Milán Magdics

Free Path Sampling in High Resolution Inhomogeneous Participating Media

This paper presents efficient algorithms for free path sampling in heterogeneous participating media defined either by high‐resolution voxel arrays or generated procedurally. The method is based on the concept of mixing ‘virtual’ material or particles to the medium, augmenting the extinction coefficient to a function for which the free path can be sampled in a straightforward way. The virtual material is selected such that it modifies the volume density but does not alter the radiance. We define the total extinction coefficient of the real and virtual particles by a low‐resolution grid of super‐voxels that are much larger than the real voxels defining the medium. The computational complexity of the proposed method depends just on the resolution of the super‐voxel grid and does not grow with the resolution above the scale of super‐voxels. The method is particularly efficient to render large, low‐density, heterogeneous volumes, which should otherwise be defined by enormously high resolution voxel grids and where the average free path length would cross many voxels.

A Kinect-Based System for Cardiopulmonary Resuscitation Simulation: A Pilot Study

Cardiopulmonary Resuscitation (CPR) training is a crucial procedure to reduce the decease from cardiac arrest in pre–hospital situation. Due to the importance of CPR its knowledge is required not only by professions prescribing CPR certification such as fire fighter, life guard, police or daycare, but also by laypersons. To learn CPR skill, practice is highly recommended and 3D simulators with effective interaction tools are one of the best options to practice CPR anywhere and anytime. In this paper, we present a pilot study in developing a Kinect-based system focusing on two key parameters of the CPR procedure: the chest compression rate and correct arm pose, implemented in our existing CPR training system, LIfe Support Simulation Application (LISSA). Our system falls into the category of markerless tracking using commercial depth–cameras, making the proposed method flexible and economic. We also present a comparison with different CPR feedback systems with regard to the chest compression rate and correct arm pose.

Performance evaluation of scatter modeling of the GPU-based “Tera-Tomo” 3D PET reconstruction

In positron emission tomography (PET), photon scattering inside the body causes significant blurring and quantification error in the reconstructed images. To solve this problem we have developed Monte Carlo (MC) based 3D PET reconstruction algorithms implemented on the Graphics Processing Unit (GPU). Our implementation takes multiple Compton scattering into account without any significant additional cost. The performance of the scatter correction is evaluated using GATE simulation as well as by comparing reconstruction results of Tera-Tomo to the reference reconstruction implementation of the Philips Gemini TOF PET which applies attenuation correction and single scatter simulation (SSS) for scatter correction. The comparative reconstruction results are based on the NEMA NU2-2007 image quality phantom.

Real-time generation of L-system scene models for rendering and interaction

We present a framework for generating procedural geometry described by a context-free, parametric L-system completely on the GPU in real-time. These formal grammars allow us to easily describe various types of complex objects, such as vegetation or buildings, in a great diversity. We can create large and complex parts of the scene on-the-fly, which enables us to create a potentially infinite world of such objects. To make modeling easier, we show how the grammar description can be transformed automatically to a shader code that evaluates the L-system on the GPU. Additionally, to allow interaction with the procedural geometry, we propose an algorithm to efficiently perform discrete collision detection with the procedural scene for a large number of objects.

Scatter estimation for PET reconstruction

This paper presents a Monte Carlo scatter estimation algorithm for Positron Emission Tomography (PET) where positron-electron annihilations induce photon pairs that fly independently in the medium and eventually get absorbed in the detector grid. The path of the photon pair will be a polyline defined by the detector hits and scattering points where one of the photons changed its direction. The values measured by detector pairs will then be the total contribution, i.e. the integral of such polyline paths of arbitrary length. This integral is evaluated with Monte Carlo quadrature, using a sampling strategy that is appropriate for the graphics processing unit (GPU) that executes the process. We consider the contribution of photon paths to each pair of detectors as an integral over the Cartesian product set of the volume. This integration domain is sampled globally, i.e. a single polyline will represent all annihilation events occurred in any of its points. Furthermore, line segments containing scattering points will be reused for all detector pairs, which allows us to significantly reduce the number of samples. The scatter estimation is incorporated into a PET reconstruction algorithm where the scattered term is subtracted from the measurements.

Post-processing NPR effects for video games

This paper describes different interactive, non photorealistic techniques which can be easily applied to video games. Based on a study of art and games, an emphasis is put on considering NPR tools as basic elements to provide different styles and moods, to convey different emotional and experiential representations of a game. We restrict ourselves to screen based effects, which permits any existing game to use our framework with practically no integration cost. This allows us not only to comply with user preferences in rendering style, but also the creation of multiple gaming experiences out of the same game. We show the resulting effects in an in-house videogame and in standard Unity demos, and show how the users can change the style of the videogame by means of a menu.

