, Liu H, Toyonaga T. et al. Generation of synthetic PET images of synaptic density and amyloid from 18 F-FDG images using deep learning. Med Phys 2021; 48: 5115-5129
26 Ladefoged CN, Hansen AE, Henriksen OM. et al. AI-driven attenuation correction for brain PET/MRI: Clinical evaluation of a dementia cohort and importance of the training group size. Neuroimage 2020; 222: 117221