Relevant bibliographies by topics / MR Fingerprinting

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'MR Fingerprinting'

Author: Grafiati
Published: 25 January 2025

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Journal articles on the topic "MR Fingerprinting"

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Flassbeck, Sebastian, Simon Schmidt, Peter Bachert, Mark E. Ladd, and Sebastian Schmitter. "Flow MR fingerprinting." Magnetic Resonance in Medicine 81, no. 4 (December 2, 2018): 2536–50. http://dx.doi.org/10.1002/mrm.27588.

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Pierre, Eric Y., Dan Ma, Yong Chen, Chaitra Badve, and Mark A. Griswold. "Multiscale reconstruction for MR fingerprinting." Magnetic Resonance in Medicine 75, no. 6 (June 30, 2015): 2481–92. http://dx.doi.org/10.1002/mrm.25776.

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Zhang, Xiaodi, Zechen Zhou, Shiyang Chen, Shuo Chen, Rui Li, and Xiaoping Hu. "MR fingerprinting reconstruction with Kalman filter." Magnetic Resonance Imaging 41 (September 2017): 53–62. http://dx.doi.org/10.1016/j.mri.2017.04.004.

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Buonincontri, Guido, and Stephen J. Sawiak. "MR fingerprinting with simultaneous B1 estimation." Magnetic Resonance in Medicine 76, no. 4 (October 28, 2015): 1127–35. http://dx.doi.org/10.1002/mrm.26009.

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Cohen, Ouri, Bo Zhu, and Matthew S. Rosen. "MR fingerprinting Deep RecOnstruction NEtwork (DRONE)." Magnetic Resonance in Medicine 80, no. 3 (April 6, 2018): 885–94. http://dx.doi.org/10.1002/mrm.27198.

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Benjamin, Arnold Julian Vinoj, Pedro A. Gómez, Mohammad Golbabaee, Zaid Bin Mahbub, Tim Sprenger, Marion I. Menzel, Michael Davies, and Ian Marshall. "Multi-shot Echo Planar Imaging for accelerated Cartesian MR Fingerprinting: An alternative to conventional spiral MR Fingerprinting." Magnetic Resonance Imaging 61 (September 2019): 20–32. http://dx.doi.org/10.1016/j.mri.2019.04.014.

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Chen, Yong, Yun Jiang, Shivani Pahwa, Dan Ma, Lan Lu, Michael D. Twieg, Katherine L. Wright, Nicole Seiberlich, Mark A. Griswold, and Vikas Gulani. "MR Fingerprinting for Rapid Quantitative Abdominal Imaging." Radiology 279, no. 1 (April 2016): 278–86. http://dx.doi.org/10.1148/radiol.2016152037.

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Cauley, Stephen F., Kawin Setsompop, Dan Ma, Yun Jiang, Huihui Ye, Elfar Adalsteinsson, Mark A. Griswold, and Lawrence L. Wald. "Fast group matching for MR fingerprinting reconstruction." Magnetic Resonance in Medicine 74, no. 2 (August 28, 2014): 523–28. http://dx.doi.org/10.1002/mrm.25439.

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Anderson, Christian E., Charlie Y. Wang, Yuning Gu, Rebecca Darrah, Mark A. Griswold, Xin Yu, and Chris A. Flask. "Regularly incremented phase encoding – MR fingerprinting (RIPE‐MRF) for enhanced motion artifact suppression in preclinical cartesian MR fingerprinting." Magnetic Resonance in Medicine 79, no. 4 (August 10, 2017): 2176–82. http://dx.doi.org/10.1002/mrm.26865.

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Zou, Lixian, Dong Liang, Huihui Ye, Shi Su, Yanjie Zhu, Xin Liu, Hairong Zheng, and Haifeng Wang. "Quantitative MR relaxation using MR fingerprinting with fractional-order signal evolution." Journal of Magnetic Resonance 330 (September 2021): 107042. http://dx.doi.org/10.1016/j.jmr.2021.107042.

