Should the standard dimethyl sulfoxide concentration be reduced? Results of a European Group for Blood and Marrow Transplantation prospective noninterventional study on usage and side effects of dimethyl sulfoxide.

BACKGROUND Dimethyl sulfoxide (DMSO) is essential for the preservation of liquid nitrogen-frozen stem cells, but is associated with toxicity in the transplant recipient. STUDY DESIGN AND METHODS In this prospective noninterventional study, we describe the use of DMSO in 64 European Blood and Marrow Transplant Group centers undertaking autologous transplantation on patients with myeloma and lymphoma and analyze side effects after return of DMSO-preserved stem cells. RESULTS While the majority of centers continue to use 10% DMSO, a significant proportion either use lower concentrations, mostly 5 or 7.5%, or wash cells before infusion (some for selected patients only). In contrast, the median dose of DMSO given (20 mL) was much less than the upper limit set by the same institutions (70 mL). In an accompanying statistical analysis of side effects noted after return of DMSO-preserved stem cells, we show that patients in the highest quartile receiving DMSO (mL and mL/kg body weight) had significantly more side effects attributed to DMSO, although this effect was not observed if DMSO was calculated as mL/min. Dividing the myeloma and lymphoma patients each into two equal groups by age we were able to confirm this result in all but young myeloma patients in whom an inversion of the odds ratio was seen, possibly related to the higher dose of melphalan received by young myeloma patients. CONCLUSION We suggest better standardization of preservation method with reduced DMSO concentration and attention to the dose of DMSO received by patients could help reduce the toxicity and morbidity of the transplant procedure.

Improved sampling for gradient-domain metropolis light transport

We present a generalized framework for gradient-domain Metropolis rendering, and introduce three techniques to reduce sampling artifacts and variance. The first one is a heuristic weighting strategy that combines several sampling techniques to avoid outliers. The second one is an improved mapping to generate offset paths required for computing gradients. Here we leverage the properties of manifold walks in path space to cancel out singularities. Finally, the third technique introduces generalized screen space gradient kernels. This approach aligns the gradient kernels with image structures such as texture edges and geometric discontinuities to obtain sparser gradients than with the conventional gradient kernel. We implement our framework on top of an existing Metropolis sampler, and we demonstrate significant improvements in visual and numerical quality of our results compared to previous work.

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.