ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026
Image Fusion for Super-Resolution Mass Spectrometry Imaging of Plant Tissue.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Multimodal FRACTAL-MSI and Image Fusion Improves Detection and Resolution of Biomolecule Imaging.ACS central science · 2026Article
- Image Fusion for Super-Resolution Mass Spectrometry Imaging of Plant Tissue.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
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4 authors.
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Abstract
Mass spectrometry imaging (MSI) is a vital tool in botanical research. Image fusion is introduced for resolution enhancement of MSI data from animal samples, but its application to plant MSI data resulted in unsatisfactory visualizations due to the distinct morphological characteristics of plant tissues. Herein, this study presents loss controlled residual network (LCRN), a workflow dedicated to the super-resolution fusion of plant MSI data. The pipeline used a residual connection-based neural network implemented with a novel loss metric called edge perceptual loss. Edge perceptual loss is developed for evaluating complex morphological information that can not be properly reflected by common image metrics, and its implementation in loss propagation is vital to the quality of the fusion result. Compared to existing deep learning-based methods, LCRN is able to generate a high-quality super-resolution fusion image of extra high magnification (up to 20-fold) that combined chemical and morphological information obtained from MSI and microscopy, respectively.
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Registered trials
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