DiffuseRT: predicting likely anatomical deformations of patients undergoing radiotherapy.

Objective. Predicting potential deformations of patients can improve radiotherapy treatment planning. Here, we introduce new deep-learning models that predict likely anatomical changes during radiotherapy for head and neck cancer patients.Approach. Denoising diffusion probabilistic models (DDPMs) were developed to generate fraction-specific anatomical changes based on a reference cone-beam CT (CBCT), the fraction number and the dose distribution delivered. Three distinct DDPMs were developed: (1) theimage modelwas trained to directly generate likely future CBCTs, (2) the deformable vector field (DVF) model was trained to generate DVFs that deform a reference CBCT and (3) thehybrid modelwas trained similarly to the DVF model, but without relying on an external deformable registration algorithm. The models were trained on 9 patients with longitudinal CBCT images (224 CBCTs) and evaluated on 5 patients (152 CBCTs).Results. The generated images mainly exhibited random positioning shifts and small anatomical changes for early fractions. For later fractions, all models predicted weight losses in accordance with the training data. The distributions of volume and position changes of the body, esophagus, and parotids generated with the image and hybrid models were more similar to the ground truth distribution than the DVF model, evident from the lower Wasserstein distance achieved with the image (0.33) and hybrid model (0.30) compared to the DVF model (0.36). Generating several images for the same fraction did not yield the expected variability since the ground truth anatomical changes were only in 76% of the fractions within the 95% bounds predicted with the best model. Using the generated images for robust optimization of simplified proton therapy plans improved the worst-case clinical target volume V95 with 7% compared to optimizing with 3 mm set-up robustness while maintaining a similar integral dose.Significance. The newly developed DDPMs generate distributions similar to the real anatomical changes and have the potential to be used for robust anatomical optimization.

Robust optimization strategies for contour uncertainties in online adaptive radiation therapy.

Objective.Online adaptive radiation therapy requires fast and automated contouring of daily scans for treatment plan re-optimization. However, automated contouring is imperfect and introduces contour uncertainties. This work aims at developing and comparing robust optimization strategies accounting for such uncertainties.Approach.A deep-learning method was used to predict the uncertainty of deformable image registration, and to generate a finite set of daily contour samples. Ten optimization strategies were compared: two baseline methods, five methods that convert contour samples into voxel-wise probabilities, and three methods accounting explicitly for contour samples as scenarios in robust optimization. Target coverage and organ-at-risk (OAR) sparing were evaluated robustly for simplified proton therapy plans for five head-and-neck cancer patients.Results.We found that explicitly including target contour uncertainty in robust optimization provides robust target coverage with better OAR sparing than the baseline methods, without increasing the optimization time. Although OAR doses first increased when increasing target robustness, this effect could be prevented by additionally including robustness to OAR contour uncertainty. Compared to the probability-based methods, the scenario-based methods spared the OARs more, but increased integral dose and required more computation time.Significance.This work proposed efficient and beneficial strategies to mitigate contour uncertainty in treatment plan optimization. This facilitates the adoption of automatic contouring in online adaptive radiation therapy and, more generally, enables mitigation also of other sources of contour uncertainty in treatment planning.

Deep learning based uncertainty prediction of deformable image registration for contour propagation and dose accumulation in online adaptive radiotherapy.

Objective.Online adaptive radiotherapy aims to fully leverage the advantages of highly conformal therapy by reducing anatomical and set-up uncertainty, thereby alleviating the need for robust treatments. This requires extensive automation, among which is the use of deformable image registration (DIR) for contour propagation and dose accumulation. However, inconsistencies in DIR solutions between different algorithms have caused distrust, hampering its direct clinical use. This work aims to enable the clinical use of DIR by developing deep learning methods to predict DIR uncertainty and propagating it into clinically usable metrics.Approach.Supervised and unsupervised neural networks were trained to predict the Gaussian uncertainty of a given deformable vector field (DVF). Since both methods rely on different assumptions, their predictions differ and were further merged into a combined model. The resulting normally distributed DVFs can be directly sampled to propagate the uncertainty into contour and accumulated dose uncertainty.Main results.The unsupervised and combined models can accurately predict the uncertainty in the manually annotated landmarks on the DIRLAB dataset. Furthermore, for 5 patients with lung cancer, the propagation of the predicted DVF uncertainty into contour uncertainty yielded for both methods anexpected calibration errorof less than 3%. Additionally, theprobabilisticly accumulated dose volume histograms(DVH) encompass well the accumulated proton therapy doses using 5 different DIR algorithms. It was additionally shown that the unsupervised model can be used for different DIR algorithms without the need for retraining.Significance.Our work presents first-of-a-kind deep learning methods to predict the uncertainty of the DIR process. The methods are fast, yield high-quality uncertainty estimates and are useable for different algorithms and applications. This allows clinics to use DIR uncertainty in their workflows without the need to change their DIR implementation.