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Dissertations / Theses on the topic "MR Fingerprinting"

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Barbieri, Marco <1991&gt. "Advances in the Role of Quantitative NMR in Medicine: Deep Learning applied to MR Fingerprinting and Trabecular Bone Volume Fraction Estimation through Single-Sided NMR." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amsdottorato.unibo.it/9236/1/Ph_D_Thesis_Marco_Barbieri.pdf.

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Nuclear Magnetic Resonance (NMR) has been a powerful and widespread tool since its birth thanks to its flexibility in assessing properties of physical systems without being invasive and without using ionizing radiations. Although applications of NMR for medical purposes have rapidly developed since the introduction of MR imaging (MRI), most of the clinical protocols retrieve qualitative information about biological tissues. Being able to retrieve also quantitative information with NMR may be beneficial to identify biomarkers for understanding and describing the pathophysiology of complex diseases in many tissues. However, established quantitative MRI (qMRI) methods require long scan times that not only can represent more exposure to image artifacts and more discomfort for the patient, but they also increase the costs of MRI protocols. To improve the clinical feasibility of quantitative NMR, one can focus on optimizing qMRI protocols to increase data acquisition efficiency, i.e. minimizing the acquisition times and maximising the number of retrieved information. Alternatively, one can focus on the application of low-cost, portable and low maintenance NMR devices in the medical field, such as single-sided devices. This Ph.D thesis presents studies that aim to advance the role of quantitative NMR in medicine using the two directions stated above. The first part of the thesis proposes a deep learning approach based on deep Fully Connected Networks (NN), for pixel-wise MR parameter prediction task in Magnetic Resonance Fingerprinting (MRF) as a solution to overcome the curse of dimensionality affecting the gold standard dictionary approach. The second part proposes a methodology to assess the trabecular bone-volume-to-total-volume (BV/TV) ratio using single-side NMR by means of NMR relaxometry measurements. Nowadays there are not well-established methodologies to assess trabecular BV/TV that are suitable for wide screening campaigns of the population at risk of bone fractures related to diseases such as osteoporosis.
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Coudert, Thomas. "IRM «fingerprint» et Intelligence Artificielle pour la prise en charge des patients victimes d'un AVC." Electronic Thesis or Diss., Université Grenoble Alpes, 2024. http://www.theses.fr/2024GRALY044.

