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Artigos de revistas sobre o tema "MR Fingerprinting"

Autor: Grafiati

Publicado: 25 de janeiro de 2025

Última modificação: 15 de fevereiro de 2025

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1

Flassbeck, Sebastian, Simon Schmidt, Peter Bachert, Mark E. Ladd e Sebastian Schmitter. "Flow MR fingerprinting". Magnetic Resonance in Medicine 81, n.º 4 (2 de dezembro de 2018): 2536–50. http://dx.doi.org/10.1002/mrm.27588.

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2

Pierre, Eric Y., Dan Ma, Yong Chen, Chaitra Badve e Mark A. Griswold. "Multiscale reconstruction for MR fingerprinting". Magnetic Resonance in Medicine 75, n.º 6 (30 de junho de 2015): 2481–92. http://dx.doi.org/10.1002/mrm.25776.

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3

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

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4

Buonincontri, Guido, e Stephen J. Sawiak. "MR fingerprinting with simultaneous B1 estimation". Magnetic Resonance in Medicine 76, n.º 4 (28 de outubro de 2015): 1127–35. http://dx.doi.org/10.1002/mrm.26009.

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5

Cohen, Ouri, Bo Zhu e Matthew S. Rosen. "MR fingerprinting Deep RecOnstruction NEtwork (DRONE)". Magnetic Resonance in Medicine 80, n.º 3 (6 de abril de 2018): 885–94. http://dx.doi.org/10.1002/mrm.27198.

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6

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

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7

Chen, Yong, Yun Jiang, Shivani Pahwa, Dan Ma, Lan Lu, Michael D. Twieg, Katherine L. Wright, Nicole Seiberlich, Mark A. Griswold e Vikas Gulani. "MR Fingerprinting for Rapid Quantitative Abdominal Imaging". Radiology 279, n.º 1 (abril de 2016): 278–86. http://dx.doi.org/10.1148/radiol.2016152037.

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8

Cauley, Stephen F., Kawin Setsompop, Dan Ma, Yun Jiang, Huihui Ye, Elfar Adalsteinsson, Mark A. Griswold e Lawrence L. Wald. "Fast group matching for MR fingerprinting reconstruction". Magnetic Resonance in Medicine 74, n.º 2 (28 de agosto de 2014): 523–28. http://dx.doi.org/10.1002/mrm.25439.

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9

Anderson, Christian E., Charlie Y. Wang, Yuning Gu, Rebecca Darrah, Mark A. Griswold, Xin Yu e 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, n.º 4 (10 de agosto de 2017): 2176–82. http://dx.doi.org/10.1002/mrm.26865.

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10

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

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11

Budaházi, Árpád, e Zsanett Fantoly. "Brain Fingerprinting as a Criminalistics Technique and Method". Magyar Rendészet 19, n.º 1 (2019): 35–49. http://dx.doi.org/10.32577/mr.2019.1.2.

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12

Badve, C., A. Yu, S. Dastmalchian, M. Rogers, D. Ma, Y. Jiang, S. Margevicius et al. "MR Fingerprinting of Adult Brain Tumors: Initial Experience". American Journal of Neuroradiology 38, n.º 3 (29 de dezembro de 2016): 492–99. http://dx.doi.org/10.3174/ajnr.a5035.

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13

Springer, Elisabeth, Pedro Lima Cardoso, Bernhard Strasser, Wolfgang Bogner, Matthias Preusser, Georg Widhalm, Mathias Nittka et al. "MR Fingerprinting—A Radiogenomic Marker for Diffuse Gliomas". Cancers 14, n.º 3 (30 de janeiro de 2022): 723. http://dx.doi.org/10.3390/cancers14030723.

