Presentation of DELTA project
Ultrafast 3D ultrasound (US) imaging is being developed in research laboratories. However, its clinical application is hindered by insufficient image quality. This project addresses this limitation by proposing various signal and image processing methods for functional 3D US imaging applied to the cardiac muscle.
During ischemia, the tissue structure undergoes various changes, such as oedema and reperfusion, and patient management remains under study. Thus, obtaining a local tissue marker related to the orientation of the cardiac tissue is crucial for improving patient management and assessing treatment success. High-quality real-time 3D imaging is necessary to allow practitioners to visualize the heart and perform local measurements.
Deep learning algorithms will generate a high-resolution 3D volume from a limited number of low-resolution volumes. This learning process will involve synthetic and experimental acquisitions. Once localized, the anisotropy of the tissue will be measured using the coherence of ultrasound signals. This approach consists of calculating the 3D spatial covariance matrix. Estimating this matrix with limited sample supports compared to the probe elements’ dimension requires specific signal processing techniques to help compute it efficiently, understand it smartly and propose a dedicated estimator of the local anisotropy. Furthermore, the spatial structure of the coherence matrix, derived from the covariance matrix, will be explored. Integrating the evaluation of this coherence matrix as a parameter of a matrix-variable distribution will be studied to adapt to biological signals and ensure stable anisotropy estimation.
The project will also address out-of-plane estimation of anisotropy and the extension of the field of view, which is essential for future clinical applications. Currently, measurement is restricted to planes parallel to the probe, limiting its use. In cardiac imaging, a wider field of view is necessary, and the validity of anisotropy measurement with this type of acquisition needs verification. These methodologies will be validated using different in vitro models and an animal ischemia model before evaluation on human subjects. The proposed imaging will be compared with diffusion magnetic resonance imaging, a state-of-the-art technique for estimating cardiac muscle anisotropy but unsuitable for routine clinical use.
In conclusion, this project aims to validate the entire acquisition and post-processing chain to develop a new marker of cardiac muscle anisotropy in ultrasound imaging. This marker will be compared with reference imaging techniques to assess its potential for future human clinical applications.
During ischemia, the tissue structure undergoes various changes, such as oedema and reperfusion, and patient management remains under study. Thus, obtaining a local tissue marker related to the orientation of the cardiac tissue is crucial for improving patient management and assessing treatment success. High-quality real-time 3D imaging is necessary to allow practitioners to visualize the heart and perform local measurements.
Deep learning algorithms will generate a high-resolution 3D volume from a limited number of low-resolution volumes. This learning process will involve synthetic and experimental acquisitions. Once localized, the anisotropy of the tissue will be measured using the coherence of ultrasound signals. This approach consists of calculating the 3D spatial covariance matrix. Estimating this matrix with limited sample supports compared to the probe elements’ dimension requires specific signal processing techniques to help compute it efficiently, understand it smartly and propose a dedicated estimator of the local anisotropy. Furthermore, the spatial structure of the coherence matrix, derived from the covariance matrix, will be explored. Integrating the evaluation of this coherence matrix as a parameter of a matrix-variable distribution will be studied to adapt to biological signals and ensure stable anisotropy estimation.
The project will also address out-of-plane estimation of anisotropy and the extension of the field of view, which is essential for future clinical applications. Currently, measurement is restricted to planes parallel to the probe, limiting its use. In cardiac imaging, a wider field of view is necessary, and the validity of anisotropy measurement with this type of acquisition needs verification. These methodologies will be validated using different in vitro models and an animal ischemia model before evaluation on human subjects. The proposed imaging will be compared with diffusion magnetic resonance imaging, a state-of-the-art technique for estimating cardiac muscle anisotropy but unsuitable for routine clinical use.
In conclusion, this project aims to validate the entire acquisition and post-processing chain to develop a new marker of cardiac muscle anisotropy in ultrasound imaging. This marker will be compared with reference imaging techniques to assess its potential for future human clinical applications.
Presentation of the consortium
ULTIM (Creatis)
The Ultrasound Imaging team of Creatis develops signal and image processing methods for anatomical ultrasound imaging. It will bring expertise in beamforming, 3D ultrasound imaging, deep learning and ultrasound anisotropy imaging. It is noted that Lyon is one of the unique places in the world where a 1024-channel system is available. Moreover, two other solutions exist in the team to access 3D ultrasound imaging: (i) two multiplexed probes are on the PILoT platform and allow real-time 3D imaging, (ii) a fully integrated 3D system is under development (ANR LabCom IMAGE4US) that would be finalised and available for the project. The latter is the targeted one for the DELTA project.
Members involved: François Varray (Univ. Lyon 1), Adrian Basarab (Univ. Lyon 1), Pierre-Yves Courand (PUPH). A PhD student will be recruited for the project (36 months).
Members involved: François Varray (Univ. Lyon 1), Adrian Basarab (Univ. Lyon 1), Pierre-Yves Courand (PUPH). A PhD student will be recruited for the project (36 months).
L2S
The Modelling and Estimation team aims to address massive data analysis problems based on mathematical tools such as multivariate statistics, robust statistics, and tensor analysis. The proposed methods and algorithms rely on multivariate statistics, numerical optimisation, random matrix theory, sparse representation, and Bayesian inference. The group is also interested in statistical approaches for the optimal design of numerical experiments. The application fields include health, energy production, remote sensing, finance and statistical physics.
Members involved: Florent Bouchard (CR CNRS), Frédéric Pascal (Professor at CentraleSupélec). A post-doctoral researcher will be recruited for the project (18 months).
Members involved: Florent Bouchard (CR CNRS), Frédéric Pascal (Professor at CentraleSupélec). A post-doctoral researcher will be recruited for the project (18 months).
MAGICS (Creatis)
The Magics team has well-known expertise in both experimental and clinical interventional applications (RHU MARVELOUS, MARVEL, etc.) with a research focus on the development of MRI methods using multimodal and parametric approaches for translational research with a major interest to cardiology, acute and chronic MI, heart failure. The multidisciplinary team will include both physicists and physicians. The implied researchers have a strong experience dedicated to improving the understanding of the pathophysiology of reperfusion injury following global or focal ischemia, and, in addition, the habilitated for experimental studies on animal.
Members involved: M. Viallon (Dr in Nuclear and fundamental physics), P. Croisille (PUPH), L. Petrusca (Post-Doc). A post-doctoral researcher will be recruited for the project (18 months).
Members involved: M. Viallon (Dr in Nuclear and fundamental physics), P. Croisille (PUPH), L. Petrusca (Post-Doc). A post-doctoral researcher will be recruited for the project (18 months).