Rostislav Khlebnikov

Crepuscular Rays for Tumor Accessibility Planning

In modern clinical practice, planning access paths to volumetric target structures remains one of the most important and most complex tasks, and a physicians insufficient experience in this can lead to severe complications or even the death of the patient. In this paper, we present a method for safety evaluation and the visualization of access paths to assist physicians during preoperative planning. As a metaphor for our method, we employ a well-known, and thus intuitively perceivable, natural phenomenon that is usually called crepuscular rays. Using this metaphor, we propose several ways to compute the safety of paths from the region of interest to all tumor voxels and show how this information can be visualized in real-time using a multi-volume rendering system. Furthermore, we show how to estimate the extent of connected safe areas to improve common medical 2D multi-planar reconstruction (MPR) views. We evaluate our method by means of expert interviews, an online survey, and a retrospective evaluation of 19 real abdominal radio-frequency ablation (RFA) interventions, with expert decisions serving as a gold standard. The evaluation results show clear evidence that our method can be successfully applied in clinical practice without introducing substantial overhead work for the acting personnel. Finally, we show that our method is not limited to medical applications and that it can also be useful in other fields.

Fast Fully Automatic Segmentation of the Human Placenta from Motion Corrupted MRI

Recently, magnetic resonance imaging has revealed to be important for the evaluation of placenta’s health during pregnancy. Quantitative assessment of the placenta requires a segmentation, which proves to be challenging because of the high variability of its position, orientation, shape and appearance. Moreover, image acquisition is corrupted by motion artifacts from both fetal and maternal movements. In this paper we propose a fully automatic segmentation framework of the placenta from structural T2-weighted scans of the whole uterus, as well as an extension in order to provide an intuitive pre-natal view into this vital organ. We adopt a 3D multi-scale convolutional neural network to automatically identify placental candidate pixels. The resulting classification is subsequently refined by a 3D dense conditional random field, so that a high resolution placental volume can be reconstructed from multiple overlapping stacks of slices. Our segmentation framework has been tested on 66 subjects at gestational ages 20–38 weeks achieving a Dice score of \(71.95\pm 19.79\,\%\) for healthy fetuses with a fixed scan sequence and \(66.89\pm 15.35\,\%\) for a cohort mixed with cases of intrauterine fetal growth restriction using varying scan parameters.

Procedural Texture Synthesis for Zoom-Independent Visualization of Multivariate Data

Simultaneous visualization of multiple continuous data attributes in a single visualization is a task that is important for many application areas. Unsurprisingly, many methods have been proposed to solve this task. However, the behavior of such methods during the exploration stage, when the user tries to understand the data with panning and zooming, has not been given much attention.

Effects of needle placement inaccuracies in hepatic radiofrequency tumor ablation

The correct needle placement is one of the crucial tasks in performing radiofrequency tumor ablation (RFA). In this work we evaluated the effects of imperfect needle placement for RFAs that are performed with an expandable needle array by using a finite-element simulation. We performed simulations for normal liver tissue with hypo- and hyperperfused metastasis as well as for cirrhotic liver tissue with hepatocellular carcinoma (HCC). We found that the shortest distance from tumor to the border of the ablated region is significantly smaller even for just 5mm deviation from the position recommended by the generator manufacturer. In case of hyperperfused metastasis even the tumor itself might stay unablated which means a very high probability of local tumor recurrence. These results provide valuable information on acceptability of inaccurate needle position to the radiologist performing RFA.

Noise-Based Volume Rendering for the Visualization of Multivariate Volumetric Data

Analysis of multivariate data is of great importance in many scientific disciplines. However, visualization of 3D spatially-fixed multivariate volumetric data is a very challenging task. In this paper we present a method that allows simultaneous real-time visualization of multivariate data. We redistribute the opacity within a voxel to improve the readability of the color defined by a regular transfer function, and to maintain the see-through capabilities of volume rendering. We use predictable procedural noise - random-phase Gabor noise - to generate a high-frequency redistribution pattern and construct an opacity mapping function, which allows to partition the available space among the displayed data attributes. This mapping function is appropriately filtered to avoid aliasing, while maintaining transparent regions. We show the usefulness of our approach on various data sets and with different example applications. Furthermore, we evaluate our method by comparing it to other visualization techniques in a controlled user study. Overall, the results of our study indicate that users are much more accurate in determining exact data values with our novel 3D volume visualization method. Significantly lower error rates for reading data values and high subjective ranking of our method imply that it has a high chance of being adopted for the purpose of visualization of multivariate 3D data.

