<p>Technologies in image acquisition have developed and often provide image volumes in more than two dimensions. Computer tomography and magnet resonance imaging provide image volumes in three spatial dimensions. The image enhancement methods have developed as well and in this thesis work 3D image enhancement with filter networks is evaluated.</p><p>The aims of this work are; to find a method which makes the initial parameter settings in the 3D image enhancement processing easier, to compare 2D and 3D processed image volumes visualized with different visualization techniques and to give an illustration of the benefits with 3D image enhancement processing visualized using these techniques.</p><p>The results of this work are;</p><p>1. a parameter setting tool that makes the initial parameter setting much easier and</p><p>2. an evaluation of 3D image enhancement with filter networks that shows a significant enhanced image quality in 3D processed image volumes with a high noise level compared to the 2D processed volumes. These results are shown in slices, MIP and volume rendering. The differences are even more pronounced if the volume is presented in a different projection than the volume is 2D processed in.</p>

Medical imaging plays an increasingly important role in modern healthcare. In medical imaging, it is often relevant to relate different images to each other, something which can prove challenging, since there rarely exists a pre-defined mapping between the pixels in different images. Hence, there is a need to find such a mapping/transformation, a procedure known as image registration. Over the years, image registration has been proved useful in a number of clinical situations. Despite this, current use of image registration in clinical practice is rather limited, typically only used for image fusion. The limited use is, to a large extent, caused by excessive computation times, lack of established validation methods/metrics and a general skepticism toward the trustworthiness of the estimated transformations in deformable image registration. This thesis aims to overcome some of the issues limiting the use of image registration, by proposing a set of technical contributions and two clinical applications targeted at improved clinical efficiency. The contributions are made in the context of a generic framework for non-parametric image registration and using an image registration method known as the Morphon.  In image registration, regularization of the estimated transformation forms an integral part in controlling the registration process, and in this thesis, two regularizers are proposed and their applicability demonstrated. Although the regularizers are similar in that they rely on local structure analysis, they differ in regard to implementation, where one is implemented as applying a set of filter kernels, and where the other is implemented as solving a global optimization problem. Furthermore, it is proposed to use a set of quadrature filters with parallel scales when estimating the phase-difference, driving the registration. A proposal that brings both accuracy and robustness to the registration process, as shown on a set of challenging image sequences. Computational complexity, in general, is addressed by porting the employed Morphon algorithm to the GPU, by which a performance improvement of 38-44x is achieved, when compared to a single-threaded CPU implementation. The suggested clinical applications are based upon the concept paint on priors, which was formulated in conjunction with the initial presentation of the Morphon, and which denotes the notion of assigning a model a set of properties (local operators), guiding the registration process. In this thesis, this is taken one step further, in which properties of a model are assigned to the patient data after completed registration. Based upon this, an application using the concept of anatomical transfer functions is presented, in which different organs can be visualized with separate transfer functions. This has been implemented for both 2D slice visualization and 3D volume rendering. A second application is proposed, in which landmarks, relevant for determining various measures describing the anatomy, are transferred to the patient data. In particular, this is applied to idiopathic scoliosis and used to obtain various measures relevant for assessing spinal deformity. In addition, a data analysis scheme is proposed, useful for quantifying the linear dependence between the different measures used to describe spinal deformities.

Technologies in image acquisition have developed and often provide image volumes in more than two dimensions. Computer tomography and magnet resonance imaging provide image volumes in three spatial dimensions. The image enhancement methods have developed as well and in this thesis work 3D image enhancement with filter networks is evaluated. The aims of this work are; to find a method which makes the initial parameter settings in the 3D image enhancement processing easier, to compare 2D and 3D processed image volumes visualized with different visualization techniques and to give an illustration of the benefits with 3D image enhancement processing visualized using these techniques. The results of this work are; 1. a parameter setting tool that makes the initial parameter setting much easier and 2. an evaluation of 3D image enhancement with filter networks that shows a significant enhanced image quality in 3D processed image volumes with a high noise level compared to the 2D processed volumes. These results are shown in slices, MIP and volume rendering. The differences are even more pronounced if the volume is presented in a different projection than the volume is 2D processed in.

<p>Volume rendering techniques can be powerful tools when visualizing medical data sets. The characteristics of being able to capture 3-D internal structures make the technique attractive. Scanning equipment is producing medical images, with rapidly increasing resolution, resulting in heavily increased size of the data set. Despite the great amount of processing power CPUs deliver, the required precision in image quality can be hard to obtain in real-time rendering. Therefore, it is highly desirable to optimize the rendering process.</p><p>Modern GPUs possess much more computational power and is available for general purpose programming through high level shading languages. Efficient representations of the data are crucial due to the limited memory provided by the GPU. This thesis describes the theoretical background and the implementation of an approach presented by Patric Ljung, Claes Lundström and Anders Ynnerman at Linköping University. The main objective is to implement a fully working multi-resolution framework with two separate pipelines for pre-processing and real-time rendering, which uses the GPU to visualize large medical data sets.</p>