Dosimetric comparison of autocontouring techniques for online adaptive proton therapy.

Objective.Anatomical and daily set-up uncertainties impede high precision delivery of proton therapy. With online adaptation, the daily plan is reoptimized on an image taken shortly before the treatment, reducing these uncertainties and, hence, allowing a more accurate delivery. This reoptimization requires target and organs-at-risk (OAR) contours on the daily image, which need to be delineated automatically since manual contouring is too slow. Whereas multiple methods for autocontouring exist, none of them are fully accurate, which affects the daily dose. This work aims to quantify the magnitude of this dosimetric effect for four contouring techniques.Approach.Plans reoptimized on automatic contours are compared with plans reoptimized on manual contours. The methods include rigid and deformable registration (DIR), deep-learning based segmentation and patient-specific segmentation.Main results.It was found that independently of the contouring method, the dosimetric influence of usingautomaticOARcontoursis small (<5% prescribed dose in most cases), with DIR yielding the best results. Contrarily, the dosimetric effect of using theautomatic target contourwas larger (>5% prescribed dose in most cases), indicating that manual verification of that contour remains necessary. However, when compared to non-adaptive therapy, the dose differences caused by automatically contouring the target were small and target coverage was improved, especially for DIR.Significance.The results show that manual adjustment of OARs is rarely necessary and that several autocontouring techniques are directly usable. Contrarily, manual adjustment of the target is important. This allows prioritizing tasks during time-critical online adaptive proton therapy and therefore supports its further clinical implementation.

Patient-specific neural networks for contour propagation in online adaptive radiotherapy.

Objective.fast and accurate contouring of daily 3D images is a prerequisite for online adaptive radiotherapy. Current automatic techniques rely either on contour propagation with registration or deep learning (DL) based segmentation with convolutional neural networks (CNNs). Registration lacks general knowledge about the appearance of organs and traditional methods are slow. CNNs lack patient-specific details and do not leverage the known contours on the planning computed tomography (CT). This works aims to incorporate patient-specific information into CNNs to improve their segmentation accuracy.Approach.patient-specific information is incorporated into CNNs by retraining them solely on the planning CT. The resulting patient-specific CNNs are compared to general CNNs and rigid and deformable registration for contouring of organs-at-risk and target volumes in the thorax and head-and-neck regions.Results.patient-specific fine-tuning of CNNs significantly improves contour accuracy compared to standard CNNs. The method further outperforms rigid registration and a commercial DL segmentation software and yields similar contour quality as deformable registration (DIR). It is additionally 7-10 times faster than DIR.Significance.patient-specific CNNs are a fast and accurate contouring technique, enhancing the benefits of adaptive radiotherapy.

Sphagnum bleaching: Bicarbonate 'toxicity' and tolerance for seven Sphagnum species.

