Plant PhenomicsBioinformaticsPeer-reviewed journal article
Atlas-Based Spatio-temporal MRI Phenotyping of 3D Fungal Spread in Grapevine Wood
Gargee PhukoniD, Maïda CardosoiD, Christophe Goze‐BaciD, Loïc Le CunffiD, Jean‐Luc VerdeiliD, Cédric MoisyiD, Romain FernandeziD
Abstract
In perennial crops, inner wood degradation by pathogens often escapes detection until irreversible damage has occurred. Grapevine trunk disease (GTD) is a well-known example in viticulture that alters plants from within, years before foliar symptoms arise, making early assessment difficult. To overcome this limitation, we present a novel non-destructive 3D + t pipeline for Magnetic Resonance Imaging (MRI) spatial quantification and monitoring of early internal tissue degradation resulting from fungal colonization. This pipeline integrates (i) anatomical alignment and rigid time-series registration of volumetric MRI scans, (ii) a generalized cylindrical coordinate transformation for cross-sectional trunk anatomy normalization, (iii) supervised classification to segment water-depleted (diseased/non-functional) regions, and (iv) population-level statistical analyses including construction of population mean images, probabilistic atlases of lesions, and 3D lesion descriptors. Applied to multiple Vitis vinifera cultivars inoculated with a fungal trunk pathogen, our approach enables time-lapse comparisons between cultivar and treatment in vivo. The results reveal consistent early degradation signals across individuals and cultivar-dependent lesion differences. By combining high-resolution MRI with advanced image processing and statistical atlas tools, this method provides a new paradigm for 3D plant phenotyping of internal disease progression. This methodological innovation allows non-invasive quantification of disease development and comparative assessment of host responses in woody plants, demonstrating its potential to advance understanding and management of GTDs.
- organism: plant (other)
- assay: imaging
- research focus: spatial domain detection
- approach: statistical model
- Citations
- 0
- FWCI
- 0.00
- Field percentile
- 22th percentile
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