<p>Volume rendering applications are used in medical imaging in order to facilitate the analysis of three-dimensional image data. This study focuses on how to improve the usability of graphical user interfaces of these systems, by gathering user requirements. This is achieved by evaluations of existing systems, together with interviews and observations at clinics in Sweden that use volume rendering to some extent. The usability of the applications of today is not sufficient, according to the users participating in this study. This is due to a wide range of reasons. One reason is that the graphical user interface is not intuitive. Another reason is that the users do not rely on the technique to produce sufficient results that can be used in the diagnostic process. The issue of user confidence is mainly due to the problem of the generation and user control of the transfer functions used in volume rendering. </p><p>Based on the results of the evaluation a graphical user interface, including the most important and frequently used functions, is designed. A suggestion for how the transfer function can be generated is presented.</p>

<p>To efficiently examine volumetric data sets from CT or MRI scans good volume rendering applications are needed. This thesis describes the design and implementation of an application programming interface (API) to be used when developing volume-rendering applications. A complete application programming interface has been designed. The interface is designed so that it makes writing application programs containing volume rendering fast and easy. The interface also makes created application programs hardware independent. Volume rendering using 3d-textures is implemented on Windows and Unix platforms. Rendering performance has been compared between different graphics hardware.</p>

<p>Visualization of large scale time-varying volumetric datasets is an active topic of research. Technical limitations in terms of bandwidth and memory usage become a problem when visualizing these datasets on commodity computers at interactive frame rates. The overall objective is to overcome these limitations by adapting the methods of an existing Direct Volume Rendering pipeline. The objective is considered to be a proof of concept to assess the feasibility of visualizing large scale time-varying datasets using this pipeline. The pipeline consists of components from previous research, which make extensive use of graphics hardware to visualize large scale static data on commodity computers.</p><p>This report presents a diploma work, which adapts the pipeline to visualize flow features concealed inside the large scale Computational Fluid Dynamics dataset. The work provides a foundation to address the technical limitations of the commodity computer to visualize time-varying datasets. The report describes the components making up the Direct Volume Rendering pipeline together with the adaptations. It also briefly describes the Computational Fluid Dynamics simulation, the flow features and an earlier visualization approach to show the system’s limitations when exploring the dataset.</p>

Volume rendering techniques can be powerful tools when visualizing medical data sets. The characteristics of being able to capture 3-D internal structures make the technique attractive. Scanning equipment is producing medical images, with rapidly increasing resolution, resulting in heavily increased size of the data set. Despite the great amount of processing power CPUs deliver, the required precision in image quality can be hard to obtain in real-time rendering. Therefore, it is highly desirable to optimize the rendering process. Modern GPUs possess much more computational power and is available for general purpose programming through high level shading languages. Efficient representations of the data are crucial due to the limited memory provided by the GPU. This thesis describes the theoretical background and the implementation of an approach presented by Patric Ljung, Claes Lundström and Anders Ynnerman at Linköping University. The main objective is to implement a fully working multi-resolution framework with two separate pipelines for pre-processing and real-time rendering, which uses the GPU to visualize large medical data sets.

Visualization of large scale time-varying volumetric datasets is an active topic of research. Technical limitations in terms of bandwidth and memory usage become a problem when visualizing these datasets on commodity computers at interactive frame rates. The overall objective is to overcome these limitations by adapting the methods of an existing Direct Volume Rendering pipeline. The objective is considered to be a proof of concept to assess the feasibility of visualizing large scale time-varying datasets using this pipeline. The pipeline consists of components from previous research, which make extensive use of graphics hardware to visualize large scale static data on commodity computers. This report presents a diploma work, which adapts the pipeline to visualize flow features concealed inside the large scale Computational Fluid Dynamics dataset. The work provides a foundation to address the technical limitations of the commodity computer to visualize time-varying datasets. The report describes the components making up the Direct Volume Rendering pipeline together with the adaptations. It also briefly describes the Computational Fluid Dynamics simulation, the flow features and an earlier visualization approach to show the system’s limitations when exploring the dataset.

This thesis proposes an efficient method to accelerate ray based volume rendering with texture slabs using programmable graphics hardware. In this method, empty space skipping and early ray termination are utilized without performing any preprocessing on CPU side. The acceleration structure is created on the fly by making use of depth buffer efficiently on Graphics Processing Unit (GPU) side. In the proposed method, texture slices are grouped together to form a texture slab. Rendering all the slabs from front to back viewing order in multiple rendering passes generates the resulting volume image. Slab silhouette maps (SSM) are created to identify and skip empty spaces along the ray direction at pixel level. These maps are created from the alpha component of the slab and stored in the depth buffer. In addition to the empty region information, SSM also contains information about the terminated rays. The method relies on hardware z-occlusion culling that is realized by means of SSMs to accelerate ray traversals. The cost of generating this acceleration data structure is very small compared to the total rendering time.