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Les accidents vasculaires cérébraux (AVC), cause majeure de mortalité et d'invalidité à long terme dans le monde, nécessitent un diagnostic rapide et précis pour optimiser les résultats du traitement. Les techniques d'imagerie actuelles, en particulier l'IRM, sont essentielles pour évaluer l'étendue des lésions cérébrales et guider les interventions thérapeutiques. Toutefois, les protocoles IRM traditionnels prennent souvent beaucoup de temps et peuvent manquer de la précision requise pour une analyse détaillée du tissu cérébral ischémique, ce qui limite leur utilité dans les situations d'AVC aiguës où le temps est un facteur essentiel.L'IRM Fingerprint (MRF) est une solution relativement nouvelle qui permet d'estimer simultanément plusieurs paramètres quantitatifs cérébraux à partir d'acquisitions rapides haute résolution en utilisant une approche de recherche par dictionnaire. Cependant, son extension pour les estimations microvasculaires (textit{e.g.} volume sanguin cérébral (CBV) ou diamètre des vaisseaux sanguins (R)) et d'oxygénation du cerveau repose actuellement sur l'injection d'agents de contraste exogènes (CA) qui limitent l'application clinique et la vitesse d'acquisition. Dans cette thèse, nous avons cherché à résoudre ces limitations en développant une nouvelle technique intégrée d'IRM Fingerprint sans agent de contraste, améliorée par intelligence artificielle (IA) et adaptée aux urgences des AVC.Tout d'abord, nous avons développé et adapté des techniques MRF multiparamétriques standard basées sur des séquences à écho de gradient spoilé. En implémentant dans ces outils des corrections d'artefacts du scanner, la compression des dictionnaires et des reconstructions en espace réduit, nous avons pu générer des cartes de relaxométrie (T1,T2) et des contrastes d'IRM standard à partir d'une seule séquence MRF. Cependant, les informations microvasculaires fournies par notre nouveau modèle MRF multi-compartiments chez les volontaires humains souffraient d'un faible rapport signal/bruit.Nous nous sommes donc concentrés sur un nouveau design de séquence MRF basée sur des séquences GRE équilibrées et leur remarquable sensibilité aux inhomogénéités du champ magnétique. Après une étude théorique et textit{in-silico} sur les sensibilités des séquences générales à l'effet BOLD (Blood Oxygen Level Dependent) et l'impact des paramètres d'acquisition MRF, nous avons conçu une nouvelle séquence MRF-bSSFP qui estime simultanément des cartes de relaxométrie (T1,T2,T2*,M0), de champs magnétiques (B1,B0) et de propriétés microvasculaires (CBV,R) sans qu'il soit nécessaire d'injecter un produit de contraste. En utilisant un nouveau pipeline pour ces simulations MRF, nous avons testé notre méthode sur une cohorte de volontaires humains.Nous avons ensuite développé des méthodes de reconstruction avancées pour les acquisitions MRF à haute dimension en s'appuyant sur des modèles de faible rang et des réseaux neuronaux profonds. Enfin, nous avons utilisé notre pipeline de simulation combiné à des réseaux neuronaux récurrents pour accélérer nos temps de calcul d'un facteur 800 et permettre l'inclusion des effets de diffusion de l'eau. Cette approche a été testée sur des données précliniques rétrospectives comprenant des animaux sains et des animaux ayant subi un AVC et les résultats ont suggéré que des estimations supplémentaires de l'ADC ou de l'oxygénation sanguine pouvaient être mesurées avec notre nouvelle séquence MRF bSSFP.Après une validation et une optimisation minutieuses, ce travail méthodologique fournira une solution d'imagerie efficace, alignée sur les contraintes de temps critiques des soins de l'AVC en phase aigu. Notre protocole général pour les acquisitions MRF à haute dimension incluant les effets des microvascularités pourrait également être utilisé dans diverses autres pathologies cérébrales
Stroke, a major cause of mortality and long-term disability worldwide, necessitates rapid and accurate diagnosis to optimize treatment outcomes. Current imaging techniques, particularly MRI, are critical for assessing the extent of brain injury and guiding therapeutic interventions. However, traditional MRI protocols are often time-consuming and may lack the precision required for detailed analysis of ischemic brain tissue, limiting their utility in acute stroke settings where time is of the essence.Magnetic Resonance Fingerprinting (MRF) is a relatively new solution to simultaneously map several brain quantitative parameters from fast, high-resolution acquisitions using a dictionary search approach. However, its extension for microvascular (e.g. cerebral blood volume (CBV) or blood vessel diameter (R)) and brain oxygenation estimates currently relies on the injection of exogenous contrast agents (CA) that limit the clinical application and acquisition speed. In this thesis, we aimed to address these limitations by developing a novel and integrated, artificial intelligence (AI) augmented contrast-free MRF technique tailored for stroke emergencies.First, we developed and adapted standard multiparametric MRF techniques based on spoiled gradient echo MRI sequences. Using scanner artifacts corrections, dictionary compression, and subspace reconstruction, we were able to generate fast relaxometry (T1,T2) maps and standard MRI contrasts from a single MRF sequence. However, the microvascular information provided by our new multi-compartment MRF model in human volunteers suffered from a low signal-to-noise ratio.We thus focused on a new MRF sequence design based on balanced GRE sequences and their remarkable sensitivity to magnetic field inhomogeneities. After a theoretical and textit{in-silico} study on general sequences sensitivities to the Blood Oxygen Level Dependent (BOLD) effect and the impact of MRF acquisition parameters, we designed a new MRF-bSSFP sequence that simultaneously estimate relaxometry (T1,T2,T2*,M0), magnetic fields (B1,B0), and microvascular properties (CBV,R) without the need for CA injection. Using a new pipeline for MRF simulations, the proposed method was tested in a cohort of human volunteers.Our method was further refined by developing advanced reconstruction methods for high dimensional MRF acquisitions relying on low-rank models and deep neural networks. We finally used our simulation framework combined with Recurrent Neural Networks to fasten our computation times by a factor of 800 and allow the inclusion of water-diffusion effects. This approach was tested in retrospective preclinical data including healthy and stroke animals and the results suggested that additional estimates of ADC or blood oxygenation could be measured with our new bSSFP MRF sequence.After careful validation and optimization, this methodological work could provide an efficient imaging solution that aligns with the critical time constraints of acute stroke care. Our general framework for high dimensional MRF acquisitions that include microstructure effects could also be used in various other pathologies
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Lin, Te-Ming, and 林德銘. "A method to evaluate the relationship between signal acquisition number and parametric mapping precision in MR fingerprinting." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/30205508647644523764.

