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In this work, we propose a model-based deep learning reconstruction algorithm for optical projection tomography (ToMoDL), to greatly reduce acquisition and reconstruction times. The proposed method iterates over a data consistency step and an image domain artefact removal step achieved by a convolutional neural network. A preprocessing stage is also included to avoid potential misalignments between the sample center of rotation and the detector. The algorithm is trained using a database of wild-type zebrafish (Danio rerio) at different stages of development to minimise the mean square error for a fixed number of iterations. Using a cross-validation scheme, we compare the results to other reconstruction methods, such as filtered backprojection, compressed sensing and a direct deep learning method where the pseudo-inverse solution is corrected by a U-Net. The proposed method performs equally well or better than the alternatives. For a highly reduced number of projections, only the U-Net method provides images comparable to those obtained with ToMoDL. However, ToMoDL has a much better performance if the amount of data available for training is limited, given that the number of network trainable parameters is smaller.

Farmlands are currently among the dominant uses of the land. When managed under low-input farming systems, farmlands are associated with diverse cultural and natural heritages around the world. Known in Europe as high nature value (HNV) farmlands, these agricultural landscapes and their associated farming systems evolved as tightly coupled socioecological systems, and are essential to biodiversity conservation and the delivery of ecosystem services to society. However, HNV farmlands are vulnerable to socioeconomic changes that lead to either agricultural intensification or land abandonment. We present a range of plausible future scenarios for HNV farmlands, and discuss the related management options and expected socioecological outcomes for each scenario. We then provide recommendations for policy, practice, and research on how to best ensure the socioecological viability of HNV farming systems in the future.

Light Sheet Fluorescence Microscopy (LSFM) enables rapid and gentle 3D fluorescence imaging of dynamic processes over extended periods in translucent samples at the mesoscopic scale. However, its temporal resolution is constrained by the sequential acquisition of individual two-dimensional planes at varying depths, making it challenging to capture rapid dynamics such as the beating of a zebrafish heart. To address this limitation, we recently developed spatially modulated Selective Volume Illumination Microscopy, which utilizes a compressed sensing approach to reconstruct the entire imaging volume from measurements where multiple planes are illuminated simultaneously using spatially modulated light. Building on this advancement, we now introduce a novel spatio-temporal patterned illumination strategy and volume reconstruction method that incorporates low-rank and sparsity constraints, effectively leveraging the temporal and spatial redundancy present in sequential volumetric acquisitions. This method was applied to the volumetric imaging of embryonic zebrafish hearts, achieving an improvement in imaging speed of 4-fold compared to standard LSFM and a 2-fold improvement compared to traditional compressed sensing approaches, while preserving reconstruction accuracy and enabling the visualization of fast dynamic events with a resolution of a few tens of milliseconds. Our approach represents a step forward in enhancing the temporal resolution of LSFM for studying fast biological dynamics.

The existence of a “young farmer problem” in Europe has been recognized by scientists and policy-makers and is based on the widespread acknowledgement of the poor generational renewal rates in the farming sector and in particular in farmland management across the European Union. Despite existing support policy measures, young farmers (YF) face barriers which hamper the establishment and consolidation of their farming enterprises. Focusing on Alentejo (NUTS II), in Portugal, this paper identifies the difficulties YF face to accessing land, the high investment costs required to set up a farming unit, and the insufficient access to credit as the main reasons why young people are prevented from setting up their farming enterprises. Existing policy support measures targeting YF are widely perceived as inefficient with regard to triggering generational renewal. Hence, our findings suggest that not only is it necessary to pay greater attention to the complex question of land tenure, but that also the impact of policies implemented in the past should be examined in detail in order to develop and implement more effective measures that are sensitive to the different national and regional contexts.

The general concept of sustainable development has been specified in terms of goals, targets, and indicators by the UN Sustainable Development Goals, adopted in 2015, followed by the Green Deal of the European Union in 2019. The focus on targets and indicators does, however, not address the issue as to how these goals can be achieved for land-related SDGs in the real world, and attention in this paper is therefore focused on how land management can contribute to providing ecosystem services in line with the aims of the SDGs and the Green Deal. Agricultural production systems should at least produce healthy food (SDG2 and 3), protect ground- and surface water quality (SDG6), mitigate climate change (SDG13), avoid soil degradation, and support biodiversity (SDG15). The corresponding ecosystem services are discussed with particular emphasis on the role of soils, which are characterized in terms of soil health, defined as: contributing to ecosystem services in line with the SDGs and the Green Deal. Appropriate management, as developed and proposed by researchers working jointly with farmers in living labs, can only be realized when it is part of sound long-term business plans, supported by independent advice that is focused on farmers’ concerns based on the requirements for adaptive management. The research effort in living labs, addressing “wicked” problems, needs to be judged differently from classical linear research. As the development of successful ecosystem services requires an interdisciplinary research effort based on a systems analysis, SDG-oriented soil research in the future should be focused on: (i) presenting suitable data to the interdisciplinary effort beyond standard data to be found in existing databases; (ii) using soil types as “carriers of information” to allow extrapolation of results; (iii) providing data with a comparable degree of detail when analyzing the various ecosystem services, and (iv) revisit past experiences in soil survey and soil fertility research when contact with farmers was intense, as is again needed in future to realize ecosystem services in line with the SDGs and the Green Deal.

