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Journal articles on the topic 'MR Fingerprinting'

Author: Grafiati

Published: 25 January 2025

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Budaházi, Árpád, and Zsanett Fantoly. "Brain Fingerprinting as a Criminalistics Technique and Method." Magyar Rendészet 19, no. 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, no. 3 (December 29, 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, no. 3 (January 30, 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, and Matthew S. Rosen. "Algorithm comparison for schedule optimization in MR fingerprinting." Magnetic Resonance Imaging 41 (September 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, no. 1 (January 2019): 33–40. http://dx.doi.org/10.1148/radiol.2018180836.

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16

Assländer, Jakob, Steffen J. Glaser, and Jürgen Hennig. "Pseudo Steady-State Free Precession for MR-Fingerprinting." Magnetic Resonance in Medicine 77, no. 3 (April 15, 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, no. 2 (February 4, 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, no. 2 (August 2021): 380–87. http://dx.doi.org/10.1148/radiol.2021202302.

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19

Ropella-Panagis, Kathleen M., Nicole Seiberlich, and Vikas Gulani. "Magnetic Resonance Fingerprinting: Implications and Opportunities for PET/MR." IEEE Transactions on Radiation and Plasma Medical Sciences 3, no. 4 (July 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, and P. Börnert. "Towards predicting the encoding capability of MR fingerprinting sequences." Magnetic Resonance Imaging 41 (September 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, and Hanzhang Lu. "Multiparametric estimation of brain hemodynamics with MR fingerprinting ASL." Magnetic Resonance in Medicine 78, no. 5 (December 26, 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, and Dinggang Shen. "Submillimeter MR fingerprinting using deep learning–based tissue quantification." Magnetic Resonance in Medicine 84, no. 2 (December 19, 2019): 579–91. http://dx.doi.org/10.1002/mrm.28136.

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23

Budaházi, Árpád, Zsanett Fantoly, Brigitta Kakuszi, István Bitter, and Pál Czobor. "The Options and Limitations of the Brain Fingerprinting Lie Detection Method in the Criminal Proceeding." Magyar Rendészet 18, no. 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, and Pál Czobor. "The Options and Limitations of the Brain Fingerprinting Lie Detection Method in the Criminal Proceeding." Magyar Rendészet 18, no. 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, no. 2 (August 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, and Claudia Prieto. "Generalized low‐rank nonrigid motion‐corrected reconstruction for MR fingerprinting." Magnetic Resonance in Medicine 87, no. 2 (October 2, 2021): 746–63. http://dx.doi.org/10.1002/mrm.29027.

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27

Buonincontri, Guido, Rolf F. Schulte, Mirco Cosottini, and Michela Tosetti. "Spiral MR fingerprinting at 7 T with simultaneous B1 estimation." Magnetic Resonance Imaging 41 (September 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, and Luis Hernandez-Garcia. "Estimation of perfusion properties with MR Fingerprinting Arterial Spin Labeling." Magnetic Resonance Imaging 50 (July 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, no. 3 (September 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, and Mark A. Griswold. "MR fingerprinting using the quick echo splitting NMR imaging technique." Magnetic Resonance in Medicine 77, no. 3 (February 28, 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, and Mark Alan Griswold. "Image reconstruction algorithm for motion insensitive MR Fingerprinting (MRF): MORF." Magnetic Resonance in Medicine 80, no. 6 (May 6, 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, no. 02 (June 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, no. 04 (December 2023): 325–41. http://dx.doi.org/10.1055/a-2181-0117.

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34

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

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35

Marriott, Anna, Chris Bowen, James Rioux, and Kimberly Brewer. "Simultaneous quantification of SPIO and gadolinium contrast agents using MR fingerprinting." Magnetic Resonance Imaging 79 (June 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, and Andrew Sloan. "NIMG-90. TEXTURE ANALYSIS OF MR FINGERPRINTING IN ADULT BRAIN TUMORS." Neuro-Oncology 19, suppl_6 (November 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, and Weili Lin. "High-resolution 3D MR Fingerprinting using parallel imaging and deep learning." NeuroImage 206 (February 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, no. 2 (August 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, and Jianhui Zhong. "Robust sliding-window reconstruction for Accelerating the acquisition of MR fingerprinting." Magnetic Resonance in Medicine 78, no. 4 (November 7, 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, and Riccardo Lattanzi. "Low rank alternating direction method of multipliers reconstruction for MR fingerprinting." Magnetic Resonance in Medicine 79, no. 1 (March 5, 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, and Debra McGivney. "Low rank approximation methods for MR fingerprinting with large scale dictionaries." Magnetic Resonance in Medicine 79, no. 4 (August 13, 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, and Sebastian Schmitter. "Sodium relaxometry using 23 Na MR fingerprinting: A proof of concept." Magnetic Resonance in Medicine 84, no. 5 (June 18, 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, and 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, no. 5 (May 4, 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, and Elaine Y. P. Lee. "Repeatability of MR fingerprinting in normal cervix and utility in cervical carcinoma." Quantitative Imaging in Medicine and Surgery 11, no. 9 (September 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, and Mark A. Griswold. "Feasibility of MR fingerprinting using a high-performance 0.55 T MRI system." Magnetic Resonance Imaging 81 (September 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 (November 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, no. 3 (June 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, and Jianhui Zhong. "Detection of Lesions in Mesial Temporal Lobe Epilepsy by Using MR Fingerprinting." Radiology 288, no. 3 (September 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, and Jianhui Zhong. "Fast 3D brain MR fingerprinting based on multi‐axis spiral projection trajectory." Magnetic Resonance in Medicine 82, no. 1 (March 18, 2019): 289–301. http://dx.doi.org/10.1002/mrm.27726.

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50

Nagtegaal, Martijn, Peter Koken, Thomas Amthor, and Mariya Doneva. "Fast multi‐component analysis using a joint sparsity constraint for MR fingerprinting." Magnetic Resonance in Medicine 83, no. 2 (August 16, 2019): 521–34. http://dx.doi.org/10.1002/mrm.27947.

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