Volume ray casting | EPFL Graph Search

Volume ray casting, sometimes called volumetric ray casting, volumetric ray tracing, or volume ray marching, is an volume rendering technique. It computes 2D images from 3D volumetric data sets (3D scalar fields). Volume ray casting, which processes volume data, must not be mistaken with ray casting in the sense used in ray tracing, which processes surface data. In the volumetric variant, the computation doesn't stop at the surface but "pushes through" the object, sampling the object along the ray. Unlike ray tracing, volume ray casting does not spawn secondary rays. When the context/application is clear, some authors simply call it ray casting. Because ray marching does not necessarily require an exact solution to ray intersection and collisions, it is suitable for real time computing for many applications for which ray tracing is unsuitable. Classification The technique of volume ray casting can be derived directly from the rendering equation. It provides

Evaluation of the ray-casting analytical algorithm for pencil beam scanning proton therapy

Sojin Shim, Ye Zhang

For pencil beam scanned (PBS) proton therapy, analytical dose calculation engines are still typically used for the optimisation process, and often for the final evaluation of the plan. Recently however, the suitability of analytical calculations for planning PBS treatments has been questioned. Conceptually, the two main approaches for these analytical dose calculations are the ray-casting (RC) and the pencil-beam (PB) method. In this study, we compare dose distributions and dosimetric indices, calculated on both the clinical dose calculation grid and as a function of dose grid resolution, to Monte Carlo (MC) calculations. The analysis is done using a comprehensive set of clinical plans which represent a wide choice of treatment sites. When analysing dose difference histograms for relative treatment plans, pencil beam calculations with double grid resolution perform best, with on average 97.7%/91.9% (RC), 97.9%/92.7% (RC, double grid resolution), 97.6%/91.0% (PB) and 98.6%/94.0% (PB, double grid resolution) of voxels agreeing within +/- 5%/+/- 3% between the analytical and the MC calculations. Even though these point-to-point dose comparison shows differences between analytical and MC calculations, for all algorithms, clinically relevant dosimetric indices agree within +/- 4% for the PTV and within +/- 5% for critical organs. While the clinical agreement depends on the treatment site, there is no substantial difference of indices between the different algorithms. The pencil-beam approach however comes at a higher computational cost than the ray-casting calculation. In conclusion, we would recommend using the ray-casting algorithm for fast dose optimization and subsequently combine it with one MC calculation to scale the absolute dose and assure the quality of the treatment plan.

2019

A Non-Exponential Transmittance Model for Volumetric Scene Representations

Wenzel Alban Jakob, Delio Aleardo Vicini

We introduce a novel transmittance model to improve the volumetric representation of 3D scenes. The model can represent opaque surfaces in the volumetric light transport framework. Volumetric representations are useful for complex scenes, and become increasingly popular for level of detail and scene reconstruction. The traditional exponential transmittance model found in volumetric light transport cannot capture correlations in visibility across volume elements. When representing opaque surfaces as volumetric density, this leads to both bloating of silhouettes and light leaking artifacts. By introducing a parametric non-exponential transmittance model, we are able to approximate these correlation effects and significantly improve the accuracy of volumetric appearance representation of opaque scenes. Our parametric transmittance model can represent a continuum between the linear transmittance that opaque surfaces exhibit and the traditional exponential transmittance encountered in participating media and unstructured geometries. This covers a large part of the spectrum of geometric structures encountered in complex scenes. In order to handle the spatially varying transmittance correlation effects, we further extend the theory of non-exponential participating media to a heterogeneous transmittance model. Our model is compact in storage and computationally efficient both for evaluation and for reverse-mode gradient computation. Applying our model to optimization algorithms yields significant improvements in volumetric scene appearance quality. We further show improvements for relevant applications, such as scene appearance prefiltering, image-based scene reconstruction using differentiable rendering, neural representations, and compare it to a conventional exponential model.

ASSOC COMPUTING MACHINERY2021

Holographic Visualisation and Interaction of Fused CT, PET and MRI Volumetric Medical Imaging Data Using Dedicated Remote GPGPU Ray Casting

Adrien Raphaël Depeursinge, Stéphane Gobron, Patric Hagmann

Medical experts commonly use imaging including Computed Tomography (CT), Positron-Emission Tomography (PET) and Magnetic Resonance Imaging (MRI) for diagnosis or to plan a surgery. These scans give a highly detailed representation of the patient anatomy, but the usual Three-Dimensional (3D) separate visualisations on screens does not provide an convenient and performant understanding of the real anatomical complexity. This paper presents a computer architecture allowing medical staff to visualise and interact in real-time holographic fused CT, PET, MRI of patients. A dedicated workstation with a wireless connection enables real-time General-Purpose Processing on Graphics Processing Units (GPGPU) ray casting computation through the mixed reality (MR) headset. The hologram can be manipulated with hand gestures and voice commands through the following interaction features: instantaneous visualisation and manipulation of 3D scans with a frame rate of 30 fps and a delay lower than 120 ms. These performances give a seamless interactive experience for the user [10].

SPRINGER INTERNATIONAL PUBLISHING AG2018