Description

The PhD project aims to develop new methods in magnetic resonance imaging (MRI) for the acquisition, reconstruction, and analysis of images to map the electrical properties of tissues in the human body. Electrical properties (conductivity and permittivity) characterize the behavior of a medium in response to electromagnetic stimulation and are influenced by tissue composition, structure, water content, ion concentration, etc. Recent 3D MRI mapping techniques offer new perspectives for many diagnostic biomedical applications, such as the characterization of pathological/tumoral lesions, or for interventional applications, such as calculating the electromagnetic dose delivered to tissues (specific absorption rate, SAR) during a microwave thermo-ablation procedure. However, these techniques have certain limitations, which the thesis work will address: 1) the effective spatial resolution of conductivity maps remains modest (5 to 10 mm) compared to the potential of MRI (< 1 mm); 2) in the organs of the thorax and abdomen, respiratory and cardiac motions impose additional constraints on spatial resolution. To overcome these challenges, new numerical computation techniques will be investigated for the image reconstruction step, and new programming strategies for acquisition sequences will be implemented. Finally, new electromagnetic models will be developed for diagnostic applications and for SAR estimation.

Skills required

- Education : M.Sc. or equivalent in one of the following fields: engineering, biomedical engineering, physics, mathematics, data science. Experience (M.Sc. internship) in MRI or medical imaging would be an advantage. - Curious, ability to work independently, motivated, high interest in research and medical imagiing - Good written and spoken English skills and good scientific programming skills (e.g. Matlab) - Good communication skills

Bibliography

1. Zhang X, Liu J, He B. Magnetic-resonance-based electrical properties tomography: a review. IEEE Rev Biomed Eng. 2014;7:87–96. doi:10.1109/RBME.2013.2297206 PubMed PMID: 24803104; PubMed Central PMCID: PMC4113345.
2. Katscher U, Voigt T, Findeklee C, Vernickel P, Nehrke K, DÖssel O. Determination of Electric Conductivity and Local SAR Via B1 Mapping. IEEE Trans Med Imaging. 2009 Sep;28(9):1365–74. doi:10.1109/TMI.2009.2015757
3. Haacke EM, Petropoulos LS, Nilges EW, Wu DH. Extraction of conductivity and permittivity using magnetic resonance imaging. Phys Med Biol. 1991 Jun;36(6):723–34. doi:10.1088/0031-9155/36/6/002
4. He Z, Lefebvre PM, Soullié P, Doguet M, Ambarki K, Chen B, et al. Phantom evaluation of electrical conductivity mapping by MRI: Comparison to vector network analyzer measurements and spatial resolution assessment. Magn Reson Med. 2024 Jun;91(6):2374–90. doi:10.1002/mrm.30009 PubMed PMID: 38225861.
5. He Z, Soullié P, Lefebvre P, Ambarki K, Felblinger J, Odille F. Changes of in vivo electrical conductivity in the brain and torso related to age, fat fraction and sex using MRI. Sci Rep. 2024 Jul 12;14(1):16109. doi:10.1038/s41598-024-67014-9
6. He Z, Chen B, Lefebvre PM, Odille F. An Adaptative Savitzky-Golay Kernel for Laplacian Estimation in Magnetic Resonance Electrical Property Tomography*. In: 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) [Internet]. 2023 [cited 2026 Mar 31]. p. 1–4. Available from: https://ieeexplore.ieee.org/document/10341200 doi:10.1109/EMBC40787.2023.10341200
7. Soullié P, Missoffe A, Ambarki K, Felblinger J, Odille F. MR electrical properties imaging using a generalized image-based method. Magn Reson Med. 2021;85(2):762–76. doi:https://doi.org/10.1002/mrm.28458
8. Lohrengel S, Milano C, Salmon S. Electrical properties reconstruction from MRI data: theoretical and numerical aspects. Inverse Probl Imaging. 2025 Nov 12;23(0):123–51. doi:10.3934/ipi.2025057

Keywords

Magnetic Resonance Imaging, Signal and image processing