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(1) Background: Advanced MR imaging (MRI) of brain tumors is mainly based on qualitative contrast images. MR Fingerprinting (MRF) offers a novel approach. The purpose of this study was to use MRF-derived T1 and T2 relaxation maps to differentiate diffuse gliomas according to isocitrate dehydrogenase (IDH) mutation. (2) Methods: Twenty-four patients with histologically verified diffuse gliomas (14 IDH-mutant, four 1p/19q-codeleted, 10 IDH-wildtype) were enrolled. MRF T1 and T2 relaxation times were compared to apparent diffusion coefficient (ADC), relative cerebral blood volume (rCBV) within solid tumor, peritumoral edema, and normal-appearing white matter (NAWM), using contrast-enhanced MRI, diffusion-, perfusion-, and susceptibility-weighted imaging. For perfusion imaging, a T2* weighted perfusion sequence with leakage correction was used. Correlations of MRF T1 and T2 times with two established conventional sequences for T1 and T2 mapping were assessed (a fast double inversion recovery-based MR sequence (‘MP2RAGE’) for T1 quantification and a multi-contrast spin echo-based sequence for T2 quantification). (3) Results: MRF T1 and T2 relaxation times were significantly higher in the IDH-mutant than in IDH-wildtype gliomas within the solid part of the tumor (p = 0.024 for MRF T1, p = 0.041 for MRF T2). MRF T1 and T2 relaxation times were significantly higher in the IDH-wildtype than in IDH-mutant gliomas within peritumoral edema less than or equal to 1cm adjacent to the tumor (p = 0.038 for MRF T1 mean, p = 0.010 for MRF T2 mean). In the solid part of the tumor, there was a high correlation between MRF and conventionally measured T1 and T2 values (r = 0.913, p < 0.001 for T1, r = 0.775, p < 0.001 for T2), as well as between MRF and ADC values (r = 0.813, p < 0.001 for T2, r = 0.697, p < 0.001 for T1). The correlation was weak between the MRF and rCBV values (r = −0.374, p = 0.005 for T2, r = −0.181, p = 0.181 for T1). (4) Conclusions: MRF enables fast, single-sequence based, multi-parametric, quantitative tissue characterization of diffuse gliomas and may have the potential to differentiate IDH-mutant from IDH-wildtype gliomas.

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14

Cohen, Ouri, e Matthew S. Rosen. "Algorithm comparison for schedule optimization in MR fingerprinting". Magnetic Resonance Imaging 41 (setembro de 2017): 15–21. http://dx.doi.org/10.1016/j.mri.2017.02.010.

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15

Chen, Yong, Ananya Panda, Shivani Pahwa, Jesse I. Hamilton, Sara Dastmalchian, Debra F. McGivney, Dan Ma et al. "Three-dimensional MR Fingerprinting for Quantitative Breast Imaging". Radiology 290, n.º 1 (janeiro de 2019): 33–40. http://dx.doi.org/10.1148/radiol.2018180836.

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16

Assländer, Jakob, Steffen J. Glaser e Jürgen Hennig. "Pseudo Steady-State Free Precession for MR-Fingerprinting". Magnetic Resonance in Medicine 77, n.º 3 (15 de abril de 2016): 1151–61. http://dx.doi.org/10.1002/mrm.26202.

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17

Zhang, Qiang, Pan Su, Zhensen Chen, Ying Liao, Shuo Chen, Rui Guo, Haikun Qi et al. "Deep learning–based MR fingerprinting ASL ReconStruction (DeepMARS)". Magnetic Resonance in Medicine 84, n.º 2 (4 de fevereiro de 2020): 1024–34. http://dx.doi.org/10.1002/mrm.28166.

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18

MacAskill, Christina J., Michael Markley, Susan Farr, Ashlee Parsons, Jacob R. Perino, Kimberly McBennett, Katherine Kutney et al. "Rapid B1-Insensitive MR Fingerprinting for Quantitative Kidney Imaging". Radiology 300, n.º 2 (agosto de 2021): 380–87. http://dx.doi.org/10.1148/radiol.2021202302.

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19

Ropella-Panagis, Kathleen M., Nicole Seiberlich e Vikas Gulani. "Magnetic Resonance Fingerprinting: Implications and Opportunities for PET/MR". IEEE Transactions on Radiation and Plasma Medical Sciences 3, n.º 4 (julho de 2019): 388–99. http://dx.doi.org/10.1109/trpms.2019.2897425.

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20

Sommer, K., T. Amthor, M. Doneva, P. Koken, J. Meineke e P. Börnert. "Towards predicting the encoding capability of MR fingerprinting sequences". Magnetic Resonance Imaging 41 (setembro de 2017): 7–14. http://dx.doi.org/10.1016/j.mri.2017.06.015.

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21

Su, Pan, Deng Mao, Peiying Liu, Yang Li, Marco C. Pinho, Babu G. Welch e Hanzhang Lu. "Multiparametric estimation of brain hemodynamics with MR fingerprinting ASL". Magnetic Resonance in Medicine 78, n.º 5 (26 de dezembro de 2016): 1812–23. http://dx.doi.org/10.1002/mrm.26587.

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22

Fang, Zhenghan, Yong Chen, Sheng‐Che Hung, Xiaoxia Zhang, Weili Lin e Dinggang Shen. "Submillimeter MR fingerprinting using deep learning–based tissue quantification". Magnetic Resonance in Medicine 84, n.º 2 (19 de dezembro de 2019): 579–91. http://dx.doi.org/10.1002/mrm.28136.

