Alexandre Ancel

Pre-Integrated Volume Rendering with Non-Linear Gradient Interpolation

Shading is an important feature for the comprehension of volume datasets, but is difficult to implement accurately. Current techniques based on pre-integrated direct volume rendering approximate the volume rendering integral by ignoring non-linear gradient variations between front and back samples, which might result in cumulated shading errors when gradient variations are important and / or when the illumination function features high frequencies. In this paper, we explore a simple approach for pre-integrated volume rendering with non-linear gradient interpolation between front and back samples. We consider that the gradient smoothly varies along a quadratic curve instead of a segment in-between consecutive samples. This not only allows us to compute more accurate shaded pre-integrated look-up tables, but also allows us to more efficiently process shading amplifying effects, based on gradient filtering. An interesting property is that the pre-integration tables we use remain two-dimensional as for usual pre-integrated classification. We conduct experiments using a full hardware approach with the Blinn-Phong illumination model as well as with a non-photorealistic illumination model.

Load-Balanced Multi-GPU Ambient Occlusion for Direct Volume Rendering

Ambient occlusion techniques were introduced to improve data comprehension by bringing soft fading shadows to the visualization of 3D datasets. They consist in attenuating light by considering the occlusion resulting from the presence of neighboring structures. Nevertheless they often come with an important precomputation cost, which prevents their use in interactive applications based on transfer function editing. This paper explores parallel solutions to reach interactive framerates with the use of a multi-GPU setup. Our method distributes the data to the different devices for computation. We use bricking and load balancing to optimize computation time. We also introduce two repartition schemes: a static one, which divides the dataset into as many blocks as there are GPUs and a dynamic one, which divides the dataset into smaller blocks and distributes them using a producerconsumer way. Results, using an 8-GPU architecture, show that we manage to get important speedups compared to a mono-GPU setup.

Feature-driven ambient occlusion for direct volume rendering

Ambient occlusion techniques were introduced to improve data comprehension by bringing soft fading shadows. They consist in attenuating light by considering the occlusion resulting from the presence of neighboring structures. Recently introduced in volume rendering, we show that the straightforward application of ambient occlusion in direct volume rendering has its limits as rendering a multi-layer volume results in overdarkening the internal layers of the volume. This paper proposes to address the overdarkening issue by computing ambient occlusion according to the features present in the dataset. This allows us to neglect inter-occlusions between features without losing the auto-occlusions that give cues on the shape of the considered features. We use a GPU-based approach with bricking to speed up the computations of our ambient occlusion method. Results show that our approach not only improves the visual quality of images compared to classical ambient occlusion, but it is also less parametersensitive, thus furthermore improving usability for every-day users.

From Real MRA to Virtual MRA: Towards an Open-Source Framework

Angiographic imaging is a crucial domain of medical imaging. In particular , Magnetic Resonance Angiography (MRA) is used for both clinical and research purposes. This article presents the first framework geared toward the design of virtual MRA images from real MRA images. It relies on a pipeline that involves image processing, vascular modeling, computational fluid dynamics and MR image simulation, with several purposes. It aims to provide to the whole scientific community (1) software tools for MRA analysis and blood flow simulation ; and (2) data (computational meshes, virtual MRAs with associated ground truth), in an open-source / open-data paradigm. Beyond these purposes, it constitutes a versatile tool for progressing in the understanding of vascular networks, especially in the brain, and the associated imaging technologies.