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碩士
國立臺灣大學
生醫電子與資訊學研究所
103
MR Fingerprinting (MRF) is a novel technique to quantify multiple MR parameters simultaneously. A train of pseudorandomized radiofrequency (RF) excitations are used to generate unique signal evolution for different tissues, followed by matching the measured signals to a pre-established dictionary. The signal acquisition number in MRF is related to the signal length. Longer signals and larger dictionaries increase the scan time and computational complexity. However, as the signal acquisition reduces, the mapping precision also changes. Therefore, for designing an efficient MRF sequence, a method to evaluate the precision change is necessary. In this thesis, we propose a mapping variation index to reflect the mapping precision in MRF. Besides, this index can predict the precision change under different signal acquisition numbers before MRF scans and provide a reference for sequence designers to modify the signal acquisition number.
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Book chapters on the topic "MR Fingerprinting"

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Runge, Val M., and Johannes T. Heverhagen. "MR Fingerprinting." In The Physics of Clinical MR Taught Through Images, 312–13. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85413-3_141.

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Chen, Yong, Christina J. MacAskill, Sherry Huang, Katherine M. Dell, Sree H. Tirumani, Mark A. Griswold, and Chris A. Flask. "MR Fingerprinting for Quantitative Kidney Imaging." In Advanced Clinical MRI of the Kidney, 163–80. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-40169-5_12.

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Chen, Dongdong, Mike E. Davies, and Mohammad Golbabaee. "Compressive MR Fingerprinting Reconstruction with Neural Proximal Gradient Iterations." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 13–22. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59713-9_2.

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Balsiger, Fabian, Alain Jungo, Olivier Scheidegger, Benjamin Marty, and Mauricio Reyes. "Learning Bloch Simulations for MR Fingerprinting by Invertible Neural Networks." In Machine Learning for Medical Image Reconstruction, 60–69. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61598-7_6.

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Kang, Beomgu, Hye-Young Heo, and HyunWook Park. "Only-Train-Once MR Fingerprinting for Magnetization Transfer Contrast Quantification." In Lecture Notes in Computer Science, 387–96. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-16446-0_37.

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Barrier, Antoine, Thomas Coudert, Aurélien Delphin, Benjamin Lemasson, and Thomas Christen. "MARVEL: MR Fingerprinting with Additional micRoVascular Estimates Using Bidirectional LSTMs." In Lecture Notes in Computer Science, 259–69. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-72069-7_25.

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Cheng, Feng, Yong Chen, Xiaopeng Zong, Weili Lin, Dinggang Shen, and Pew-Thian Yap. "Acceleration of High-Resolution 3D MR Fingerprinting via a Graph Convolutional Network." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 158–66. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59713-9_16.

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Gómez, Pedro A., Miguel Molina-Romero, Cagdas Ulas, Guido Bounincontri, Jonathan I. Sperl, Derek K. Jones, Marion I. Menzel, and Bjoern H. Menze. "Simultaneous Parameter Mapping, Modality Synthesis, and Anatomical Labeling of the Brain with MR Fingerprinting." In Lecture Notes in Computer Science, 579–86. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46726-9_67.