Intervention Planning of Hepatocellular Carcinoma Radio-Frequency Ablations

We present a software solution for planning and simulating radio-frequency ablation (RFA) treatment for patients suffering from hepatocellular carcinoma. Our software provides the graphical front-end for the results of the EU FP7 project IMPPACT. The main planning application was designed to assist with the identification of minimum-risk setups for RFA probes and generation of evaluable 3D representations of the predicted necrosis zones. Patient-specific mesh data describing the involved anatomic structures are used to individually parameterize the simulation procedure for personalized results. Our software supplies tools for extracting the required anatomic meshes from computed tomography (CT) images and for specifying custom intervention protocols. Data collected during simulations allow for detailed illustration of expected effectiveness and progress of heat-induced necrosis over time. Our software was evaluated positively by medical personnel and has been successfully put into practice at two independent European clinical sites.

Multi-GPU Image-based Visual Hull Rendering

Many virtual mirror and telepresence applications require novel viewpoint synthesis with little latency to user motion. Image-based visual hull (IBVH) rendering is capable of rendering arbitrary views from segmented images without an explicit intermediate data representation, such as a mesh or a voxel grid. By computing depth images directly from the silhouette images, it usually outperforms indirect methods. GPU-hardware accelerated implementations exist, but due to the lack of an intermediate representation no multi-GPU parallel strategies and implementations are currently available. This paper suggests three ways to parallelize the IBVH-pipeline and maps them to the sorting classification that is often applied to conventional parallel rendering systems. In addition to sort-first parallelization, we suggest a novel sort-last formulation that regards cameras as scene objects. We enhance this method’s performance by a block-based encoding of the rendering results. For interactive systems with hard real-time constraints, we combine the algorithm with a multi-frame rate (MFR) system. We suggest a combination of forward and backward image warping to improve the visual quality of the MFR rendering. We observed the runtime behavior of the suggested methods and assessed how their performance scales with respect to input and output resolutions and the number of GPUs. By using additional GPUs, we reduced rendering times by up to 60%. Multi-frame rate viewing can even be ten times faster.

Parallel irradiance caching for interactive Monte-Carlo direct volume rendering

We propose a technique to build the irradiance cache for isotropic scattering simultaneously with Monte Carlo progressive direct volume rendering on a single GPU, which allows us to achieve up to four times increased convergence rate for complex scenes with arbitrary sources of light. We use three procedures that run concurrently on a single GPU. The first is the main rendering procedure. The second procedure computes new cache entries, and the third one corrects the errors that may arise after creation of new cache entries. We propose two distinct approaches to allow massive parallelism of cache entry creation. In addition, we show a novel extrapolation approach which outputs high quality irradiance approximations and a suitable prioritization scheme to increase the convergence rate by dedicating more computational power to more complex rendering areas.

Stylization-based ray prioritization for guaranteed frame rates

This paper presents a new method to control graceful scene degradation in complex ray-based rendering environments. It proposes to constrain the image sampling density with object features, which are known to support the comprehension of the three-dimensional shape. The presented method uses Non-Photorealistic Rendering (NPR) techniques to extract features such as silhouettes, suggestive contours, suggestive highlights, ridges and valleys. To map different feature types to sampling densities, we also present an evaluation of the features impact on the resulting image quality. To reconstruct the image from sparse sampling data, we use linear interpolation on an adaptively aligned fractal pattern. With this technique, we are able to present an algorithm that guarantees a desired minimal frame rate without much loss of image quality. Our scheduling algorithm maximizes the use of each given time slice by rendering features in order of their corresponding importance values until a time constraint is reached. We demonstrate how our method can be used to boost and guarantee the rendering time in complex ray-based environments consisting of geometric as well as volumetric data.

CRIMSON: Towards a Software Environment for Patient-Specific Blood Flow Simulation for Diagnosis and Treatment

In this paper, we introduce the new software environment CRIMSON: CardiovasculaR Integrated Modelling and SimulatiON. This software provides a number of tools for medical image data analysis, preprocessing, segmentation and blood flow simulation. In this paper we describe the work flow necessary to perform such tasks as well its implementation in CRIMSON based on multiple well-established open-source libraries, such as MITK and OpenCASCADE. We show that the software is easy to use for both experts and non-experts in the field of hemodynamic modelling. The intuitive and responsive interface of CRIMSON facilitates learning and speeds up the model building process by up to a factor of two compared to the existing tool being used for the same purpose. The overall goal of this work is to produce a feature-rich and intuitive open-source blood flow modelling framework that can be used both by engineers and medical personnel.