Growth and functioning of Sphagnum mosses are closely linked to water level and chemistry. Sphagnum mosses occur in wet, generally acidic conditions, and when buffered, alkaline water is known to negatively impact Sphagnum. The effects of time, dose and species-specific responses of buffered, alkaline water on Sphagnum are largely unknown. We investigated the effects of bicarbonate and calcium on the survival, growth and physiological functioning of seven Sphagnum species occurring in contrasting environments, from raised bogs to (rich) fens. Mosses were submerged in different concentrations of bicarbonate and calcium solutions for 10 weeks under climate-controlled circumstances. After 2 weeks, all species exposed to the high bicarbonate treatment (2.0 mM) showed severe potassium leakage and swift discoloration. In contrast, species showed differential responses to the intermediate bicarbonate treatment (0.8 mM), some with a later onset of potassium leakage. S. squarrosum, S. teres & S. contortum generally persisted the longest, with all species dying after 6 to 10 weeks. Calcium alone, in contrast, negatively affected S. squarrosum, S. teres & S. contortum, causing discoloration and potassium leakage. Our study shows enrichment with bicarbonate, but not calcium, is detrimental for most Sphagnum species tested. A mechanistic model was developed that is consistent with dose and duration dependence and the species specificity. Future conservation and restoration measures for Sphagnum-dominated habitats and Sphagnum farming (cultivation, production and harvest of Sphagnum moss biomass) should limit flooding with bicarbonate-rich waters while investigating new management options, like acidifying surface waters to lower bicarbonate levels.

The state of the art in beyond 5G distributed massive multiple-input multiple-output communication system solutions.

Beyond fifth generation (5G) communication systems aim towards data rates in the tera bits per second range, with improved and flexible coverage options, introducing many new technological challenges in the fields of network architecture, signal pro- cessing, and radio frequency front-ends. One option is to move towards cell-free, or distributed massive Multiple-Input Multiple-Output (MIMO) network architectures and highly integrated front-end solutions. This paper presents an outlook on be- yond 5G distributed massive MIMO communication systems, the signal processing, characterisation and simulation challenges, and an overview of the state of the art in millimetre wave antennas and electronics.

Uncertainty in Reverberation-Chamber Antenna-Efficiency Measurements in the Presence of a Phantom.

The effect of a user's proximity on wireless device performance is critical to test the device under realistic conditions. In this work, we propose and demonstrate an improved uncertainty estimation method for antenna efficiency measurements in a reverberation chamber. The improved method separately computes uncertainties due to the effects of chamber loading by a phantom and the effects of antenna mismatch introduced by this phantom, illustrating the sensitivity of uncertainty to close-proximity user effects. We demonstrate that, while the impact of the phantom may be significant on antenna efficiency, and, it has some influence on the uncertainty in the measurement, its impact on overall uncertainty may be insignificant. This is demonstrated using the two-antenna method in the presence of a phantom close to the antenna under test. We illustrate the method by summarizing the antenna efficiencies with their uncertainties and the impact of the phantom for important communication bands. Due to the large effect of the user on antenna performance, this type of measurement and its uncertainty evaluation is a valuable way to characterize antenna efficiency including user effects.

V-band nanosecond-scale pulse reflectometer diagnostic in the TCV tokamak.

This article describes the realization of a novel approach to short pulse (∼1 ns) reflectometry (SPR) recently implemented in the tokamak configuration variable tokamak. Taking advantage of a fast arbitrary waveform generator and vector-network-analyzer extension modules, the design offers flexibility regarding pulse output frequency, duration, and repetition rate. Such flexibility allows the instrument to overcome traditional SPR spatial sampling limitations while reducing hardware complexity. In order to measure the group-delay of nanosecond-scale pulses, both traditional analog and novel digital sampling techniques have been explored. A group-delay range resolution of 17 ps (2.6 mm) in average over the V-band has been achieved with both timing techniques against a waveguide mirror featuring 10 dB power fluctuations. Direct pulse sampling during L-mode plasmas shows that reflected pulse widths increase only by 4% in average. However, pulse width dispersion does occur in L-mode plasmas and leads to an increase in the group-delay uncertainty up to 40 ps (6 mm). Raw histograms of group-delay data show interesting qualitative changes from the L mode to the H-mode. Frequency spectra of group-delay data allow the identification of macroscopic density fluctuations as well as edge quasicoherent modes during edge-localized mode-free H-modes. Finally, fast changes to the density profile have been measured with microsecond time resolution and subcentimeter spatial resolution in both O and X-mode polarizations.

A printed Yagi-Uda antenna for application in magnetic resonance thermometry guided microwave hyperthermia applicators.

Biological studies and clinical trials show that addition of hyperthermia stimulates conventional cancer treatment modalities and significantly improves treatment outcome. This supra-additive stimulation can be optimized by adaptive hyperthermia to counteract strong and dynamic thermoregulation. The only clinically proven method for the 3D non-invasive temperature monitoring required is by magnetic resonance (MR) temperature imaging, but the currently available set of MR compatible hyperthermia applicators lack the degree of heat control required. In this work, we present the design and validation of a high-frequency (433 MHz ISM band) printed circuit board antenna with a very low MR-footprint. This design is ideally suited for use in a range of hyperthermia applicator configurations. Experiments emulating the clinical situation show excellent matching properties of the antenna over a 7.2% bandwidth (S 11 < -15 dB). Its strongly directional radiation properties minimize inter-element coupling for typical array configurations (S 21 < -23 dB). MR imaging distortion by the antenna was found negligible and MR temperature imaging in a homogeneous muscle phantom was highly correlated with gold-standard probe measurements (root mean square error: RMSE = 0.51 °C and R 2 = 0.99). This work paves the way for tailored MR imaging guided hyperthermia devices ranging from single antenna or incoherent antenna-arrays, to real-time adaptive hyperthermia with phased-arrays.

Differential responses of two wetland graminoids to high ammonium at different pH values.

Enhanced soil ammonium (NH4+) concentrations in wetlands often lead to graminoid dominance, but species composition is highly variable. Although NH4+ is readily taken up as a nutrient, several wetland species are known to be sensitive to high NH4+ concentrations or even suffer toxicity, particularly at low soil pH. More knowledge about differential graminoid responses to high NH4+ availability in relation to soil pH can help to better understand vegetation changes. The responses of two wetland graminoids, Juncus acutiflorus and Carex disticha, to high (2 mmol·l(-1) ) versus control (20 µmol·l(-1) ) NH4+ concentrations were tested in a controlled hydroponic set up, at two pH values (4 and 6). A high NH4+ concentration did not change total biomass for these species at either pH, but increased C allocation to shoots and increased P uptake, leading to K and Ca limitation, depending on pH treatment. More than 50% of N taken up by C. disticha was invested in N-rich amino acids with decreasing C:N ratio, but only 10% for J. acutiflorus. Although both species appeared to be well adapted to high NH4+ loadings in the short term, C. disticha showed higher classic detoxifying responses that are early warning indicators for decreased tolerance in the long term. In general, the efficient aboveground biomass allocation, P uptake and N detoxification explain the competitive strength of wetland graminoids at the expense of overall biodiversity at high NH4+ loading. In addition, differential responses to enhanced NH4+ affect interspecific competition among graminoids and lead to a shift in vegetation composition.

Massive uprooting of Littorella uniflora (L.) Asch. during a storm event and its relation to sediment and plant characteristics.

During spring storms massive uprooting of Littorella uniflora occurred in a shallow Dutch softwater lake. The aim of this study was to test whether changes in plant morphology and sediment characteristics could explain the observed phenomenon. Uprooting was expected to occur in plants having a high shoot biomass and low root to shoot ratio (R:S), growing on sediments with a high organic matter content. Normally, uprooting of the relative buoyant L. uniflora is prevented by an extensive root system, expressed as a high R:S. This was studied by sampling floating and still rooted L. uniflora plants, as well as sediment and sediment pore water, along a gradient of increasing sediment organic matter content. Increasing organic matter content was related to increasing L. uniflora shoot biomass and consequently decreasing R:S. Furthermore, the results indicated that uprooting indeed occurred in plants growing on very organic sediments and was related to a low R:S. The increased shoot biomass on more organic sediments could be related to increased sediment pore water total inorganic carbon (TIC; mainly CO2 ) availability. Additionally, increased phosphorus availability could also have played a role. The disappearance of L. uniflora might lead to higher nutrient availability in the sediments. It is suggested that this could eventually promote the expansion of faster-growing macrophytes.

Nutrient additions in pristine Patagonian Sphagnum bog vegetation: can phosphorus addition alleviate (the effects of) increased nitrogen loads.

Sphagnum-bog ecosystems have a limited capability to retain carbon and nutrients when subjected to increased nitrogen (N) deposition. Although it has been proposed that phosphorus (P) can dilute negative effects of nitrogen by increasing biomass production of Sphagnum mosses, it is still unclear whether P-addition can alleviate physiological N-stress in Sphagnum plants. A 3-year fertilisation experiment was conducted in lawns of a pristine Sphagnum magellanicum bog in Patagonia, where competing vascular plants were practically absent. Background wet deposition of nitrogen was low (≈ 0.1-0.2 g · N · m(-2) · year(-1)). Nitrogen (4 g · N · m(-2) · year(-1)) and phosphorus (1 g · P · m(-2) · year(-1)) were applied, separately and in combination, six times during the growing season. P-addition substantially increased biomass production of Sphagnum. Nitrogen and phosphorus changed the morphology of Sphagnum mosses by enhancing height increment, but lowering moss stem density. In contrast to expectations, phosphorus failed to alleviate physiological stress imposed by excess nitrogen (e.g. amino acid accumulation, N-saturation and decline in photosynthetic rates). We conclude that despite improving growth conditions by P-addition, Sphagnum-bog ecosystems remain highly susceptible to nitrogen additions. Increased susceptibility to desiccation by nutrients may even worsen the negative effects of excess nitrogen especially in windy climates like in Patagonia.

Selecting predictors for discriminant analysis of species performance: an example from an amphibious softwater plant.

Selecting an appropriate variable subset in linear multivariate methods is an important methodological issue for ecologists. Interest often exists in obtaining general predictive capacity or in finding causal inferences from predictor variables. Because of a lack of solid knowledge on a studied phenomenon, scientists explore predictor variables in order to find the most meaningful (i.e. discriminating) ones. As an example, we modelled the response of the amphibious softwater plant Eleocharis multicaulis using canonical discriminant function analysis. We asked how variables can be selected through comparison of several methods: univariate Pearson chi-square screening, principal components analysis (PCA) and step-wise analysis, as well as combinations of some methods. We expected PCA to perform best. The selected methods were evaluated through fit and stability of the resulting discriminant functions and through correlations between these functions and the predictor variables. The chi-square subset, at P < 0.05, followed by a step-wise sub-selection, gave the best results. In contrast to expectations, PCA performed poorly, as so did step-wise analysis. The different chi-square subset methods all yielded ecologically meaningful variables, while probable noise variables were also selected by PCA and step-wise analysis. We advise against the simple use of PCA or step-wise discriminant analysis to obtain an ecologically meaningful variable subset; the former because it does not take into account the response variable, the latter because noise variables are likely to be selected. We suggest that univariate screening techniques are a worthwhile alternative for variable selection in ecology.

The Eocene Arctic Azolla bloom: environmental conditions, productivity and carbon drawdown.

Enormous quantities of the free-floating freshwater fern Azolla grew and reproduced in situ in the Arctic Ocean during the middle Eocene, as was demonstrated by microscopic analysis of microlaminated sediments recovered from the Lomonosov Ridge during Integrated Ocean Drilling Program (IODP) Expedition 302. The timing of the Azolla phase (approximately 48.5 Ma) coincides with the earliest signs of onset of the transition from a greenhouse towards the modern icehouse Earth. The sustained growth of Azolla, currently ranking among the fastest growing plants on Earth, in a major anoxic oceanic basin may have contributed to decreasing atmospheric pCO2 levels via burial of Azolla-derived organic matter. The consequences of these enormous Azolla blooms for regional and global nutrient and carbon cycles are still largely unknown. Cultivation experiments have been set up to investigate the influence of elevated pCO2 on Azolla growth, showing a marked increase in Azolla productivity under elevated (760 and 1910 ppm) pCO2 conditions. The combined results of organic carbon, sulphur, nitrogen content and 15N and 13C measurements of sediments from the Azolla interval illustrate the potential contribution of nitrogen fixation in a euxinic stratified Eocene Arctic. Flux calculations were used to quantitatively reconstruct the potential storage of carbon (0.9-3.5 10(18) gC) in the Arctic during the Azolla interval. It is estimated that storing 0.9 10(18) to 3.5 10(18) g carbon would result in a 55 to 470 ppm drawdown of pCO2 under Eocene conditions, indicating that the Arctic Azolla blooms may have had a significant effect on global atmospheric pCO2 levels through enhanced burial of organic matter.

Toxicity of reduced nitrogen in eelgrass (Zostera marina) is highly dependent on shoot density and pH.

In sheltered, eutrophicated estuaries, reduced nitrogen (NHx), and pH levels in the water layer can be greatly enhanced. In laboratory experiments, we studied the interactive effects of NHx, pH, and shoot density on the physiology and survival of eelgrass (Zostera marina). We tested long-term tolerance to NHx at pH 8 in a 5-week experiment. Short-term tolerance was tested for two shoot densities at both pH 8 and 9 in a 5-day experiment. At pH 8, eelgrass accumulated nitrogen as free amino acids when exposed to high loads of NHx, but showed no signs of necrosis. Low shoot density treatments became necrotic within days when exposed to NHx at pH 9. Increased NH3 intrusion and carbon limitation seemed to be the cause of this, as intracellular NHx could no longer be assimilated. Remarkably, experiments with high shoot densities at pH 9 showed hardly any necrosis, as the plants seemed to be able to alleviate the toxic effects of high NHx loads through joint NHx uptake. Our results suggest that NHx toxicity can be important in worldwide observed seagrass mass mortalities. We argue that the mitigating effect of high seagrass biomass on NHx toxicity is a positive feedback mechanism, potentially leading to alternative stable states in field conditions.

Effects of mining activities on heavy metal concentrations in water, sediment, and macroinvertebrates in different reaches of the Pilcomayo River, South America.

From 1997 until 1999 the extent and the ecological effects of zinc, copper, lead, and cadmium pollution were studied in different reaches of the South American Pilcomayo River. A comparison of metal concentrations in water, sediment, and chironomid larvae, as well as the diversity of macroinvertebrate species, was made between sites near the origin of the Pilcomayo River, with hardly any mining activities, sites in the Potosí region, with intensive mining, and sites located 500 km or further downstream of Potosí, in the Chaco plain. Samples were also collected in an unpolluted river (Cachi Mayu River) and in the Tarapaya River, which is strongly contaminated by mine tailings (1000 tons a day). The upper parts of the Pilcomayo River are strongly affected by the release of mine tailings from the Potosí mines where mean concentrations of lead, cadmium, copper, and zinc in water, filtered water, sediment, and chironomid larvae were up to a thousand times higher than the local background levels. The diversity of the benthic macroinvertebrate community was strongly reduced in the contaminated parts; 97% of the benthic macroinvertebrates consisted of chironomid larvae. The degree of contamination in the lower reaches of the river, however, was fairly low because of sedimentation processes and the strong dilution of mine tailings with enormous amounts of clean sediment from erosion processes. Analysis of sediment cores from the Ibibobo floodplain, however, reveal an increase of the heavy metal concentrations in the lower reaches since the introduction of the contaminating flotation process in the mine industry in 1985.

Potential sensitivity of mires to drought, acidification and mobilisation of heavy metals: the sediment s/(Ca + Mg) ratio as diagnostic tool.

In recent decades sulphate concentrations in the ground water in many parts of The Netherlands have increased dramatically resulting in increased production of iron-(di)sulphides in sediments of ecosystems fed by this water. A sediment survey was carried out to study the potential sensitivity of wetlands to drought and subsequent acidification as a consequence of iron-(di)sulphide oxidation. Dessication led to severe acidification and mobilisation of heavy metals when the sediment S/(Ca + Mg) ratio exceeded 2/ 3. A total of 47% of the investigated locations contained S/(Ca + Mg) ratios higher than 2/3 and in 100, 75 and 50% of the locations mobilisation of Zn, Cd and Ni exceeded the Dutch signal value for ground water. Consistent with the sediment survey, lime addition experiments confirmed that increasing the buffer capacity, down to a S/(Ca + Mg) ratio 2/3, led to a drastic inhibition of the acidification and heavy metal percolation from dredged sediments. The performance of the same processes under drained field conditions demonstrates the relevance of these processes during dry summers.