Relevant bibliographies by topics / MR Fingerprinting

Contents

  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
Last updated: 31 July 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 (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 (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 (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 (2018): 885–94. http://dx.doi.org/10.1002/mrm.27198.

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Benjamin, Arnold Julian Vinoj, Pedro A. Gómez, Mohammad Golbabaee, et al. "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, et al. "MR Fingerprinting for Rapid Quantitative Abdominal Imaging." Radiology 279, no. 1 (2016): 278–86. http://dx.doi.org/10.1148/radiol.2016152037.

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Cauley, Stephen F., Kawin Setsompop, Dan Ma, et al. "Fast group matching for MR fingerprinting reconstruction." Magnetic Resonance in Medicine 74, no. 2 (2014): 523–28. http://dx.doi.org/10.1002/mrm.25439.

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Anderson, Christian E., Charlie Y. Wang, Yuning Gu, et al. "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 (2017): 2176–82. http://dx.doi.org/10.1002/mrm.26865.

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Zou, Lixian, Dong Liang, Huihui Ye, et al. "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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Slioussarenko, Constantin. "Whole-body quantitative imaging of the skeletal muscle by Magnetic Resonance Fingerprinting." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPAST188.

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Les maladies neuromusculaires présentent des atteintes systémiques et hétérogènes des muscles squelettiques, pouvant éventuellement entraîner la mort par insuffisance respiratoire ou cardiaque. Cela rend le suivi de l'évolution de la maladie crucial, mais particulièrement complexe. L'IRM multiparamétrique quantitative permet d'évaluer précisément la structure et la composition des tissus musculaires, mais est compliquée à mettre en application dans un contexte clinique en raison des longs temps d'acquisition et de reconstruction, ainsi que du mouvement physiologique pour les muscles respiratoi
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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 disea
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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
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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<br>國立臺灣大學<br>生醫電子與資訊學研究所<br>103<br>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 design
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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. 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, et al. "MR Fingerprinting for Quantitative Kidney Imaging." In Advanced Clinical MRI of the Kidney. 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. 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. 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. 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. 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. 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, et al. "Simultaneous Parameter Mapping, Modality Synthesis, and Anatomical Labeling of the Brain with MR Fingerprinting." In Lecture Notes in Computer Science. 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. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59713-9_75.

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Cohen, Ouri. "Detection and Characterization of Brain Metastases Using Quantitative Chemical Exchange Saturation Transfer MR Fingerprinting (CEST-MRF)." In Cancer Detection and Diagnosis. CRC Press, 2025. https://doi.org/10.1201/9781003449942-21.

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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). 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. ACM, 2024. http://dx.doi.org/10.1145/3678935.3678944.

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Keil, V., S. Bakoeva, A. Jurcoane, et al. "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, et al. "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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Journal articles
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