Optical coherence tomography (OCT) can image deep inside scattering plant tissue with micrometer resolution. However, conventional OCT lacks specificity to distinguish plant tissue from pathogen tissue. Here we show how dynamic OCT (dOCT) creates functional contrast of Bremia, a downy mildew in lettuce, based on sub-resolution dynamic activity inside the tissue. We demonstrate its applicability for disease resistance quantification and longitudinal study of pathogen growth.@en

Microscopic imaging for studying plant-pathogen interactions is limited by its reliance on invasive histological techniques, like clearing and staining, or, for in vivo imaging, on complicated generation of transgenic pathogens. We present real-time 3D in vivo visualization of pathogen dynamics with label-free optical coherence tomography. Based on intrinsic signal fluctuations as tissue contrast we image filamentous pathogens and a nematode in vivo in 3D in plant tissue. We analyze 3D images of lettuce downy mildew infection (Bremia lactucae) to obtain hyphal volume and length in three different lettuce genotypes with different resistance levels showing the ability for precise (micro) phenotyping and quantification of the infection level. In addition, we demonstrate in vivo longitudinal imaging of the growth of individual pathogen (sub)structures with functional contrast on the pathogen micro-activity revealing pathogen vitality thereby opening a window on the underlying molecular processes.@en

Phase-preserving spectral estimation optical coherence tomography (SE-OCT) enables combining axial resolution improvement with computational depth of focus (DOF) extension. We combine SE-OCT with interferometric synthetic aperture microscopy (ISAM) to obtain a high 3D resolution over a large depth range with a narrow bandwidth visible light super-luminescent diode (SLD). SE-OCT gives a five times axial resolution improvement to 1.5 micrometer. The combination with ISAM gives a sub-micron lateral resolution over a 300 micrometer axial range, 12 times the conventional DOF. The results show that phase-preserving SE-OCT is sufficiently accurate for coherent post-processing, enabling the use of cost-effective SLDs in the visible light range for high spatial resolution OCT.

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Phase sensitive optical coherence tomography (OCT) is able to measure small axial motion at the level of ten nanometer. However, when interfaces are located close to each other, the phase of one interface may influence the phase of the other interface. This spectral leakage hampers the ability to see relative motion between structures within the sample, especially when the separation is below the axial resolution. Spectral estimation OCT (SE-OCT) based on IAA can not only improve the axial resolution beyond the conventional bandwidth limitation, but also reduce this spectral leakage. Here we show accurate reconstruction of the vibration of an interface at sub-resolution distance from a high-intensity interface with a different vibration frequency. Phase preserved IAA successfully reduces spectral leakage and outperforms conventional DFT-based reconstruction methods.

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Phase-preserving spectral estimation optical coherence tomography (SE-OCT) enables combining axial resolution improvement with computational depth of field (DOF) extension. We show that the combination of SE-OCT with interferometric synthetic aperture microscopy (ISAM) and computational adaptive optics (CAO) results in high 3D resolution over a large depth range for an OCT system with a narrow bandwidth visible light super-luminescent diode (SLD). SE-OCT results in up to five times axial resolution improvement from 8 µm to 1.5 µm. The combination with ISAM gives a sub-micron lateral resolution over a 400 µm axial range, which is at least 16 times the conventional depth of field. CAO can be successfully applied after SE and ISAM and removes residual aberrations, resulting in high quality images. The results show that phase-preserving SE-OCT is sufficiently accurate for coherent post-processing, enabling the use of cost-effective SLDs in the visible light range for high spatial resolution OCT.

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Plant pathogens cause yield losses in crops worldwide. Breeding for improved disease resistance and management by precision agriculture are two approaches to limit such yield losses. Both rely on detecting and quantifying signs and symptoms of plant disease. To achieve this, the field of plant phenotyping makes use of non-invasive sensor technology. Compared to invasive methods, this can offer improved throughput and allow for repeated measurements on living plants. Abiotic stress responses and yield components have been successfully measured with phenotyping technologies, whereas phenotyping methods for biotic stresses are less developed, despite the relevance of plant disease in crop production. The interactions between plants and pathogens can lead to a variety of signs (when the pathogen itself can be detected) and diverse symptoms (detectable responses of the plant). Here, we review the strengths and weaknesses of a broad range of sensor technologies that are being used for sensing of signs and symptoms on plant shoots, including monochrome, RGB, hyperspectral, fluorescence, chlorophyll fluorescence and thermal sensors, as well as Raman spectroscopy, X-ray computed tomography, and optical coherence tomography. We argue that choosing and combining appropriate sensors for each plant-pathosystem and measuring with sufficient spatial resolution can enable specific and accurate measurements of above-ground signs and symptoms of plant disease.

