Dual-Energy Computed Tomography for Accurate Stopping-Power Prediction in Proton Treatment Planning

Derzeitige Reichweiteunsicherheiten in der Protonentherapie verhindern das vollständige Ausschöpfen ihrer physikalischen Vorteile. Ein wesentlicher Anteil ist dabei auf die Vorhersage der Reichweite mittels Röntgen-Computertomographie (CT) zurückzuführen. Um die CT-bezogene Unsicherheit zu verringern, wird die Zwei-Spektren-Computertomographie (DECT) als vielversprechend angesehen. Innerhalb dieser Arbeit wurde die Anwendbarkeit von DECT in der Protonentherapie untersucht. Zunächst wurde ein CT-Scanprotokoll für die Strahlentherapie hinsichtlich Bildqualität und Konstanz der CT-Zahlen für verschiedene Körperregionen und -größen optimiert. Anschließend wurde die patientenindividuelle DECT- basierte Reichweitevorhersage kalibriert und ihre Genauigkeit in zwei Experimenten mit bekannter Referenz unter Verwendung eines anthropomorphen Phantoms und von homogenen biologischen Geweben verifiziert. Die klinische Relevanz von DECT wurde in einer retrospektiven Analyse von Krebspatienten mit Tumoren im Kopf, Becken oder Thorax nachgewiesen. Die systematischen Reichweiteunterschiede zwischen DECT und dem klinischen Standardverfahren konnten durch die Optimierung der Standardmethode basierend auf zusätzlichen mit DECT erworbenen Patienteninformationen reduziert werden. Somit wurde DECT erstmalig klinisch genutzt, um die Reichweiteberechnung zu verbessern. Die patientenindividuelle DECT-basierte Reichweitevorhersage kann zusätzlich Gewebevariabilitäten innerhalb eines und zwischen Patienten berücksichtigen, wie für Kopftumorpatienten gezeigt wurde. Dies legt den Grundstein für eine genauere Reichweiteberechnung und eröffnet neue Möglichkeiten für die Reduktion klinischer Sicherheitssäume, in denen die CT-bezogenen Unsicherheiten berücksichtigt sind.:1 Introduction 2 Physical Principles of Computed Tomography 2.1 Image Acquisition 2.2 Image Reconstruction 2.3 Dual-Energy Computed Tomography 3 Physical Principles of Proton Therapy 3.1 Treatment Techniques 3.2 Uncertainties in Proton Therapy 4 Principles of Stopping-Power Prediction from Computed Tomography 4.1 Single-Energy Computed Tomography 4.2 Dual-Energy Computed Tomography 5 Experimental Calibration of Stopping-Power Prediction 5.1 Scan Protocol Optimisation in Computed Tomography 5.2 Characterisation of Pseudo-Monoenergetic CT Calculation 5.3 Determination of Proton Stopping Power 5.4 Calibration of Stopping-Power Prediction Methods 6 Experimental Verification of Stopping-Power Prediction 6.1 Anthropomorphic Head Phantom 6.2 Homogeneous Biological Tissue Samples 7 Clinical Translation and Validation of Dual-Energy Computed Tomography 7.1 Feasibility of Dual-Spiral Dual-Energy CT 7.2 Range Prediction in Cerebral and Pelvic Tumour Patients 7.3 Tissue Variability in Brain-Tumour Patients 7.4 Feasibility of 4D Dual-Spiral Dual-Energy CT 7.5 DECT-Based Refinement of the Hounsfield Look-Up Table 8 Summary 9 ZusammenfassungRange uncertainty in proton therapy currently hampers the full exploitation of its physical advantages. A substantial amount of this uncertainty arises from proton range prediction based on X-ray computed tomography (CT). Dual-energy CT (DECT) has often been suggested as a promising imaging modality to reduce this CT-related range uncertainty. Within this thesis, the translation of DECT into application in proton therapy was evaluated. First, a CT scan protocol was optimised for radiotherapy considering the image quality and CT number stability for various body regions and sizes. The patient-specific DECT-based range prediction was then calibrated and its accuracy validated in two ground-truth experiments using an anthropomorphic phantom and homogeneous biological tissues. Subsequently, the clinical relevance of DECT was demonstrated in a retrospective cohort analysis of cerebral, pelvic and thoracic tumour patients. The systematic range deviations between the DECT and state-of-the-art approach were then reduced by adapting the standard method utilizing additional patient information obtained from DECT. Hence, DECT was clinically applied for the first time to refine proton range calculation. As a further step, the use of patient-specific DECT-based range prediction also considers intra- and inter-patient tissue variabilities as quantified in brain-tumour patients. A future implementation will be an important cornerstone to improve proton range calculation and might open up the possibility to reduce clinical safety margins accounting for the CT-related range uncertainty.:1 Introduction 2 Physical Principles of Computed Tomography 2.1 Image Acquisition 2.2 Image Reconstruction 2.3 Dual-Energy Computed Tomography 3 Physical Principles of Proton Therapy 3.1 Treatment Techniques 3.2 Uncertainties in Proton Therapy 4 Principles of Stopping-Power Prediction from Computed Tomography 4.1 Single-Energy Computed Tomography 4.2 Dual-Energy Computed Tomography 5 Experimental Calibration of Stopping-Power Prediction 5.1 Scan Protocol Optimisation in Computed Tomography 5.2 Characterisation of Pseudo-Monoenergetic CT Calculation 5.3 Determination of Proton Stopping Power 5.4 Calibration of Stopping-Power Prediction Methods 6 Experimental Verification of Stopping-Power Prediction 6.1 Anthropomorphic Head Phantom 6.2 Homogeneous Biological Tissue Samples 7 Clinical Translation and Validation of Dual-Energy Computed Tomography 7.1 Feasibility of Dual-Spiral Dual-Energy CT 7.2 Range Prediction in Cerebral and Pelvic Tumour Patients 7.3 Tissue Variability in Brain-Tumour Patients 7.4 Feasibility of 4D Dual-Spiral Dual-Energy CT 7.5 DECT-Based Refinement of the Hounsfield Look-Up Table 8 Summary 9 Zusammenfassun

