Development of a Downscaling Scheme for a Coarse Scale Soil Water Estimation Method

Many river basins worldwide, especially in semi-arid regions, are adversely impacted by poor hydrological infrastructure or are poorly characterized due to limited or no hydrologic data. This condition challenges water-management authorities, who benefit from reliable prediction of the hydrological dynamics that can be made by means of hydrological models. Because of the lack of sufficient or reliable data, often such models are difficult to calibrate and to validate. This study addresses this data limitation by formulating and testing an independent validation tool for hydrological models that can be applied to downscale macro-scale soil water data derived from a remotely sensed scatterometer dataset. This proposed method uses the concept of hydrological response units (HRU) to analyze the spatial variability within one scatterometer footprint. The HRUs are treated as model entities in the process oriented hydrological model J2000 that was applied to the Great Letaba River catchment (ca. 4.700 km²) in South Africa. The soil water time series results were then compared to the remotely sensed data set and the downscaling scheme derived. First, the analysis conducted on footprint scale highlights the similarities in predicting the soil water generation over the long term and in seasonal terms. It also exhibits that the absolute values of both time series can not be used for further investigation, due to differences in the observed soil water volume. Second, the resulted simulated soil water time series were used to establish the downscaling method. Here, the study provides promising results that allow the downscaling of the coarse scale soil water calculated dataset, based upon the landscape related parameters of land cover, soil group and precipitation. The study findings indicate that, by linking the two concepts, hydrological modeling and remote sensing, water management authorities should be able to reduce certain prediction uncertainties of the applied models

Near-real time oculodynamic MRI: a feasibility study for evaluation of diplopia in comparison with clinical testing

Objective: To demonstrate feasibility of near-real-time oculodynamic magnetic resonance imaging (od-MRI) in depicting extraocular muscles and correlate quantitatively the motion degree in comparison with clinical testing in patients with diplopia. Methods: In 30 od-MRIs eye movements were tracked in the horizontal and sagittal plane using a a TrueFISP sequence with high temporal resolution. Three physicians graded the visibility of extraocular muscles by a qualitative scale. In 12 cases, the maximal monocular excursions in the horizontal and vertical direction of both eyes were measured in od-MRIs and a clinical test and correlated by the Pearson test. Results: The medial and lateral rectus muscles were visible in the axial plane in 93% of the cases. The oblique, superior and inferior rectus muscles were overall only in 14% visible. Horizontal (p = 0,015) and vertical (p = 0,029) movements of the right eye and vertical movement of the left eye (p = 0,026) measured by od-MRI correlated positively to the clinical measurements. Conclusions: Od-MRI is a feasible technique. Visualization of the horizontal/vertical rectus muscles is better than for the superior/inferior oblique muscle. Od-MRI correlates well with clinical testing and may reproduce the extent of eye bulb motility and extraocular muscle structural or functional deteriorations. Key Points • Oculodynamic MRI technique helps clinicians to assess eye bulb motility disorders • MRI evaluation of eye movement provides functional information in cases of diplopia • Oculodynamic MRI reproduces excursion of extraocular muscles with good correlation with clinical testing • Dynamic MRI sequence supplements static orbital protocol for evaluation of motility disorder

Naturbasierte Lösungen zur Stärkung der Resilienz in Städten

Chapter 10 (in German). Als naturbasierte Lösungen (NBL) werden auf EU-Ebene naturbezogene Ansätze bezeichnet, die als Instrumente zur Bewältigung gesellschaftlicher Herausforderungen hinsichtlich des Klimawandels dienen. Eine Stadt erhöht ihre Resilienz, wenn ihre NBL-Ansätze auch soziale Probleme und das Wohlergehen der Bürger*innen ansprechen. Dieser Beitrag ist der beispielhaften Anwendung von NBL in drei europäischen Städten unterschiedlicher Größe gewidmet: einer Megacity (Region Paris, Frankreich), einer mittelgroßen Stadt (Aarhus, Dänemark) und einer Kleinstadt (Velika Gorica, Kroatien). Dabei wird untersucht, welche Herausforderungen und Chancen bei der Anwendung von NBL in verschiedenen sozialen und ökologischen Systemen auftreten und inwiefern NBL ein Schlüssel zur städtischen Resilienz sind

Analysis on the supportive technology for optimized manufacturability vs stack performance