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23

Budaházi, Árpád, Zsanett Fantoly, Brigitta Kakuszi, István Bitter e Pál Czobor. "The Options and Limitations of the Brain Fingerprinting Lie Detection Method in the Criminal Proceeding". Magyar Rendészet 18, n.º 5 (2018): 43–56. http://dx.doi.org/10.32577/mr.2018.5.3.

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The aim of this study is to introduce the new lie detection method of brain fingerprinting already introduced in the United States of America. According to some scholars, the method of a brain-focused instrumental credibility examination of testimonies still unknown in Hungary is highly reliable, establishing their concept on their belief that the human brain does not lie. First of all, we shall examine the possibilities lying in the measure, and second of all, we shall introduce the doubts causing the delay of its admission in Hungary.

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24

Budaházi, Árpád, Zsanett Fantoly, Brigitta Kakuszi, István Bitter e Pál Czobor. "The Options and Limitations of the Brain Fingerprinting Lie Detection Method in the Criminal Proceeding". Magyar Rendészet 18, n.º 5 (2018): 43–56. http://dx.doi.org/10.32577/mr.2018.5.3.

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The aim of this study is to introduce the new lie detection method of brain fingerprinting already introduced in the United States of America. According to some scholars, the method of a brain-focused instrumental credibility examination of testimonies still unknown in Hungary is highly reliable, establishing their concept on their belief that the human brain does not lie. First of all, we shall examine the possibilities lying in the measure, and second of all, we shall introduce the doubts causing the delay of its admission in Hungary.

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25

Laustsen, Christoffer. "Renal MR Fingerprinting: A Novel Solution to a Complex Problem". Radiology 300, n.º 2 (agosto de 2021): 388–89. http://dx.doi.org/10.1148/radiol.2021210924.

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26

Cruz, Gastao, Haikun Qi, Olivier Jaubert, Thomas Kuestner, Torben Schneider, Rene Michael Botnar e Claudia Prieto. "Generalized low‐rank nonrigid motion‐corrected reconstruction for MR fingerprinting". Magnetic Resonance in Medicine 87, n.º 2 (2 de outubro de 2021): 746–63. http://dx.doi.org/10.1002/mrm.29027.

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27

Buonincontri, Guido, Rolf F. Schulte, Mirco Cosottini e Michela Tosetti. "Spiral MR fingerprinting at 7 T with simultaneous B1 estimation". Magnetic Resonance Imaging 41 (setembro de 2017): 1–6. http://dx.doi.org/10.1016/j.mri.2017.04.003.

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28

Wright, Katherine L., Yun Jiang, Dan Ma, Douglas C. Noll, Mark A. Griswold, Vikas Gulani e Luis Hernandez-Garcia. "Estimation of perfusion properties with MR Fingerprinting Arterial Spin Labeling". Magnetic Resonance Imaging 50 (julho de 2018): 68–77. http://dx.doi.org/10.1016/j.mri.2018.03.011.

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29

Prayer, Daniela. "MR Fingerprinting: An Advance for Patients with Temporal Lobe Epilepsy". Radiology 288, n.º 3 (setembro de 2018): 813–14. http://dx.doi.org/10.1148/radiol.2018180865.

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30

Jiang, Yun, Dan Ma, Renate Jerecic, Jeffrey Duerk, Nicole Seiberlich, Vikas Gulani e Mark A. Griswold. "MR fingerprinting using the quick echo splitting NMR imaging technique". Magnetic Resonance in Medicine 77, n.º 3 (28 de fevereiro de 2016): 979–88. http://dx.doi.org/10.1002/mrm.26173.

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31

Mehta, Bhairav Bipin, Dan Ma, Eric Yann Pierre, Yun Jiang, Simone Coppo e Mark Alan Griswold. "Image reconstruction algorithm for motion insensitive MR Fingerprinting (MRF): MORF". Magnetic Resonance in Medicine 80, n.º 6 (6 de maio de 2018): 2485–500. http://dx.doi.org/10.1002/mrm.27227.

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32

Keil, Vera C. "Neue Methoden in der Neuroradiologie: MR-Fingerprinting und synthetische Bildgebung". Radiologie up2date 23, n.º 02 (junho de 2023): 101–16. http://dx.doi.org/10.1055/a-2010-0600.

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ZusammenfassungWas bedeutet „synthetische Bildgebung“? Ist es eine bestimmte Form der Akquisition oder kann man auch durch Postprocessing von Standard-MRT-Aufnahmen 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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33

Keil, Vera Catharina. "Neue Methoden in der Neuroradiologie: MR-Fingerprinting und synthetische Bildgebung". Neurologie up2date 06, n.º 04 (dezembro de 2023): 325–41. http://dx.doi.org/10.1055/a-2181-0117.