Terminology and Classification of Muscle Injuries in Sport: The Munich Consensus Statement

Objective: To provide a clear terminology and classification of muscle injuries in order to facilitate effective communication among medical practitioners and development of systematic treatment strategies. Methods: Thirty native English-speaking scientists and team doctors of national and first division professional sports teams were asked to complete a questionnaire on muscle injuries to evaluate the currently used terminology of athletic muscle injury. In addition, a consensus meeting of international sports medicine experts was established to develop practical and scientific definitions of muscle injuries as well as a new and comprehensive classification system. Results: The response rate of the survey was 63%. The responses confirmed the marked variability in the use of the terminology relating to muscle injury, with the most obvious inconsistencies for the term strain. In the consensus meeting, practical and systematic terms were defined and established. In addition, a new comprehensive classification system was developed, which differentiates between four types: functional muscle disorders (type 1: overexertion-related and type 2: neuromuscular muscle disorders) describing disorders without macroscopic evidence of fibre tear and structural muscle injuries (type 3: partial tears and type 4: (sub)total tears/tendinous avulsions) with macroscopic evidence of fibre tear, that is, structural damage. Subclassifications are presented for each type. Conclusions: A consistent English terminology as well as a comprehensive classification system for athletic muscle injuries which is proven in the daily practice are presented. This will help to improve clarity of communication for diagnostic and therapeutic purposes and can serve as the basis for future comparative studies to address the continued lack of systematic information on muscle injuries in the literature. What are the new things: Consensus definitions of the terminology which is used in the field of muscle injuries as well as a new comprehensive classification system which clearly defines types of athletic muscle injuries

Stomatal optimisation based on xylem hydraulics (SOX) improves land surface model simulation of vegetation responses to climate

