Tenfold your photons -- a physically-sound approach to filtering-based variance reduction of Monte-Carlo-simulated dose distributions

X-ray dose constantly gains interest in the interventional suite. With dose being generally difficult to monitor reliably, fast computational methods are desirable. A major drawback of the gold standard based on Monte Carlo (MC) methods is its computational complexity. Besides common variance reduction techniques, filter approaches are often applied to achieve conclusive results within a fraction of time. Inspired by these methods, we propose a novel approach. We down-sample the target volume based on the fraction of mass, simulate the imaging situation, and then revert the down-sampling. To this end, the dose is weighted by the mass energy absorption, up-sampled, and distributed using a guided filter. Eventually, the weighting is inverted resulting in accurate high resolution dose distributions. The approach has the potential to considerably speed-up MC simulations since less photons and boundary checks are necessary. First experiments substantiate these assumptions. We achieve a median accuracy of 96.7 % to 97.4 % of the dose estimation with the proposed method and a down-sampling factor of 8 and 4, respectively. While maintaining a high accuracy, the proposed method provides for a tenfold speed-up. The overall findings suggest the conclusion that the proposed method has the potential to allow for further efficiency.Comment: 6 pages, 3 figures, Bildverarbeitung f\"ur die Medizin 202

Effective Altruism and Religion: Synergies, Tensions, Dialogue

Effective altruism has become a worldwide phenomenon. The movement combines empathy and reason in the attempt to improve the world. Adherents don’t let moral gut instincts dictate their altruistic efforts, but use evidence and reflection to do the most good they can. Effective altruism originated, and primarily grew, in strongly secular environments—such as philosophy departments or Silicon Valley. So far, a religious perspective on this movement has been lacking. What can people of faith learn from effective altruism? What may they criticise? What can effective altruism in turn take from religion? This volume offers a first examination of these questions, covering various Christian as well as Jewish and Buddhist perspectives

Effects of Tissue Material Properties on X-Ray Image, Scatter and Patient Dose Determined using Monte Carlo Simulations

With increasing patient and staff X-ray radiation awareness, many efforts have been made to develop accurate patient dose estimation methods. To date, Monte Carlo (MC) simulations are considered golden standard to simulate the interaction of X-ray radiation with matter. However, sensitivity of MC simulation results to variations in the experimental or clinical setup of image guided interventional procedures are only limited studied. In particular, the impact of patient material compositions is poorly investigated. This is mainly due to the fact, that these methods are commonly validated in phantom studies utilizing a single anthropomorphic phantom. In this study, we therefore investigate the impact of patient material parameters mapping on the outcome of MC X-ray dose simulations. A computation phantom geometry is constructed and three different commonly used material composition mappings are applied. We used the MC toolkit Geant4 to simulate X-ray radiation in an interventional setup and compared the differences in dose deposition, scatter distributions and resulting X-ray images. The evaluation shows a discrepancy between different material composition mapping up to 20 % concerning directly irradiated organs. These results highlight the need for standardization of material composition mapping for MC simulations in a clinical setup.Comment: 6 pages, 4 figures, Bildverarbeitung f\"ur die Medizin 201

Dynamical cluster approximation within an augmented plane-wave framework: Spectral properties of SrVO3_3

We present a combination of local density approximation (LDA) with the dynamical cluster approximation (LDA+DCA) in the framework of the full-potential linear augmented plane-wave method, and compare our LDA+DCA results for SrVO$_3$ to LDA with the dynamical mean field theory (LDA+DMFT) calculations as well as experimental observations on SrVO$_3$. We find a qualitative agreement of the momentum resolved spectral function with angle-resolved photoemission spectra (ARPES) and former LDA+DMFT results. As a correction to LDA+DMFT, we observe more pronounced coherent peaks below the Fermi level, as indicated by ARPES experiments. In addition, we resolve the spectral functions in the ${\bf K}_{0}=(0,0,0)$ and ${\bf K}_{1}=(\pi,\pi,\pi)$ sectors of DCA, where band insulating and metallic phases coexist. Our approach can be applied to correlated compounds where not only local quantum fluctuations but also spatial fluctuations are important.Comment: 6 pages, 3 figures, accepted in Phys. Rev.

Fully-automatic CT data preparation for interventional X-ray skin dose simulation

Recently, deep learning (DL) found its way to interventional X-ray skin dose estimation. While its performance was found to be acceptable, even more accurate results could be achieved if more data sets were available for training. One possibility is to turn to computed tomography (CT) data sets. Typically, computed tomography (CT) scans can be mapped to tissue labels and mass densities to obtain training data. However, care has to be taken to make sure that the different clinical settings are properly accounted for. First, the interventional environment is characterized by wide variety of table setups that are significantly different from the typical patient tables used in conventional CT. This cannot be ignored, since tables play a crucial role in sound skin dose estimation in an interventional setup, e. g., when the X-ray source is directly underneath a patient (posterior-anterior view). Second, due to interpolation errors, most CT scans do not facilitate a clean segmentation of the skin border. As a solution to these problems, we applied connected component labeling (CCL) and Canny edge detection to (a) robustly separate the patient from the table and (b) to identify the outermost skin layer. Our results show that these extensions enable fully-automatic, generalized pre-processing of CT scans for further simulation of both skin dose and corresponding X-ray projections.Comment: 6 pages, 4 figures, Bildverarbeitung f\"ur die Medizin 2020, code will be accessible soon (url