Search for: All records

The development of effective and safe agricultural treatments requires sub-cellular insight of the biochemical effects of treatments in living tissue in real-time. Industry-standard mass spectroscopic imaging lacks real-timein vivocapability. As an alternative, multiphoton fluorescence lifetime imaging microscopy (MPM-FLIM) allows for 3D sub-cellular quantitative metabolic imaging but is often limited to low frame rates. To resolve relatively fast effects (e.g., photosynthesis inhibiting treatments), high-frame-rate MPM-FLIM is needed. In this paper, we demonstrate and evaluate a high-speed MPM-FLIM system, “Instant FLIM”, as a time-resolved 3D sub-cellular molecular imaging system in highly scattering, living plant tissues. We demonstrate simultaneous imaging of cellular autofluorescence and crystalline agrochemical crystals within plant tissues. We further quantitatively investigate the herbicidal effects of two classes of agricultural herbicide treatments, photosystem II inhibiting herbicide (Basagran) and auxin-based herbicide (Arylex), and successfully demonstrate the capability of the MPM-FLIM system to measure biological changes over a short time with enhanced imaging speed. Results indicate that high-frame-rate 3D MPM-FLIM achieves the required fluorescence lifetime resolution, temporal resolution, and spatial resolution to be a useful tool in basic plant cellular biology research and agricultural treatment development.

Fluorescence microscopy imaging speed is fundamentally limited by the measurement signal-to-noise ratio (SNR). To improve image SNR for a given image acquisition rate, computational denoising techniques can be used to suppress noise. However, common techniques to estimate a denoised image from a single frame either are computationally expensive or rely on simple noise statistical models. These models assume Poisson or Gaussian noise statistics, which are not appropriate for many fluorescence microscopy applications that contain quantum shot noise and electronic Johnson–Nyquist noise, therefore a mixture of Poisson and Gaussian noise. In this paper, we show convolutional neural networks (CNNs) trained on mixed Poisson and Gaussian noise images to overcome the limitations of existing image denoising methods. The trained CNN is presented as an open-source ImageJ plugin that performs real-time image denoising (within tens of milliseconds) with superior performance (SNR improvement) compared to conventional fluorescence microscopy denoising methods. The method is validated on external datasets with out-of-distribution noise, contrast, structure, and imaging modalities from the training data and consistently achieves high-performance ($><\#comment/>8\mathrm{d}\mathrm{B}$) denoising in less time than other fluorescence microscopy denoising methods.