Background To enable free-breathing whole-heart sub-millimeter resolution coronary magnetic resonance angiography (CMRA) in a clinically feasible scan time by combining low-rank patch-based undersampled reconstruction (3D-PROST) with a highly accelerated non-rigid motion correction framework. Methods Non-rigid motion corrected CMRA combined with 2D image-based navigators has been previously proposed to enable 100% respiratory scan efficiency in modestly undersampled acquisitions. Achieving sub-millimeter isotropic resolution with such techniques still requires prohibitively long acquisition times. We propose to combine 3D-PROST reconstruction with a highly accelerated non-rigid motion correction framework to achieve sub-millimeter resolution CMRA in less than 10 min. Ten healthy subjects and eight patients with suspected coronary artery disease underwent 4–5-fold accelerated free-breathing whole-heart CMRA with 0.9 mm 3 isotropic resolution. Vessel sharpness, vessel length and image quality obtained with the proposed non-rigid (NR) PROST approach were compared against translational correction only (TC-PROST) and a previously proposed NR motion-compensated technique (non-rigid SENSE) in healthy subjects. For the patient study, image quality scoring and visual comparison with coronary computed tomography angiography (CCTA) were performed. Results Average scan times [min:s] were 6:01 ± 0:59 (healthy subjects) and 8:29 ± 1:41 (patients). In healthy subjects, vessel sharpness of the left anterior descending (LAD) and right (RCA) coronary arteries were improved with the proposed non-rigid PROST (LAD: 51.2 ± 8.8%, RCA: 61.2 ± 9.1%) in comparison to TC-PROST (LAD: 43.8 ± 5.1%, P  = 0.051, RCA: 54.3 ± 8.3%, P  = 0.218) and non-rigid SENSE (LAD: 46.1 ± 5.8%, P  = 0.223, RCA: 56.7 ± 9.6%, P  = 0.50), although differences were not statistically significant. The average visual image quality score was significantly higher for NR-PROST (LAD: 3.2 ± 0.6, RCA: 3.3 ± 0.7) compared with TC-PROST (LAD: 2.1 ± 0.6, P  = 0.018, RCA: 2.0 ± 0.7, P  = 0.014) and non-rigid SENSE (LAD: 2.3 ± 0.5, P  = 0.008, RCA: 2.5 ± 0.7, P  = 0.016). In patients, the proposed approach showed good delineation of the coronaries, in agreement with CCTA, with image quality scores and vessel sharpness similar to that of healthy subjects. Conclusions We demonstrate the feasibility of combining high undersampling factors with non-rigid motion-compensated reconstruction to obtain high-quality sub-millimeter isotropic CMRA images in ~ 8 min. Validation in a larger cohort of patients with coronary artery disease is now warranted.

Cardiovascular disease (CVD) is the leading single cause of morbidity and mortality, causing over 17. 9 million deaths worldwide per year with associated costs of over $800 billion. Improving prevention, diagnosis, and treatment of CVD is therefore a global priority. Cardiovascular magnetic resonance (CMR) has emerged as a clinically important technique for the assessment of cardiovascular anatomy, function, perfusion, and viability. However, diversity and complexity of imaging, reconstruction and analysis methods pose some limitations to the widespread use of CMR. Especially in view of recent developments in the field of machine learning that provide novel solutions to address existing problems, it is necessary to bridge the gap between the clinical and scientific communities. This review covers five essential aspects of CMR to provide a comprehensive overview ranging from CVDs to CMR pulse sequence design, acquisition protocols, motion handling, image reconstruction and quantitative analysis of the obtained data. (1) The basic MR physics of CMR is introduced. Basic pulse sequence building blocks that are commonly used in CMR imaging are presented. Sequences containing these building blocks are formed for parametric mapping and functional imaging techniques. Commonly perceived artifacts and potential countermeasures are discussed for these methods. (2) CMR methods for identifying CVDs are illustrated. Basic anatomy and functional processes are described to understand the cardiac pathologies and how they can be captured by CMR imaging. (3) The planning and conduct of a complete CMR exam which is targeted for the respective pathology is shown. Building blocks are illustrated to create an efficient and patient-centered workflow. Further strategies to cope with challenging patients are discussed. (4) Imaging acceleration and reconstruction techniques are presented that enable acquisition of spatial, temporal, and parametric dynamics of the cardiac cycle. The handling of respiratory and cardiac motion strategies as well as their integration into the reconstruction processes is showcased. (5) Recent advances on deep learning-based reconstructions for this purpose are summarized. Furthermore, an overview of novel deep learning image segmentation and analysis methods is provided with a focus on automatic, fast and reliable extraction of biomarkers and parameters of clinical relevance.

