Fast quantitative MRI
Research Works
Principal Investigator : 
Ludovic de Rochefort, PhD
Members
Arnaud le Troter, PhD 
Lucas Soustelle, PhD
Guillaume Duhamel, PhD 
Olivier Girard, PhD
Swetali Nimje, PhD student Alexandre Cabane (Engineer)
Alumni
Jérémy Beaumont (Postdoctoral fellow, 2021-2022)
Internships:
Mahya FARAJI ZAMHARIR (2022)
Jean-Eudes Ayilo (2022)
Florian SGARD (2021)
Mathieu VALLET (2020)
Research
However, clinical routine protocols are far from exploiting these promising new approaches, as it requires additional scan time. Patient motion is also a critical limitation of product and research sequences that always need innovative strategies to reduce its effects.The Fast Quantitative MRI topic targets on the following research objectives:
– Push forward Quantitative Susceptibility Mapping (QSM) for brain imaging, at both high (3T) and ultra-high (7T) clinical magnetic field in order to reach unpreceded in vivo brain iron and myelin characterization with QSM.
– Reduce motion artefacts and reach highly-accelerated scans for multi-parametric brain mapping using artificial intelligence approaches tailored to process MRI data.
– Exploit the richness of gradient-echo sequences and propose new concepts for the processing of MR signals to further enhance the characterization of brain tissue in vivo.
– Implement proof-of-concept and transfer these technologies to clinical and neuroscience research.
Collaborations
Alexandre Vignaud (Research Engineer, CEA)
Lisa Leroi (PhD student, CEA)
Thierry Artières (Ecole Centrale Marseille, LIS, AMU, CNRS)
Yi Wang (Cornell University)
Pascal Spincemaille (Cornell University)
Selected Publications
Brun, Gilles, Benoit Testud, Olivier M. Girard, Pierre Lehmann, Ludovic de Rochefort, Pierre Besson, Aurélien Massire, et al. “Automatic Segmentation of Deep Grey Nuclei Using a High-Resolution 7T Magnetic Resonance Imaging Atlas—Quantification of T1 Values in Healthy Volunteers.” European Journal of Neuroscience 55, no. 2 (2022): 438–60. https://doi.org/10.1111/ejn.15575.
A. Le Troter, A. Cabane, B. Testud, S. Grimaldi, M. Guye, J-P Ranjeva, L. de Rochefort. Parcellation of the Substantia Nigra from QSM using multi-modal 7T MRI Template, QMR workshop 2022, Lucca, Italy
https://qmrlucca.files.wordpress.com/2022/11/program_qmr_lucca_final_a5_rev.pdf
Beaumont, J., Thomas Troalen, S. Nimje, Stanislas Rapacchi, and L. de Rochefort. Energy dependent z-scores improve parallel imaging motion correction using trimmed autocalibrating k-space estimation (TAKE), p. 0353. London (UK), ISMRM 2022. https://hal.archives-ouvertes.fr/hal-03859860v1
S. Nimje, T. Artières and L. de Rochefort. SANGRIA: one-Shot leArNinG super-ResolutIon with Adversarial training for accelerated Magnetic Resonance Imaging, CAp conference 2022, https://caprfiap2022.sciencesconf.org/resource/page/id/30
S. Nimje, T. Artières and L. de Rochefort. Réseaux de neurones convolutifs complexes et scan-spécifiques pour accélérer l’IRM : Faisabilité pour l’Imagerie Pondérée T2, RITS 2022, https://rits2022.sciencesconf.org/data/pages/ProgrammeRITS2022_final_1.pdf
2021:
Troalen T, Troter AL, Confort-Gouny S, Vioux P, Costes C, Pini L, Ranjeva J-P, Guye M, de Rochefort L. Clinical Whole-Brain R2* and Quantitative Susceptibility Maps at 3T – Reproducibility and Parameter Optimization Towards Millimetric 5min Scan. ISMRM; 2021. p 1307. https://hal.archives-ouvertes.fr/hal-03434318v1
2020:
Leroi, Lisa, Vincent Gras, Nicolas Boulant, Mathilde Ripart, Emilie Poirion, Mathieu D. Santin, Romain Valabregue, et al. “Simultaneous Proton Density, T1 , T2 , and Flip-Angle Mapping of the Brain at 7 T Using Multiparametric 3D SSFP Imaging and Parallel-Transmission Universal Pulses.” Magnetic Resonance in Medicine 84, no. 6 (December 2020): 3286–99. https://doi.org/10.1002/mrm.28391.
