The unruptured intracranial aneurysm treatment score: a multidisciplinary consensus.

OBJECTIVE We endeavored to develop an unruptured intracranial aneurysm (UIA) treatment score (UIATS) model that includes and quantifies key factors involved in clinical decision-making in the management of UIAs and to assess agreement for this model among specialists in UIA management and research. METHODS An international multidisciplinary (neurosurgery, neuroradiology, neurology, clinical epidemiology) group of 69 specialists was convened to develop and validate the UIATS model using a Delphi consensus. For internal (39 panel members involved in identification of relevant features) and external validation (30 independent external reviewers), 30 selected UIA cases were used to analyze agreement with UIATS management recommendations based on a 5-point Likert scale (5 indicating strong agreement). Interrater agreement (IRA) was assessed with standardized coefficients of dispersion (vr*) (vr* = 0 indicating excellent agreement and vr* = 1 indicating poor agreement). RESULTS The UIATS accounts for 29 key factors in UIA management. Agreement with UIATS (mean Likert scores) was 4.2 (95% confidence interval [CI] 4.1-4.3) per reviewer for both reviewer cohorts; agreement per case was 4.3 (95% CI 4.1-4.4) for panel members and 4.5 (95% CI 4.3-4.6) for external reviewers (p = 0.017). Mean Likert scores were 4.2 (95% CI 4.1-4.3) for interventional reviewers (n = 56) and 4.1 (95% CI 3.9-4.4) for noninterventional reviewers (n = 12) (p = 0.290). Overall IRA (vr*) for both cohorts was 0.026 (95% CI 0.019-0.033). CONCLUSIONS This novel UIA decision guidance study captures an excellent consensus among highly informed individuals on UIA management, irrespective of their underlying specialty. Clinicians can use the UIATS as a comprehensive mechanism for indicating how a large group of specialists might manage an individual patient with a UIA.

Gradient-domain Path Tracing

We introduce gradient-domain rendering for Monte Carlo image synthesis.While previous gradient-domain Metropolis Light Transport sought to distribute more samples in areas of high gradients, we show, in contrast, that estimating image gradients is also possible using standard (non-Metropolis) Monte Carlo algorithms, and furthermore, that even without changing the sample distribution, this often leads to significant error reduction. This broadens the applicability of gradient rendering considerably. To gain insight into the conditions under which gradient-domain sampling is beneficial, we present a frequency analysis that compares Monte Carlo sampling of gradients followed by Poisson reconstruction to traditional Monte Carlo sampling. Finally, we describe Gradient-Domain Path Tracing (G-PT), a relatively simple modification of the standard path tracing algorithm that can yield far superior results.

Recent Advances in Adaptive Sampling and Reconstruction for Monte Carlo Rendering

Monte Carlo integration is firmly established as the basis for most practical realistic image synthesis algorithms because of its flexibility and generality. However, the visual quality of rendered images often suffers from estimator variance, which appears as visually distracting noise. Adaptive sampling and reconstruction algorithms reduce variance by controlling the sampling density and aggregating samples in a reconstruction step, possibly over large image regions. In this paper we survey recent advances in this area. We distinguish between “a priori” methods that analyze the light transport equations and derive sampling rates and reconstruction filters from this analysis, and “a posteriori” methods that apply statistical techniques to sets of samples to drive the adaptive sampling and reconstruction process. They typically estimate the errors of several reconstruction filters, and select the best filter locally to minimize error. We discuss advantages and disadvantages of recent state-of-the-art techniques, and provide visual and quantitative comparisons. Some of these techniques are proving useful in real-world applications, and we aim to provide an overview for practitioners and researchers to assess these approaches. In addition, we discuss directions for potential further improvements.

Gradient-Domain Bidirectional Path Tracing

Gradient-domain path tracing has recently been introduced as an efficient realistic image synthesis algorithm. This paper introduces a bidirectional gradient-domain sampler that outperforms traditional bidirectional path tracing often by a factor of two to five in terms of squared error at equal render time. It also improves over unidirectional gradient-domain path tracing in challenging visibility conditions, similarly as conventional bidirectional path tracing improves over its unidirectional counterpart. Our algorithm leverages a novel multiple importance sampling technique and an efficient implementation of a high-quality shift mapping suitable for bidirectional path tracing. We demonstrate the versatility of our approach in several challenging light transport scenarios.