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34

Vaccaro, M., e A. Napolitano. "SC07.02 SINGLE-SITE REPRODUCIBILITY IN PULSEQ-DESIGNED MR FINGERPRINTING SEQUENCES". Physica Medica 125 (setembro de 2024): 103457. http://dx.doi.org/10.1016/j.ejmp.2024.103457.

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35

Marriott, Anna, Chris Bowen, James Rioux e Kimberly Brewer. "Simultaneous quantification of SPIO and gadolinium contrast agents using MR fingerprinting". Magnetic Resonance Imaging 79 (junho de 2021): 121–29. http://dx.doi.org/10.1016/j.mri.2021.03.017.

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36

Badve, Chaitra, Sara Dastmalchian, Ozden Kilinc, Debra McGivney, Dan Ma, Mark Griswold, Jeffrey Sunshine, Vikas Gulani, Jill Barnholtz-Sloan e Andrew Sloan. "NIMG-90. TEXTURE ANALYSIS OF MR FINGERPRINTING IN ADULT BRAIN TUMORS". Neuro-Oncology 19, suppl_6 (novembro de 2017): vi162. http://dx.doi.org/10.1093/neuonc/nox168.659.

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37

Chen, Yong, Zhenghan Fang, Sheng-Che Hung, Wei-Tang Chang, Dinggang Shen e Weili Lin. "High-resolution 3D MR Fingerprinting using parallel imaging and deep learning". NeuroImage 206 (fevereiro de 2020): 116329. http://dx.doi.org/10.1016/j.neuroimage.2019.116329.

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38

Körzdörfer, Gregor, Rainer Kirsch, Kecheng Liu, Josef Pfeuffer, Bernhard Hensel, Yun Jiang, Dan Ma et al. "Reproducibility and Repeatability of MR Fingerprinting Relaxometry in the Human Brain". Radiology 292, n.º 2 (agosto de 2019): 429–37. http://dx.doi.org/10.1148/radiol.2019182360.

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39

Cao, Xiaozhi, Congyu Liao, Zhixing Wang, Ying Chen, Huihui Ye, Hongjian He e Jianhui Zhong. "Robust sliding-window reconstruction for Accelerating the acquisition of MR fingerprinting". Magnetic Resonance in Medicine 78, n.º 4 (7 de novembro de 2016): 1579–88. http://dx.doi.org/10.1002/mrm.26521.

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40

Assländer, Jakob, Martijn A. Cloos, Florian Knoll, Daniel K. Sodickson, Jürgen Hennig e Riccardo Lattanzi. "Low rank alternating direction method of multipliers reconstruction for MR fingerprinting". Magnetic Resonance in Medicine 79, n.º 1 (5 de março de 2017): 83–96. http://dx.doi.org/10.1002/mrm.26639.

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41

Yang, Mingrui, Dan Ma, Yun Jiang, Jesse Hamilton, Nicole Seiberlich, Mark A. Griswold e Debra McGivney. "Low rank approximation methods for MR fingerprinting with large scale dictionaries". Magnetic Resonance in Medicine 79, n.º 4 (13 de agosto de 2017): 2392–400. http://dx.doi.org/10.1002/mrm.26867.

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42

Kratzer, Fabian J., Sebastian Flassbeck, Armin M. Nagel, Nicolas G. R. Behl, Benjamin R. Knowles, Peter Bachert, Mark E. Ladd e Sebastian Schmitter. "Sodium relaxometry using 23 Na MR fingerprinting: A proof of concept". Magnetic Resonance in Medicine 84, n.º 5 (18 de junho de 2020): 2577–91. http://dx.doi.org/10.1002/mrm.28316.

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43

Mostardeiro, Thomaz R., Ananya Panda, Robert J. Witte, Norbert G. Campeau, Kiaran P. McGee, Yi Sui e Aiming Lu. "Whole-brain 3D MR fingerprinting brain imaging: clinical validation and feasibility to patients with meningioma". Magnetic Resonance Materials in Physics, Biology and Medicine 34, n.º 5 (4 de maio de 2021): 697–706. http://dx.doi.org/10.1007/s10334-021-00924-1.