de Rochefort L. Quantitative MRI: from MR-physics to tissue microstructure – Bringing quantitative magnetic susceptibility mapping into the clinic. ECR 2020 Highlight Weeks; 2020; Virtual. https://connect.myesr.org/course/quantitative-mri-from-mr-physics-to-tissue-microstructure/
2019:
Bandt, S. Kathleen, Ludovic de Rochefort, Weiwei Chen, Alexey V. Dimov, Pascal Spincemaille, Brian H. Kopell, Ajay Gupta, and Yi Wang. “Clinical Integration of Quantitative Susceptibility Mapping Magnetic Resonance Imaging into Neurosurgical Practice.” World Neurosurgery 122 (February 2019): e10–19. https://doi.org/10.1016/j.wneu.2018.08.213.
Spincemaille, Pascal, Zhe Liu, Shun Zhang, Ilhami Kovanlikaya, Matteo Ippoliti, Marcus Makowski, Richard Watts, et al. “Clinical Integration of Automated Processing for Brain Quantitative Susceptibility Mapping: Multi-Site Reproducibility and Single-Site Robustness.” Journal of Neuroimaging: Official Journal of the American Society of Neuroimaging 29, no. 6 (November 2019): 689–98. https://doi.org/10.1111/jon.12658.
Düzel, Emrah, Julio Acosta-Cabronero, David Berron, Geert Jan Biessels, Isabella Björkman-Burtscher, Michel Bottlaender, Richard Bowtell, et al. “European Ultrahigh-Field Imaging Network for Neurodegenerative Diseases (EUFIND).” Alzheimer’s & Dementia (Amsterdam, Netherlands) 11 (December 2019): 538–49. https://doi.org/10.1016/j.dadm.2019.04.010.
2018:
Bydder, Mark, Gavin Hamilton, Ludovic de Rochefort, Ajinkya Desai, Elhamy R. Heba, Rohit Loomba, Jeffrey B. Schwimmer, Nikolaus M. Szeverenyi, and Claude B. Sirlin. “Sources of Systematic Error in Proton Density Fat Fraction (PDFF) Quantification in the Liver Evaluated from Magnitude Images with Different Numbers of Echoes.” NMR in Biomedicine 31, no. 1 (January 2018). https://doi.org/10.1002/nbm.3843.
Leroi, Lisa, Arthur Coste, Ludovic de Rochefort, Mathieu D. Santin, Romain Valabregue, Franck Mauconduit, Eric Giacomini, et al. “Simultaneous Multi-Parametric Mapping of Total Sodium Concentration, T1, T2 and ADC at 7 T Using a Multi-Contrast Unbalanced SSFP.” Magnetic Resonance Imaging 53 (2018): 156–63. https://doi.org/10.1016/j.mri.2018.07.012.
2017:
Schweser, Ferdinand, Simon Daniel Robinson, Ludovic de Rochefort, Wei Li, and Kristian Bredies. “An Illustrated Comparison of Processing Methods for Phase MRI and QSM: Removal of Background Field Contributions from Sources Outside the Region of Interest.” NMR in Biomedicine 30, no. 4 (April 2017). https://doi.org/10.1002/nbm.3604.
Kee, Youngwook, Zhe Liu, Liangdong Zhou, Alexey Dimov, Junghun Cho, Ludovic de Rochefort, Jin Keun Seo, and Yi Wang. “Quantitative Susceptibility Mapping (QSM) Algorithms: Mathematical Rationale and Computational Implementations.” IEEE Transactions on Bio-Medical Engineering 64, no. 11 (November 2017): 2531–45. https://doi.org/10.1109/TBME.2017.2749298.
Kaaouana, Takoua, Anne Bertrand, Fatma Ouamer, Bruno Law-Ye, Nadya Pyatigorskaya, Ali Bouyahia, Nathalie Thiery, et al. “Improved Cerebral Microbleeds Detection Using Their Magnetic Signature on T2*-Phase-Contrast: A Comparison Study in a Clinical Setting.” NeuroImage. Clinical 15 (2017): 274–83. https://doi.org/10.1016/j.nicl.2016.08.005.
Associated Keywords
- Acceleration
- Artificial Intelligence
- Brain
- motion
- QSM
- Quantification