The content of this document is a technology and business study in the field of fuel cell technologies in the transport sector (parcel delivery), depending on government requirements (latest pollution laws, etc.) and focusing on the technical aspects. It will give an overall outlook regarding future fuel cell EU targets, resulting in a pre-selection of relevant (present) stacks. The critical technical aspects related to the targeted production ratio will be given and illuminated. In cooperation with WP 2, a review will be performed regarding advantageous manufacturing technologies and strategies: WP2 Redesign current stack and stack design components for mass production and design to-cost. For this purpose, an overview of state of the art manufacturing systems will be provided. In close collaboration with the consortium partners the correct balance has been determined between manufacturability and stack performance. PM has predefined their current and their target automation, production rate and test cycle time for fuel cell stacks. EWii has calculated their current and their target automation and production rate of fuel cell stack components and important requirements regarding quality control and testing, supported by TUC. Based on this PM has also defined a plan for upscaling its balance of plant (BoP) component assembling capabilities to complement the implementation of the automated stack manufacturing. Critical technical aspects are pointed out regarding the targeted production ratio. In order to improve the understanding of the recommendations and related guidance developed in this report, reference to the main objective or product requirements of UPS (delivery service) shall be given. For the delivery sector, it is important to have a range extender for the delivery cars. The demands placed on such range extenders by the parcel service are short charging or refuelling times, and much longer service life compared to privately used cars. However, features of the FC stack for LCV application especially as a range extender such as dimension, weight and power range tend to be subordinate to those prioritized in the private automotive sector. Other specifications may be more critical such as lifetime requirements of up to 20,000 operating hours or even more

Globe restriction in a severely myopic patient visualized through oculodynamic magnetic resonance imaging (od-MRI)

Different mechanisms have been hypothesized as contributing to abduction deficit in high myopia: the size of the eye within the orbit, tightness of the medial rectus muscles, decompensation of longstanding esotropia, and inferior displacement of the lateral rectus muscle. Using oculodynamic magnetic resonance imaging, enhanced by computer-aided visualization, we demonstrate globe restriction by the medial orbital wall on abduction in a patient with high myopia

Near-real time oculodynamic MRI: a feasibility study for evaluation of diplopia in comparison with clinical testing

To demonstrate feasibility of near-real-time oculodynamic magnetic resonance imaging (od-MRI) in depicting extraocular muscles and correlate quantitatively the motion degree in comparison with clinical testing in patients with diplopia

Fully Automated MR

Background Non-small cell lung cancer (NSCLC) is the most common tumor entity spreading to the brain and up to 50% of patients develop brain metastases (BMs). Detection of BMs on MRI is challenging with an inherent risk of missed diagnosis. Purpose To train and evaluate a deep learning model (DLM) for fully automated detection and 3D segmentation of BMs in NSCLC on clinical routine MRI. Study Type Retrospective. Population Ninety-eight NSCLC patients with 315 BMs on pretreatment MRI, divided into training (66 patients, 248 BMs) and independent test (17 patients, 67 BMs) and control (15 patients, 0 BMs) cohorts. Field Strength/Sequence T-1-/T-2-weighted, T-1-weighted contrast-enhanced (T1CE; gradient-echo and spin-echo sequences), and FLAIR at 1.0, 1.5, and 3.0 T from various vendors and study centers. Assessment A 3D convolutional neural network (DeepMedic) was trained on the training cohort using 5-fold cross-validation and evaluated on the independent test and control sets. Three-dimensional voxel-wise manual segmentations of BMs by a neurosurgeon and a radiologist on T1CE served as the reference standard. Statistical Tests Sensitivity (recall) and false positive (FP) findings per scan, dice similarity coefficient (DSC) to compare the spatial overlap between manual and automated segmentations, Pearson's correlation coefficient (r) to evaluate the relationship between quantitative volumetric measurements of segmentations, and Wilcoxon rank-sum test to compare the volumes of BMs. A P value In the test set, the DLM detected 57 of the 67 BMs (mean volume: 0.99 +/- 4.24 cm(3)), resulting in a sensitivity of 85.1%, while FP findings of 1.5 per scan were observed. Missed BMs had a significantly smaller volume (0.05 +/- 0.04 cm(3)) than detected BMs (0.96 +/- 2.4 cm(3)). Compared with the reference standard, automated segmentations achieved a median DSC of 0.72 and a good volumetric correlation (r = 0.95). In the control set, 1.8 FPs/scan were observed. Data Conclusion Deep learning provided a high detection sensitivity and good segmentation performance for BMs in NSCLC on heterogeneous scanner data while yielding a low number of FP findings. 3 2 Level of Evidence Technical Efficacy Stag

