- Thème : Imagerie des tissus biologiques

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Accueil > La Recherche > Axes & Equipes > Bio Nano Imagerie > Equipe : Spectroscopie et Imagerie RMN > Thème : Imagerie des tissus biologiques

Thème : Imagerie des tissus biologiques

Présentation

The group nanoNMRI is involved in Nuclear Magnetic Resonance Spectroscopy and Imaging for the study of living systems. NMR and MRI are heavily used in biochemistry, agronomy, biology and diagnosis in medicine. They are based on the detection of radio-frequencies (RF) originating from polarized nuclear spins. Our project concerns a new strategy to detect these RF in the near field of the object of interest and to evaluate this new approach to the study of living systems. In the light of these potential developments, one expect unprecedented spatial, time and spectral resolutions leading to accurate understanding of physical, chemical and biological interplays existing in an ensemble or individual living cells. Our approach is based on the scaling down of the RF antenna, in a first step to micrometer with the use of a micro cantilever and in a second step by taking advantage of the exceptional mechanical and electronic properties of one carbon nanotube (NT) fixed at the end of a microcoaxial tip. Hyperlocalyzed NMR spectroscopy will be possible from nuclear spins located around the tiny antenna. With the help of an accurate control of the positioning of the RF probe, MRI will be performed to sub-micronic scales by collecting spectral information like line shifts, line intensities, and relaxations in correlation with the displacements. The detection of hyper localized NMR signals from living systems will be of great interest to better understand the biological, physical and chemical mechanisms and thus open new routes for diagnosis and therapeutic treatments. In the present project, the development of a micro/nano NMR probe will improve the sensitivity and the time-space resolution of the measured parameters in order to study the localization, the occurrence and/or disappearance of biological markers involved in physiological or pathophysiological phenomena.

Collaborations : E. Nativel (IES, Montpellier), P. Stein (Odense), A. Bertin and M. Génard (INRA Avignon), M. Gaviria and A. Privat (Neureva company, Montpellier), R. Schimpf (RS2 company, Strasbourg)

Fundings : CNRS PIR “nanoNMRI” project (2008-2010), Siemens CIFRE (2007-2010), ARPE Languedoc-Roussillon (2008-2010), RTRA virtual fruit Agropolis (2009-2011), BQR UM2 (2009)

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Dernières publications

Micro‐CT and high‐field MRI for studying very early post‐mortem human fetal anatomy at 8 weeks of gestation

Auteur(s): Lamouroux Audrey, Cardoso Maïda, Bottero Célia, Gallo Mathieu, Duraes Martha, Salerno Jennifer, Bertrand Martin, Rigau Valérie, Fuchs Florent, Mousty Eve, Genevieve David, Subsol Gérard, Goze-Bac C., Captier Guillaume

(Article) Publié: Prenatal Diagnosis, vol. 44 p.3-14 (2024)

End-to-end multimodal 3D imaging and machine learning workflow for non-destructive phenotyping of grapevine trunk internal structure

Auteur(s): Fernandez Romain, Le Cunff Loïc, Mérigeaud Samuel, Verdeil Jean‐Luc, Perry Julie, Larignon Philippe, Spilmont Anne-Sophie, Chatelet Philippe, Cardoso Maïda, Goze-Bac C., Moisy Cédric

(Article) Publié: Scientific Reports, vol. 14 p.5033 (2024)

Ref HAL: hal-04501077_v1
PMID 38424155
DOI: 10.1038/s41598-024-55186-3
Exporter : BibTex | endNote
Résumé:

Quantifying healthy and degraded inner tissues in plants is of great interest in agronomy, for example, to assess plant health and quality and monitor physiological traits or diseases. However, detecting functional and degraded plant tissues in-vivo without harming the plant is extremely challenging. New solutions are needed in ligneous and perennial species, for which the sustainability of plantations is crucial. To tackle this challenge, we developed a novel approach based on multimodal 3D imaging and artificial intelligence-based image processing that allowed a non-destructive diagnosis of inner tissues in living plants. The method was successfully applied to the grapevine ( Vitis vinifera L.). Vineyard’s sustainability is threatened by trunk diseases, while the sanitary status of vines cannot be ascertained without injuring the plants. By combining MRI and X-ray CT 3D imaging with an automatic voxel classification, we could discriminate intact, degraded, and white rot tissues with a mean global accuracy of over 91%. Each imaging modality contribution to tissue detection was evaluated, and we identified quantitative structural and physiological markers characterizing wood degradation steps. The combined study of inner tissue distribution versus external foliar symptom history demonstrated that white rot and intact tissue contents are key measurements in evaluating vines’ sanitary status. We finally proposed a model for an accurate trunk disease diagnosis in grapevine. This work opens new routes for precision agriculture and in-situ monitoring of tissue quality and plant health across plant species.

Commentaires: The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. The extension of the Trainable Segmentation plugin is open-source, and available as a fork of Trainable Segmentation on GitHub https://github.com/Rocsg/Trainable_Segmentation/tree/Hyperweka.

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