Multiple-volume visualization is a growing field in medical imaging providing simultaneous exploration of volumes acquired from varying modalities. However, high complexity results in an increased strain on performance compared to single volume rendering as scenes may consist of volumes with arbitrary orientations and rendering is performed with varying sample densities. Expensive image order techniques such as depth peeling have previously been used to perform the necessary calculations. In. this work we present a view-independent region based scene description for multi-volume pipelines. Using Binary Space Partitioning we are able to create a simple interface providing all required information for advanced multi-volume renderings while introducing a minimal overhead for scenes with few volumes. The modularity of our solution is demonstrated by the use of visual development and performance is documented with benchmarks and real-time simulations.

In many cases, only the combination of geometric and volumetric data sets is able to describe a single phenomenon under observation when visualizing large and complex data. When semi-transparent geometry is present, correct rendering results require sorting of transparent structures. Additional complexity is introduced as the contributions from volumetric data have to be partitioned according to the geometric objects in the scene. The A-buffer, an enhanced framebuffer with additional per-pixel information, has previously been introduced to deal with the complexity caused by transparent objects. In this paper, we present an optimized rendering algorithm for hybrid volume-geometry data based on the A-buffer concept. We propose two novel components for modern GPUs that tailor memory utilization to the depth complexity of individual pixels. The proposed components are compatible with modern A-buffer implementations and yield performance gains of up to eight times compared to existing approaches through reduced allocation and reuse of fast cache memory. We demonstrate the applicability of our approach and its performance with several examples from molecular biology, space weather, and medical visualization containing both, volumetric data and geometric structures.

Computed Tomography Angiography (CTA) is commonly used in clinical routine for diagnosing vascular diseases. The procedure involves the injection of a contrast agent into the blood stream to increase the contrast between the blood vessels and the surrounding tissue in the image data. CTA is often visualized with Direct Volume Rendering (DVR) where the enhanced image contrast is important for the construction of Transfer Functions (TFs). For increased efficiency, clinical routine heavily relies on preset TFs to simplify the creation of such visualizations for a physician. In practice, however, TF presets often do not yield optimal images due to variations in mixture concentration of contrast agent in the blood stream. In this paper we propose an automatic, optimization- based method that shifts TF presets to account for general deviations and local variations of the intensity of contrast enhanced blood vessels. Some of the advantages of this method are the following. It computationally automates large parts of a process that is currently performed manually. It performs the TF shift locally and can thus optimize larger portions of the image than is possible with manual interaction. The method is based on a well known vesselness descriptor in the definition of the optimization criterion. The performance of the method is illustrated by clinically relevant CT angiography datasets displaying both improved structural overviews of vessel trees and improved adaption to local variations of contrast concentration. 

Visualization of contrast enhanced blood vessels in CT angiography data presents a challenge due to varying concentration of the contrast agent. The purpose of this work is to evaluate the correctness (effectiveness) in visualizing the vessel lumen using two different 3D visualization strategies, thereby assessing the feasibility of using such visualizations for diagnostic decisions. We compare a standard visualization approach with a recent method which locally adapts to the contrast agent concentration. Both methods are evaluated in a parallel setting where the participant is instructed to produce a complete visualization of the vessel lumen, including both large and small vessels, in cases of calcified vessels in the legs. The resulting visualizations are thereafter compared in a slice viewer to assess the correctness of the visualized lumen. The results indicate that the participants generally overestimated the size of the vessel lumen using the standard visualization, whereas the locally adaptive method better conveyed the true anatomy. The participants did find the interpretation of the locally adaptive method to be less intuitive, but also noted that this did not introduce any prohibitive complexity in the work flow. The observed trends indicate that the visualized lumen strongly depends on the width and placement of the applied transfer function and that this dependency is inherently local rather than global. We conclude that methods that permit local adjustments, such as the method investigated in this study, can be beneficial to certain types of visualizations of large vascular trees

The purpose of this work is to investigate, and improve, the feasibility of advanced Region Of Interest (ROI) selection schemes in clinical volume rendering. In particular, this work implements and evaluates an Automated Anatomical ROI (AA-ROI) approach based on the combination of automatic image registration (AIR) and Distance-Based Transfer Functions (DBTFs), designed for automatic selection of complex anatomical shapes without relying on prohibitive amounts of interaction. Domain knowledge and clinical experience has been included in the project through participation of practicing radiologists in all phases of the project. This has resulted in a set of requirements that are critical for Direct Volume Rendering applications to be utilized in clinical practice and a prototype AA-ROI implementation that was developed to addresses critical points in existing solutions. The feasibility of the developed approach was assessed through a study where five radiologists investigated three medical data sets with complex ROIs, using both traditional tools and the developed prototype software. Our analysis indicate that advanced, registration based ROI schemes could increase clinical efficiency in time-critical settings for cases with complex ROIs, but also that their clinical feasibility is conditional with respect to the radiologists trust in the registration process and its application to the data.

In many applications of Direct Volume Rendering (DVR) the importance of a certain material or feature is highly dependent on its relative spatial location. For instance, in the medical diagnostic procedure, the patients symptoms often lead to specification of features, tissues and organs of particular interest. One such example is pockets of gas which, if found inside the body at abnormal locations, are a crucial part of a diagnostic visualization. This paper presents an approach that enhances DVR transfer function design with spatial localization based on user specified material dependencies. Semantic expressions are used to define conditions based on relations between different materials, such as only render iodine uptake when close to liver. The underlying methods rely on estimations of material distributions which are acquired by weighing local neighborhoods of the data against approximations of material likelihood functions. This information is encoded and used to influence rendering according to the users specifications. The result is improved focus on important features by allowing the user to suppress spatially less-important data. In line with requirements from actual clinical DVR practice, the methods do not require explicit material segmentation that would be impossible or prohibitively time-consuming to achieve in most real cases. The scheme scales well to higher dimensions which accounts for multi-dimensional transfer functions and multivariate data. Dual-Energy Computed Tomography, an important new modality in radiology, is used to demonstrate this scalability. In several examples we show significantly improved focus on clinically important aspects in the rendered images.

In visualization, the combined role of data reconstruction and its classification plays a crucial role. In this paper we propose a novel approach that improves classification of different materials and their boundaries by combining information from the classifiers at the reconstruction stage. Our approach estimates the targeted materials’ local support before performing multiple material-specific reconstructions that prevent much of the misclassification traditionally associated with transitional regions and transfer function (TF) design. With respect to previously published methods our approach offers a number of improvements and advantages. For one, it does not rely on TFs acting on derivative expressions, therefore it is less sensitive to noisy data and the classification of a single material does not depend on specialized TF widgets or specifying regions in a multidimensional TF. Additionally, improved classification is attained without increasing TF dimensionality, which promotes scalability to multivariate data. These aspects are also key in maintaining low interaction complexity. The results are simple-to-achieve visualizations that better comply with the user’s understanding of discrete features within the studied object.