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DRIM-IT
FunderFrench National Research Agency (ANR)
Funder Contribution €241,581.00
DurationFrom 01 Oct 2025
Project codeANR-25-CE19-5366
Open Access mandatePublications: NoResearch data: No
Identifiers
Description
The gold standard method for diagnosing and staging liver fibrosis is liver biopsy but it is invasive and induces sampling errors due to the small sample size. The need for non-invasive methods for the detection and follow-up of this liver disease is crucial since it has a high and increasing prevalence. A new method based on diffusion-weighted MR imaging, called virtual MR Elastography (vMRE), has recently been demonstrated to estimate the biomechanical properties of liver, which are correlated to the level of liver fibrosis, from the measurement of water diffusivity. This technique presents a great potential in staging liver fibrosis but so far it has only been explored in a few studies on clinical data. vMRE uses magnetic field gradients added between the excitation and the signal acquisition to encode the diffusion information in the MRI signal. This lengthens the echo time leading to the reduction of the signal-to-noise ratio (SNR), especially for high b-values. In this context, DRIM-IT proposes first to investigate the potential of vMRE on preclinical in vivo mouse liver since the powerful gradients system will allow to explore the limits of vMRE. Longitudinal studies will be performed to validate its diagnostic value. Secondly, DRIM-IT proposes a novel method for diffusion encoding that consists in applying a gradient simultaneously with a radiofrequency pulse optimized via an Optimal Control framework. This will enable the measurement of a wider spectrum of b-values while preserving SNR, it is thus expected to be a valuable tool for improving the estimation of biomechanical properties and thus the staging of liver fibrosis. The proposed method will be compared with the classical MRI encoding techniques on a preclinical MRI scanner and with histological analysis. Finally, it will also be tested on a clinical MRI scanner to demonstrate proof of concept in healthy subjects.This project concerns a public health problem, since it will potentially help an early detection of liver fibrosis, which is not possible to date.
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The gold standard method for diagnosing and staging liver fibrosis is liver biopsy but it is invasive and induces sampling errors due to the small sample size. The need for non-invasive methods for the detection and follow-up of this liver disease is crucial since it has a high and increasing prevalence. A new method based on diffusion-weighted MR imaging, called virtual MR Elastography (vMRE), has recently been demonstrated to estimate the biomechanical properties of liver, which are correlated to the level of liver fibrosis, from the measurement of water diffusivity. This technique presents a great potential in staging liver fibrosis but so far it has only been explored in a few studies on clinical data. vMRE uses magnetic field gradients added between the excitation and the signal acquisition to encode the diffusion information in the MRI signal. This lengthens the echo time leading to the reduction of the signal-to-noise ratio (SNR), especially for high b-values. In this context, DRIM-IT proposes first to investigate the potential of vMRE on preclinical in vivo mouse liver since the powerful gradients system will allow to explore the limits of vMRE. Longitudinal studies will be performed to validate its diagnostic value. Secondly, DRIM-IT proposes a novel method for diffusion encoding that consists in applying a gradient simultaneously with a radiofrequency pulse optimized via an Optimal Control framework. This will enable the measurement of a wider spectrum of b-values while preserving SNR, it is thus expected to be a valuable tool for improving the estimation of biomechanical properties and thus the staging of liver fibrosis. The proposed method will be compared with the classical MRI encoding techniques on a preclinical MRI scanner and with histological analysis. Finally, it will also be tested on a clinical MRI scanner to demonstrate proof of concept in healthy subjects.This project concerns a public health problem, since it will potentially help an early detection of liver fibrosis, which is not possible to date.