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Spectral estimation can improve axial resolution in optical coherence tomography over its traditional limit. Contrary to sparsity-based methods, the iterative adaptive approach combines resolution enhancement with good tissue contrast reconstruction.

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Spectral estimation can improve axial resolution for optical coherence tomography. Using a fast implementation of the non-parametric iterative adaptive approach, we significantly improve resolution and image quality in processing times below 2 s.

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Spectral estimation can improve axial resolution for optical coherence tomography. Using a fast implementation of the non-parametric iterative adaptive approach, we significantly improve resolution and image quality in processing times below 2 s.

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Spectral-estimation OCT (SE-OCT) is a computational method to enhance the axial resolution beyond the traditional bandwidth limit. However, it has not yet been used widely due to its high computational load, dependency on user-optimized parameters, and inaccuracy in intensity reconstruction. In this study, we implement SE-OCT using a fast implementation of the iterative adaptive approach (IAA). This non-parametric spectral estimation method is optimized for use on OCT data. Both in simulations and experiments we show an axial resolution improvement with a factor between 2 and 10 compared to standard discrete Fourier transform. Contrary to parametric methods, IAA gives consistent peak intensity and speckle statistics. Using a recursive and fast reconstruction scheme the computation time is brought to the sub-second level for a 2D scan. Our work shows that SE-OCT can be used for volumetric OCT imaging in a reasonable computation time, thus paving the way for wide-scale implementation of superresolution OCT.

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Optical coherence tomography (OCT) can be a valuable imaging tool for in vivo and label-free digital plant phenotyping. However, for imaging leaves, air-filled cavities limit the penetration depth and reduce the image quality. Moreover, up to now quantification of leaf morphology with OCT has been done in one-dimensional or two-dimensional images only, and has often been limited to relative measurements. In this paper, we demonstrate a significant increase in OCT imaging depth and image quality by infiltrating the leaf air spaces with water. In the obtained high-quality OCT images the top and bottom surface of the leaf are digitally segmented. Moreover, high-quality en face images of the leaf are obtained from numerically flattened leaves. Segmentation in three-dimensional OCT images is used to quantify the spatially resolved leaf thickness. Based on a segmented leaf image, the refractive index of an infiltrated leaf is measured to be 1.345 ± 0.004, deviating only 1.2% from that of pure water. Using the refractive index and a correction for refraction effects at the air-leaf interface, we quantitatively mapped the leaf thickness. The results show that OCT is an efficient and promising technique for quantitative phenotyping on leaf and tissue level.

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An ultrasound scan generates a huge amount of data. To form an image this data has to be transferred to the imaging system. This is an issue for applications where the data transfer capacity is limited such as hand-held systems, wireless probes and miniaturized array probes. Two-stage beamforming methods can be used to significantly reduce the data transfer requirements. In the first stage, which is applied in-probe, the amount of data is reduced from channel to scanline data. In the imaging system the data is then beamformed to obtain images that are synthetically focused over the entire image. Currently two approaches exist for the second stage. The first approach is a time-of-flight approach called synthetic aperture sequential beamforming (SASB) that has been developed for both linear and phased arrays. SASB does however introduce artefacts in the image that can be reduced by tapering the first stage scan lines at the cost of lateral resolution. The second approach is based on the wave equation, but a computationally efficient method for phased arrays that is producing sector scan data is lacking. Here we propose an algorithm that uses the fast Hankel transform to obtain a fast algorithm. The imaging performance of this method is evaluated with simulations and experiments. Compared with PSASB, which is an adaption of SASB for phased arrays, our method requires a similar amount of operations to construct the entire image and there is no trade-off between resolution and artefacts. These results show the advantage of using the wave equation instead of a time-of-flight approach.

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The huge amount of data that needs to be transferred between probe and imaging system becomes a major issue when the data transfer capacity is limited, e.g. in handheld systems, wireless probes and miniaturized probes. The amount of data can be significantly reduced by using two-stage beamforming. The first stage consists of a fixed focus algorithm that compresses channel data to scanline data. This can be done by integrated electronics in the handle. In the second stage the scanline data is further beamformed in the imaging system to obtain images that are synthetically focused at all depths. Here we present a wave equation two-stage beamforming method for phased array imaging that is computationally efficient and outperforms PSASB, a time-of-flight alternative, in terms of lateral resolution and contrast-to-noise ratio.@en