EP-1557: Minimum prescription concept for dose painting increases robustness towards geometrical uncertainty

Intra-voxel heterogeneity influences the dose prescription for dose-painting with radiotherapy: a modelling study.

The purpose of this study was to increase the potential of dose redistribution by incorporating estimates of oxygen heterogeneity within imaging voxels for optimal dose determination. Cellular oxygen tension (pO(2)) distributions were estimated for imaging-size-based voxels by solving oxygen diffusion-consumption equations around capillaries placed at random locations. The linear-quadratic mode...

Robustness-based portfolio optimization under epistemic uncertainty

In this paper, we propose formulations and algorithms for robust portfolio optimization under both aleatory uncertainty (i.e., natural variability) and epistemic uncertainty (i.e., imprecise probabilistic information) arising from interval data. Epistemic uncertainty is represented using two approaches: (1) moment bounding approach and (2) likelihood-based approach. This paper first proposes a ...

Theragnostic imaging for radiation oncology : dose - painting

112 http://oncology.thelancet.com Vol 6 February 2005 Diagnostic imaging, from radiographs to CT, has been used throughout the history of radiotherapy to define the site to which the radiation should be delivered for patients with cancer. Theragnostic imaging is a method by which the radiation dose can be delivered in the four dimensions of space and time to achieve the optimum outcome after ra...

Towards Automatic Painting Authentication

This work investigates pattern recognition techniques that can be used to identify the author of fine art paintings. The methodology proposed consists of four sequential phases: image acquisition, segmentation, feature extraction, and classification. Brushstrokes are segmented assuming that hue is uniform within a single brushstroke. Using a wavelet-based technique, textures are measured and us...

Uncertainty Models for Robustness Analysis