Fully Automated MR Detection and Segmentation of Brain Metastases in Non-small Cell Lung Cancer Using Deep Learning

Background Non-small cell lung cancer (NSCLC) is the most common tumor entity spreading to the brain and up to 50% of patients develop brain metastases (BMs). Detection of BMs on MRI is challenging with an inherent risk of missed diagnosis. Purpose To train and evaluate a deep learning model (DLM) for fully automated detection and 3D segmentation of BMs in NSCLC on clinical routine MRI. Study Type Retrospective. Population Ninety-eight NSCLC patients with 315 BMs on pretreatment MRI, divided into training (66 patients, 248 BMs) and independent test (17 patients, 67 BMs) and control (15 patients, 0 BMs) cohorts. Field Strength/Sequence T-1-/T-2-weighted, T-1-weighted contrast-enhanced (T1CE; gradient-echo and spin-echo sequences), and FLAIR at 1.0, 1.5, and 3.0 T from various vendors and study centers. Assessment A 3D convolutional neural network (DeepMedic) was trained on the training cohort using 5-fold cross-validation and evaluated on the independent test and control sets. Three-dimensional voxel-wise manual segmentations of BMs by a neurosurgeon and a radiologist on T1CE served as the reference standard. Statistical Tests Sensitivity (recall) and false positive (FP) findings per scan, dice similarity coefficient (DSC) to compare the spatial overlap between manual and automated segmentations, Pearson's correlation coefficient (r) to evaluate the relationship between quantitative volumetric measurements of segmentations, and Wilcoxon rank-sum test to compare the volumes of BMs. A P value In the test set, the DLM detected 57 of the 67 BMs (mean volume: 0.99 +/- 4.24 cm(3)), resulting in a sensitivity of 85.1%, while FP findings of 1.5 per scan were observed. Missed BMs had a significantly smaller volume (0.05 +/- 0.04 cm(3)) than detected BMs (0.96 +/- 2.4 cm(3)). Compared with the reference standard, automated segmentations achieved a median DSC of 0.72 and a good volumetric correlation (r = 0.95). In the control set, 1.8 FPs/scan were observed. Data Conclusion Deep learning provided a high detection sensitivity and good segmentation performance for BMs in NSCLC on heterogeneous scanner data while yielding a low number of FP findings. 3 2 Level of Evidence Technical Efficacy Stag

Treatment monitoring of immunotherapy and targeted therapy using FET PET in patients with melanoma and lung cancer brain metastases: Initial experiences.

Background: Due to the lack of specificity of contrast-enhanced (CE) MRI, the differentiation of progression from pseudoprogression (PsP) following immunotherapy using checkpoint inhibitors (IT) or targeted therapy (TT) may be challenging, especially when IT or TT is applied in combination with radiotherapy (RT). Similarly, for response assessment of RT plus IT or targeted therapy (TT), the use of CE MRI alone may also be difficult. For problem solving, the integration of advanced imaging methods may add valuable information. Here, we evaluated the value of amino acid PET using O-(2-[18F]fluoroethyl)-L-tyrosine (FET) in comparison to CE MRI for these important clinical situations in patients with brain metastases (BM) secondary to malignant melanoma (MM) and non-small cell lung cancer (NSCLC). Methods: From 2015-2018, we retrospectively identified 31 patients with 74 BM secondary to MM (n = 20 with 42 BM) and NSCLC (n = 11 with 32 BM) who underwent 52 FET PET scans during the course of disease. All patients had RT prior to IT or TT initiation (61%) or RT concurrent to IT or TT (39%). In 13 patients, FET PET was performed for treatment response assessment of IT or TT using baseline and follow-up scans (median time between scans, 4.2 months). In the remaining 18 patients, FET PET was used for the differentiation of progression from PsP related to RT plus IT or TT. In all BM, metabolic activity on FET PET was evaluated by calculation of tumor/brain ratios. FET PET imaging findings were compared to CE MRI and correlated to the clinical follow-up or neuropathological findings after neuroimaging. Results: In 4 of 13 patients (31%), FET PET provided additional information for treatment response evaluation beyond the information provided by CE MRI alone. Furthermore, responding patients on FET PET had a median stable clinical follow-up of 10 months. In 10 of 18 patients (56%) with CE MRI findings suggesting progression, FET PET detected PsP. In 9 of these 10 patients, PsP was confirmed by a median stable clinical follow-up of 11 months. Conclusions: FET PET may add valuable information for treatment monitoring in individual BM patients undergoing RT in combination with IT or TT