Averaging and Metropolis Iterations For Positron Emission Tomography

Iterative positron emission tomography (PET) reconstruction computes projections between the voxel space and the lines of response (LOR) space, which are mathematically equivalent to the evaluation of multi-dimensional integrals. The dimension of the integration domain can be very high if scattering needs to be compensated. Monte Carlo (MC) quadrature is a straightforward method to approximate high-dimensional integrals. As the numbers of voxels and LORs can be in the order of hundred millions and the projection also depends on the measured object, the quadratures cannot be precomputed, but Monte Carlo simulation should take place on-the-fly during the iterative reconstruction process. This paper presents modifications of the maximum likelihood, expectation maximization (ML-EM) iteration scheme to reduce the reconstruction error due to the on-the-fly MC approximations of forward and back projections. If the MC sample locations are the same in every iteration step of the ML-EM scheme, then the approximation error will lead to a modified reconstruction result. However, when random estimates are statistically independent in different iteration steps, then the iteration may either diverge or fluctuate around the solution. Our goal is to increase the accuracy and the stability of the iterative solution while keeping the number of random samples and therefore the reconstruction time low. We first analyze the error behavior of ML-EM iteration with on-the-fly MC projections, then propose two solutions: averaging iteration and Metropolis iteration. Averaging iteration averages forward projection estimates during the iteration sequence. Metropolis iteration rejects those forward projection estimates that would compromise the reconstruction and also guarantees the unbiasedness of the tracer density estimate. We demonstrate that these techniques allow a significant reduction of the required number of samples and thus the reconstruction time. The proposed methods are built into the Teratomo system.

Gamma photon transport on the GPU for PET

This paper proposes a Monte Carlo algorithm for gamma-photon transport, that partially reuses random paths and is appropriate for parallel GPU implementation According to the requirements of the application of the simulation results in reconstruction algorithms, the method aims at similar relative rather than absolute errors of the detectors The resulting algorithm is SIMD-like, which is a requirement of efficient GPU implementation, i.e all random paths are built with the same sequence of instructions, thus can be simulated on parallel threads that practically have no conditional branches The algorithm is a combined method that separates the low-dimensional part that cannot be well mimicked by importance sampling and computes it by a deterministic quadrature, while the high-dimensional part that is made low-variation by importance sampling is handled by the Monte Carlo method The deterministic quadrature is based on a geometric interpretation of a direct, i.e non-scattered effect of a photon on all detectors.

Unbiased Light Transport Estimators for Inhomogeneous Participating Media

This paper presents a new stochastic particle model for efficient and unbiased Monte Carlo rendering of heterogeneous participating media. We randomly add and remove material particles to obtain a density with which free flight sampling and transmittance estimation are simple, while material particle properties are simultaneously modified to maintain the true expectation of the radiance. We show that meeting this requirement may need the introduction of light particles with negative energy and materials with negative extinction, and provide an intuitive interpretation for such phenomena. Unlike previous unbiased methods, the proposed approach does not require a‐priori knowledge of the maximum medium density that is typically difficult to obtain for procedural models. However, the method can benefit from an approximate knowledge of the density, which can usually be acquired on‐the‐fly at little extra cost and can greatly reduce the variance of the proposed estimators. The introduced mechanism can be integrated in participating media renderers where transmittance estimation and free flight sampling are building blocks. We demonstrate its application in a multiple scattering particle tracer, in transmittance computation, and in the estimation of the inhomogeneous air‐light integral.

Volume enhancement with externally controlled anisotropic diffusion

This paper proposes a method to enhance volumetric data using anisotropic diffusion controlled by another voxel array representing the same object with different physical quantities. The main application of this approach is to enhance volumetric functional data (obtained e.g. with PET or SPECT) based on anatomic (e.g. CT or MRI) information. Enhancement includes noise removal, sharpening and resolution upsampling. As different modalities measure different physical quantities that may or may not be correlated, enhancement must be carefully designed not to introduce spurious features that are present only in one modality. Forward diffusion working with non-negative diffusivity guarantees this kind of causality but also limits the potential of enhancement. To allow the preservation or even the increase of the dynamic range, diffusion should also go backwards. Therefore, we propose a forward–backward diffusion scheme for the enhancement where stability and the avoidance of spurious features are provided by the automatic determination of parameters controlling the diffusion process.