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Cheng, Feng, Yong Chen, Xiaopeng Zong, Weili Lin, Dinggang Shen, and Pew-Thian Yap. "Correction to: Acceleration of High-Resolution 3D MR Fingerprinting via a Graph Convolutional Network." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, C1. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59713-9_75.

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Ma, Dan. "MR fingerprinting: concepts, implementation and applications." In Advances in Magnetic Resonance Technology and Applications, 435–49. Elsevier, 2021. http://dx.doi.org/10.1016/b978-0-12-822479-3.00044-0.

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Conference papers on the topic "MR Fingerprinting"

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Li, Shizhuo, Huihui Ye, and Huafeng Liu. "CRLB-Based Optimization for Combined FISP and PSIF MR Fingerprinting." In 2024 IEEE International Symposium on Biomedical Imaging (ISBI), 1–5. IEEE, 2024. http://dx.doi.org/10.1109/isbi56570.2024.10635327.

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Ma, Dan. "Clinical applications of fast and quantitative MR fingerprinting." In Biomedical Applications in Molecular, Structural, and Functional Imaging, edited by Barjor S. Gimi and Andrzej Krol. SPIE, 2023. http://dx.doi.org/10.1117/12.2664416.

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Venglovskyi, Iurii. "Single-Voxel Proton MR-Spectroscopy Signal Analysis by Fingerprinting." In 2021 13th International Conference on Measurement. IEEE, 2021. http://dx.doi.org/10.23919/measurement52780.2021.9446829.

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Li, Zehao, Min Li, and Zhuo Zhang. "Accelerated MR Fingerprinting Reconstruction Using Dictionary and Local Low-Rank Regularizations." In 2021 7th International Conference on Computer and Communications (ICCC). IEEE, 2021. http://dx.doi.org/10.1109/iccc54389.2021.9674702.

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Li, Peng, and Yue Hu. "Mr Fingerprinting Reconstruction Using Structured Low-Rank Matrix Recovery And Subspace Modeling." In 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI). IEEE, 2021. http://dx.doi.org/10.1109/isbi48211.2021.9434120.

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"Accurate Dictionary Matching for MR Fingerprinting Using Neural Networks and Feature Extraction." In 2020 28th Signal Processing and Communications Applications Conference (SIU). IEEE, 2020. http://dx.doi.org/10.1109/siu49456.2020.9302455.

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Lu, Hengfa, Huihui Ye, and Bo Zhao. "Improved Balanced Steady-State Free Precession Based MR Fingerprinting with Deep Autoencoders." In 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2022. http://dx.doi.org/10.1109/embc48229.2022.9871003.

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Hu, Dakun, Huihui Ye, and Huafeng Liu. "gSlider RF encoded MR fingerprinting with thin slice thickness, high accuracy and reproducibility." In ICBET 2024: 2024 14th International Conference on Biomedical Engineering and Technology, 51–57. New York, NY, USA: ACM, 2024. http://dx.doi.org/10.1145/3678935.3678944.

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Keil, V., S. Bakoeva, A. Jurcoane, P. Koken, M. Doneva, T. Amthor, B. Mädler, W. Block, H. Schild, and E. Hattingen. "MR Fingerprinting: Wie vergleichbar ist die neuartige Mappingtechnik mit konventionellem T1 und T2 Mapping?" In 99. Deutscher Röntgenkongress. Georg Thieme Verlag KG, 2018. http://dx.doi.org/10.1055/s-0038-1641420.

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Keil, V., S. Bakoeva, A. Jurcoane, T. Amthor, M. Doneva, P. Koken, B. Mädler, W. Block, H. Schild, and E. Hattingen. "Quantitatives T1 und T2 Mapping mit MR Fingerprinting machen Alterungsprozesse des Gehirns mit geringem Aufwand messbar." In 99. Deutscher Röntgenkongress. Georg Thieme Verlag KG, 2018. http://dx.doi.org/10.1055/s-0038-1641421.

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