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Abstract Purpose MR fingerprinting (MRF) is a MR technique that allows assessment of tissue relaxation times. The purpose of this study is to evaluate the clinical application of this technique in patients with meningioma. Materials and methods A whole-brain 3D isotropic 1mm3 acquisition under a 3.0T field strength was used to obtain MRF T1 and T2-based relaxometry values in 4:38 s. The accuracy of values was quantified by scanning a quantitative MR relaxometry phantom. In vivo evaluation was performed by applying the sequence to 20 subjects with 25 meningiomas. Regions of interest included the meningioma, caudate head, centrum semiovale, contralateral white matter and thalamus. For both phantom and subjects, mean values of both T1 and T2 estimates were obtained. Statistical significance of differences in mean values between the meningioma and other brain structures was tested using a Friedman’s ANOVA test. Results MR fingerprinting phantom data demonstrated a linear relationship between measured and reference relaxometry estimates for both T1 (r2 = 0.99) and T2 (r2 = 0.97). MRF T1 relaxation times were longer in meningioma (mean ± SD 1429 ± 202 ms) compared to thalamus (mean ± SD 1054 ± 58 ms; p = 0.004), centrum semiovale (mean ± SD 825 ± 42 ms; p < 0.001) and contralateral white matter (mean ± SD 799 ± 40 ms; p < 0.001). MRF T2 relaxation times were longer for meningioma (mean ± SD 69 ± 27 ms) as compared to thalamus (mean ± SD 27 ± 3 ms; p < 0.001), caudate head (mean ± SD 39 ± 5 ms; p < 0.001) and contralateral white matter (mean ± SD 35 ± 4 ms; p < 0.001) Conclusions Phantom measurements indicate that the proposed 3D-MRF sequence relaxometry estimations are valid and reproducible. For in vivo, entire brain coverage was obtained in clinically feasible time and allows quantitative assessment of meningioma in clinical practice.

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44

Wang, Mandi, Jose A. U. Perucho, Peng Cao, Varut Vardhanabhuti, Di Cui, Yiang Wang, Pek-Lan Khong, Edward S. Hui e Elaine Y. P. Lee. "Repeatability of MR fingerprinting in normal cervix and utility in cervical carcinoma". Quantitative Imaging in Medicine and Surgery 11, n.º 9 (setembro de 2021): 3990–4003. http://dx.doi.org/10.21037/qims-20-1382.

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45

Campbell-Washburn, Adrienne E., Yun Jiang, Gregor Körzdörfer, Mathias Nittka e Mark A. Griswold. "Feasibility of MR fingerprinting using a high-performance 0.55 T MRI system". Magnetic Resonance Imaging 81 (setembro de 2021): 88–93. http://dx.doi.org/10.1016/j.mri.2021.06.002.

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46

Badve, Chaitra, Ozden Kilinc, Louisa Onyewadume, Sara Dastmalchian, Dan Ma, Samuel Frankel, Gregory O’Connor et al. "NIMG-15. VOLUMETRIC 3D MR FINGERPRINTING OF ADULT BRAIN TUMORS: INITIAL RESULTS". Neuro-Oncology 19, suppl_6 (novembro de 2017): vi145. http://dx.doi.org/10.1093/neuonc/nox168.593.

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47

Yu, Alice C., Chaitra Badve, Lee E. Ponsky, Shivani Pahwa, Sara Dastmalchian, Matthew Rogers, Yun Jiang et al. "Development of a Combined MR Fingerprinting and Diffusion Examination for Prostate Cancer". Radiology 283, n.º 3 (junho de 2017): 729–38. http://dx.doi.org/10.1148/radiol.2017161599.

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48

Liao, Congyu, Kang Wang, Xiaozhi Cao, Yueping Li, Dengchang Wu, Huihui Ye, Qiuping Ding, Hongjian He e Jianhui Zhong. "Detection of Lesions in Mesial Temporal Lobe Epilepsy by Using MR Fingerprinting". Radiology 288, n.º 3 (setembro de 2018): 804–12. http://dx.doi.org/10.1148/radiol.2018172131.

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49

Cao, Xiaozhi, Huihui Ye, Congyu Liao, Qing Li, Hongjian He e Jianhui Zhong. "Fast 3D brain MR fingerprinting based on multi‐axis spiral projection trajectory". Magnetic Resonance in Medicine 82, n.º 1 (18 de março de 2019): 289–301. http://dx.doi.org/10.1002/mrm.27726.

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50

Nagtegaal, Martijn, Peter Koken, Thomas Amthor e Mariya Doneva. "Fast multi‐component analysis using a joint sparsity constraint for MR fingerprinting". Magnetic Resonance in Medicine 83, n.º 2 (16 de agosto de 2019): 521–34. http://dx.doi.org/10.1002/mrm.27947.

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