This is the final version. Available on open access via the DOI in this record•Land surface models (LSMs) typically use empirical functions to represent vegetation responses to soil drought. These functions largely neglect recent advances in plant ecophysiology that link xylem hydraulic functioning with stomatal responses to climate. •We developed an analytical stomatal optimisation model based on xylem hydraulics (SOX) to predict plant responses to drought. Coupling SOX to the Joint UK Land Environment Simulator (JULES) LSM, we conducted a global evaluation of SOX against leaf- and ecosystem-level observations. •SOX simulates leaf stomatal conductance responses to climate for woody plants more accurately and parsimoniously than the existing JULES stomatal conductance model. An ecosystem-level evaluation at 70 eddy flux sites shows that SOX decreases the sensitivity of gross primary productivity (GPP) to soil moisture, which improves the model agreement with observations and increases the predicted annual GPP by 30% in relation to JULES. SOX decreases JULES root mean squared error in GPP by up to 45 % in evergreen tropical forests, and can simulate realistic patterns of canopy water potential and soil water dynamics at the studied sites. •SOX provides a parsimonious way to incorporate recent advances in plant hydraulics and optimality theory into LSMs, and an alternative to empirical stress factors.Newton Fund through the Met Office Climate Science for Service Partnership Brazil (CSSP Brazil)Natural Environment Research Council (NERC

Experimental assessment of inter-centre variation in stopping-power and range prediction in particle therapy

Purpose: Experimental assessment of inter-centre variation and absolute accuracy of stopping-power ratio (SPR) prediction within 17 particle therapy centres of the European Particle Therapy Network. Material and methods: A head and body phantom with seventeen tissue-equivalent materials were scanned consecutively at the participating centres using their individual clinical CT scan protocol and translated into SPR with their in-house CT-number-to-SPR conversion. Inter-centre variation and absolute accuracy in SPR prediction were quantified for three tissue groups: lung, soft tissues and bones. The integral effect on range prediction for typical clinical beams traversing different tissues was determined for representative beam paths for the treatment of primary brain tumours as well as lung and prostate cancer. Results: An inter-centre variation in SPR prediction (2 sigma) of 8.7%, 6.3% and 1.5% relative to water was determined for bone, lung and soft-tissue surrogates in the head setup, respectively. Slightly smaller variations were observed in the body phantom (6.2%, 3.1%, 1.3%). This translated into inter-centre variation of integral range prediction (2 sigma) of 2.9%, 2.6% and 1.3% for typical beam paths of prostate-, lung-and primary brain-tumour treatments, respectively. The absolute error in range exceeded 2% in every fourth participating centre. The consideration of beam hardening and the execution of an independent HLUT validation had a positive effect, on average. Conclusion: The large inter-centre variations in SPR and range prediction justify the currently clinically used margins accounting for range uncertainty, which are of the same magnitude as the inter-centre variation. This study underlines the necessity of higher standardisation in CT-number-to-SPR conversion. (C) 2021 The Authors. Published by Elsevier B.V

Towards long-term standardised carbon and greenhouse gas observations for monitoring Europe's terrestrial ecosystems : a review

Research infrastructures play a key role in launching a new generation of integrated long-term, geographically distributed observation programmes designed to monitor climate change, better understand its impacts on global ecosystems, and evaluate possible mitigation and adaptation strategies. The pan-European Integrated Carbon Observation System combines carbon and greenhouse gas (GHG; CO2, CH4, N2O, H2O) observations within the atmosphere, terrestrial ecosystems and oceans. High-precision measurements are obtained using standardised methodologies, are centrally processed and openly available in a traceable and verifiable fashion in combination with detailed metadata. The Integrated Carbon Observation System ecosystem station network aims to sample climate and land-cover variability across Europe. In addition to GHG flux measurements, a large set of complementary data (including management practices, vegetation and soil characteristics) is collected to support the interpretation, spatial upscaling and modelling of observed ecosystem carbon and GHG dynamics. The applied sampling design was developed and formulated in protocols by the scientific community, representing a trade-off between an ideal dataset and practical feasibility. The use of open-access, high-quality and multi-level data products by different user communities is crucial for the Integrated Carbon Observation System in order to achieve its scientific potential and societal value.Peer reviewe

Global maps of soil temperature

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km² resolution for 0–5 and 5–15 cm soil depth. These maps were created by calculating the difference (i.e., offset) between in-situ soil temperature measurements, based on time series from over 1200 1-km² pixels (summarized from 8500 unique temperature sensors) across all the world’s major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (-0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in-situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications