The Role of Cultural Humility in Intercultural Representations

Not peer reviewe

Combating the effects of climatic change on forests by mitigation strategies

<p>Abstract</p> <p>Background</p> <p>Forests occur across diverse biomes, each of which shows a specific composition of plant communities associated with the particular climate regimes. Predicted future climate change will have impacts on the vulnerability and productivity of forests; in some regions higher temperatures will extend the growing season and thus improve forest productivity, while changed annual precipitation patterns may show disadvantageous effects in areas, where water availability is restricted. While adaptation of forests to predicted future climate scenarios has been intensively studied, less attention was paid to mitigation strategies such as the introduction of tree species well adapted to changing environmental conditions.</p> <p>Results</p> <p>We simulated the development of managed forest ecosystems in Germany for the time period between 2000 and 2100 under different forest management regimes and climate change scenarios. The management regimes reflect different rotation periods, harvesting intensities and species selection for reforestations. The climate change scenarios were taken from the IPCC's Special Report on Emission Scenarios (SRES). We used the scenarios A1B (rapid and successful economic development) and B1 (high level of environmental and social consciousness combined with a globally coherent approach to a more sustainable development). Our results indicate that the effects of different climate change scenarios on the future productivity and species composition of German forests are minor compared to the effects of forest management.</p> <p>Conclusions</p> <p>The inherent natural adaptive capacity of forest ecosystems to changing environmental conditions is limited by the long life time of trees. Planting of adapted species and forest management will reduce the impact of predicted future climate change on forests.</p

4D dose simulation in volumetric arc therapy: Accuracy and affecting parameters

<div><p>Radiotherapy of lung and liver lesions has changed from normofractioned 3D-CRT to stereotactic treatment in a single or few fractions, often employing volumetric arc therapy (VMAT)-based techniques. Potential unintended interference of respiratory target motion and dynamically changing beam parameters during VMAT dose delivery motivates establishing 4D quality assurance (4D QA) procedures to assess appropriateness of generated VMAT treatment plans when taking into account patient-specific motion characteristics. Current approaches are motion phantom-based 4D QA and image-based 4D VMAT dose simulation. Whereas phantom-based 4D QA is usually restricted to a small number of measurements, the computational approaches allow simulating many motion scenarios. However, 4D VMAT dose simulation depends on various input parameters, influencing estimated doses along with mitigating simulation reliability. Thus, aiming at routine use of simulation-based 4D VMAT QA, the impact of such parameters as well as the overall accuracy of the 4D VMAT dose simulation has to be studied in detail–which is the topic of the present work. In detail, we introduce the principles of 4D VMAT dose simulation, identify influencing parameters and assess their impact on 4D dose simulation accuracy by comparison of simulated motion-affected dose distributions to corresponding dosimetric motion phantom measurements. Exploiting an ITV-based treatment planning approach, VMAT treatment plans were generated for a motion phantom and different motion scenarios (sinusoidal motion of different period/direction; regular/irregular motion). 4D VMAT dose simulation results and dose measurements were compared by local 3% / 3 mm <i>γ</i>-evaluation, with the measured dose distributions serving as ground truth. Overall <i>γ</i>-passing rates of simulations and dynamic measurements ranged from 97% to 100% (mean across all motion scenarios: 98% ± 1%); corresponding values for comparison of different day repeat measurements were between 98% and 100%. Parameters of major influence on 4D VMAT dose simulation accuracy were the degree of temporal discretization of the dose delivery process (the higher, the better) and correct alignment of the assumed breathing phases at the beginning of the dose measurements and simulations. Given the high <i>γ</i>-passing rates between simulated motion-affected doses and dynamic measurements, we consider the simulations to provide a reliable basis for assessment of VMAT motion effects that–in the sense of 4D QA of VMAT treatment plans–allows to verify target coverage in hypofractioned VMAT-based radiotherapy of moving targets. Remaining differences between measurements and simulations motivate, however, further detailed studies.</p></div

Study design and evaluation strategy.

<p>Illustration of performed experiments for the SI-only sinusoidal motion with 4.5 s period (i. e. case 1d); for details see text. Left column: planned dose distribution (top), simulated motion-affected dose (middle; arc discretization of 2.3°), <i>γ</i>-map for comparison of the two (bottom). Middle column: measured static dose (top), measured dynamic dose (middle), <i>γ</i>-comparison (bottom). Right column: <i>γ</i>-comparison of planned and measured static dose (top), <i>γ</i>-comparison of simulated motion-affected and corresponding measured dose (middle), <i>γ</i>-comparison of repeat dynamic measurements (bottom).</p

Total <i>γ</i>-passing rates for comparison of static dose measurements to dynamic measurements (lines ‘Day 1’ and ‘Day 2’) and <i>γ</i>-passing rates for comparison of the statically planned dose and the dose distributions containing simulated motion effects.

<p>Total <i>γ</i>-passing rates for comparison of static dose measurements to dynamic measurements (lines ‘Day 1’ and ‘Day 2’) and <i>γ</i>-passing rates for comparison of the statically planned dose and the dose distributions containing simulated motion effects.</p

Starting phase influence.

<p>Influence of breathing phase at dose delivery beginning. Left, top: In accordance with the measurements, all previous results were computed with the simulations starting at the breathing phase at <i>t</i> = 0 s of the curve (here: case 1b). Now, this starting phase was systematically varied by adding offsets . Left, bottom: The ITV <i>γ</i>-passing rates for comparison of planned static and motion-affected simulated dose distributions are shown as red lines (solid lines: Δ<i>α</i> = 2.3°; dashed: Δ<i>α</i> = 150°); the black lines visualize the dependence of the difference between dynamic measurement and simulated motion-affected dose on the starting phase. Right: similar information but for the regular real tumor trajectory (case 2a).</p

Patient motion scenarios.

<p>SI motion amplitudes of applied regular and irregular tumor trajectories.</p

ITV <i>γ</i>-passing rates for comparison of static dose distributions and dynamic dose measurements/simulations.

<p>ITV <i>γ</i>-passing rates for comparison of static dose distributions and dynamic dose measurements/simulations.</p

Intelligent 4D CT sequence scanning (i4DCT): First scanner prototype implementation and phantom measurements of automated breathing signal‐guided 4D CT

Purpose Four‐dimensional (4D) computed tomography (CT) imaging is an essential part of current 4D radiotherapy treatment planning workflows, but clinical 4D CT images are often affected by artifacts. The artifacts are mainly caused by breathing irregularity during data acquisition, which leads to projection data coverage issues for currently available commercial 4D CT protocols. It was proposed to improve projection data coverage by online respiratory signal analysis and signal‐guided CT tube control, but related work was always theoretical and presented as pure in silico studies. The present work demonstrates a first CT prototype implementation along with respective phantom measurements for the recently introduced intelligent 4D CT (i4DCT) sequence scanning concept (https://doi.org/10.1002/mp.13632). Methods Intelligent 4D CT was implemented on the Siemens SOMATOM go platform. Four‐dimensional CT measurements were performed using the CIRS motion phantom. Motion curves were programmed to systematically vary from regular to very irregular, covering typical irregular patterns that are known to result in image artifacts using standard 4D CT imaging protocols. Corresponding measurements were performed using i4DCT and routine spiral 4D CT with similar imaging parameters (e.g., mAs setting and gantry rotation time, retrospective ten‐phase reconstruction) to allow for a direct comparison of the image data. Results Following technological implementation of i4DCT on the clinical CT scanner platform, 4D CT motion artifacts were significantly reduced for all investigated levels of breathing irregularity when compared to routine spiral 4D CT scanning. Conclusions The present study confirms feasibility of fully automated respiratory signal‐guided 4D CT scanning by means of a first implementation of i4DCT on a CT scanner. The measurements thereby support the conclusions of respective in silico studies and demonstrate that respiratory signal‐guided 4D CT (here: i4DCT) is ready for integration into clinical CT scanners