Optical projection tomography (OPT) provides a non-invasive 3-D imaging modality that can be applied to longitudinal studies of live disease models, including in zebrafish. Current limitations include the requirement of a minimum number of angular projections for reconstruction of reasonable OPT images using filtered back projection (FBP), which is typically several hundred, leading to acquisition times of several minutes. It is highly desirable to decrease the number of required angular projections to decrease both the total acquisition time and the light dose to the sample. This is particularly important to enable longitudinal studies, which involve measurements of the same fish at different time points. In this work, we demonstrate that the use of an iterative algorithm to reconstruct sparsely sampled OPT data sets can provide useful 3-D images with 50 or fewer projections, thereby significantly decreasing the minimum acquisition time and light dose while maintaining image quality. A transgenic zebrafish embryo with fluorescent labelling of the vasculature was imaged to acquire densely sampled (800 projections) and under-sampled data sets of transmitted and fluorescence projection images. The under-sampled OPT data sets were reconstructed using an iterative total variation-based image reconstruction algorithm and compared against FBP reconstructions of the densely sampled data sets. To illustrate the potential for quantitative analysis following rapid OPT data acquisition, a Hessian-based method was applied to automatically segment the reconstructed images to select the vasculature network. Results showed that 3-D images of the zebrafish embryo and its vasculature of sufficient visual quality for quantitative analysis can be reconstructed using the iterative algorithm from only 32 projections-achieving up to 28 times improvement in imaging speed and leading to total acquisition times of a few seconds.

Dynamic contrast-enhanced quantitative first-pass perfusion using magnetic resonance imaging enables non-invasive objective assessment of myocardial ischemia without ionizing radiation. However, quantification of perfusion is challenging due to the non-linearity between the magnetic resonance signal intensity and contrast agent concentration. Furthermore, respiratory motion during data acquisition precludes quantification of perfusion. While motion correction techniques have been proposed, they have been hampered by the challenge of accounting for dramatic contrast changes during the bolus and long execution times. In this work we investigate the use of a novel free-breathing multi-echo Dixon technique for quantitative myocardial perfusion. The Dixon fat images, unaffected by the dynamic contrast-enhancement, are used to efficiently estimate rigid-body respiratory motion and the computed transformations are applied to the corresponding diagnostic water images. This is followed by a second non-linear correction step using the Dixon water images to remove residual motion. The proposed Dixon motion correction technique was compared to the state-of-the-art technique (spatiotemporal based registration). We demonstrate that the proposed method performs comparably to the state-of-the-art but is significantly faster to execute. Furthermore, the proposed technique can be used to correct for the decay of signal due to T2* effects to improve quantification and additionally, yields fat-free diagnostic images.

As shown by the Green Deal's ambition, the European Commission is progressively pushing for an environmental shift and climate action in Europe. For the Common Agricultural Policy (CAP), this involves a stronger focus on greening policy objectives. For agri-environmental schemes, this entails changes toward performance-based payments, partially replacing traditional activity-based payments. The CAP foresees greater flexibility in national programs and tailor-made solutions centered on results (i.e. environmental outcomes), benefiting farmers who go beyond the minimum environmental performance required. The environmental outcomes of farm practices must be assessed so that changes can be monitored over time and linked to payment delivery. This requires stakeholders to collaborate with researchers to identify farm-based indicators that are easily applicable, to achieve environmental results that are dependent on farm practices, and to assess and monitor changes in outcomes over time. The analysis in this paper is based on a transdisciplinary process that began in 2017 in a Natura 2000 site and its surroundings in Southern Portugal, to identify result-based measures for the Montado silvo-pastoral system. Farmers' understanding of how to adapt their practices to reach better environmental results was combined with scientific knowledge of the relevant environmental outcomes and how these can be assessed with indicators. Ten field-based visual indicators were defined, which farmers applied in the field, and validated by technical staff. These indicators are related to several aspects of the silvo-pastoral system: soil quality, pasture diversity, tree renewal, tree health, singular landscape elements, and biodiversity. The approach used in this process was innovative. We describe each step and present its advantages and drawbacks for designing and implementing result-based payments. Ultimately, their implementation is expected to lead to higher sustainability in the Montado.