Feature-driven Volume Visualization of Medical Imaging Data

Direct volume rendering (DVR) is a volume visualization technique that has been proved to be a very powerful tool in many scientific visualization domains. Diagnostic medical imaging is one such domain in which DVR provides new capabilities for the analysis of complex cases and improves the efficiency of image interpretation workflows. However, the full potential of DVR in the medical domain has not yet been realized. A major obstacle for a better integration of DVR in the medical domain is the time-consuming process to optimize the rendering parameters that are needed to generate diagnostically relevant visualizations in which the important features that are hidden in image volumes are clearly displayed, such as shape and spatial localization of tumors, its relationship with adjacent structures, and temporal changes in the tumors. In current workflows, clinicians must manually specify the transfer function (TF), view-point (camera), clipping planes, and other visual parameters. Another obstacle for the adoption of DVR to the medical domain is the ever increasing volume of imaging data. The advancement of imaging acquisition techniques has led to a rapid expansion in the size of the data, in the forms of higher resolutions, temporal imaging acquisition to track treatment responses over time, and an increase in the number of imaging modalities that are used for a single procedure. The manual specification of the rendering parameters under these circumstances is very challenging. This thesis proposes a set of innovative methods that visualize important features in multi-dimensional and multi-modality medical images by automatically or semi-automatically optimizing the rendering parameters. Our methods enable visualizations necessary for the diagnostic procedure in which 2D slice of interest (SOI) can be augmented with 3D anatomical contextual information to provide accurate spatial localization of 2D features in the SOI; the rendering parameters are automatically computed to guarantee the visibility of 3D features; and changes in 3D features can be tracked in temporal data under the constraint of consistent contextual information. We also present a method for the efficient computation of visibility histograms (VHs) using adaptive binning, which allows our optimal DVR to be automated and visualized in real-time. We evaluated our methods by producing visualizations for a variety of clinically relevant scenarios and imaging data sets. We also examined the computational performance of our methods for these scenarios

Feature-driven Volume Visualization of Medical Imaging Data

Direct volume rendering (DVR) is a volume visualization technique that has been proved to be a very powerful tool in many scientific visualization domains. Diagnostic medical imaging is one such domain in which DVR provides new capabilities for the analysis of complex cases and improves the efficiency of image interpretation workflows. However, the full potential of DVR in the medical domain has not yet been realized. A major obstacle for a better integration of DVR in the medical domain is the time-consuming process to optimize the rendering parameters that are needed to generate diagnostically relevant visualizations in which the important features that are hidden in image volumes are clearly displayed, such as shape and spatial localization of tumors, its relationship with adjacent structures, and temporal changes in the tumors. In current workflows, clinicians must manually specify the transfer function (TF), view-point (camera), clipping planes, and other visual parameters. Another obstacle for the adoption of DVR to the medical domain is the ever increasing volume of imaging data. The advancement of imaging acquisition techniques has led to a rapid expansion in the size of the data, in the forms of higher resolutions, temporal imaging acquisition to track treatment responses over time, and an increase in the number of imaging modalities that are used for a single procedure. The manual specification of the rendering parameters under these circumstances is very challenging. This thesis proposes a set of innovative methods that visualize important features in multi-dimensional and multi-modality medical images by automatically or semi-automatically optimizing the rendering parameters. Our methods enable visualizations necessary for the diagnostic procedure in which 2D slice of interest (SOI) can be augmented with 3D anatomical contextual information to provide accurate spatial localization of 2D features in the SOI; the rendering parameters are automatically computed to guarantee the visibility of 3D features; and changes in 3D features can be tracked in temporal data under the constraint of consistent contextual information. We also present a method for the efficient computation of visibility histograms (VHs) using adaptive binning, which allows our optimal DVR to be automated and visualized in real-time. We evaluated our methods by producing visualizations for a variety of clinically relevant scenarios and imaging data sets. We also examined the computational performance of our methods for these scenarios

Parallel Processing in Web-Based Interactive Echocardiography Simulators

Medical simulation is a new method of education in medicine. It allows training medical students or practitioners without the need to involve patients and makes them familiar with various kinds of examinations, especially related to medical imaging. Simulators that visualize examinations or operations require large computing power to keep time constraints of output presentation. A common approach to this problem is to use graphics processing units (GPU), but the code is not portable. The method of parallelization of processing is more important in component environments, to allow calculating projections in real time. In this paper parallelization issues in the ultrasound view simulation based on provided computer tomography images are analyzed. The proposed domain decomposition for this problem leads to significant reduction in simulation time and allows obtaining an animated visualization for currently available personal computers with multicore processors. The use of a component environment makes the solution portable and makes it possible to implement a web-based application that is the basis for eTraining. The method for creating animation in real time for such solutions is also analyzed

Development of a pulmonary imaging biomarker pipeline for phenotyping of chronic lung disease

We designed and generated pulmonary imaging biomarker pipelines to facilitate high-throughput research and point-of-care use in patients with chronic lung disease. Image processing modules and algorithm pipelines were embedded within a graphical user interface (based on the .NET framework) for pulmonary magnetic resonance imaging (MRI) and x-ray computed-tomography (CT) datasets. The software pipelines were generated using C++ and included: (1) inhale

Intraoperative Fourier domain optical coherence tomography for microsurgery guidance and assessment

In this dissertation, advanced high-speed Fourier domain optical coherence tomography (FD-OCT)systems were investigated and developed. Several real-time, high resolution functional Spectral-domain OCT (SD-OCT) systems capable of imaging and sensing blood flow and motion were designed and developed. The system were designed particularly for microsurgery guidance and assessment. The systems were tested for their ability to assessing microvascular anastomosis and vulnerable plaque development. An all fiber-optic common-path optical coherence tomography (CP-OCT) system capable of measuring high-resolution optical distances, was built and integrated into di fferent imaging modalities. First, a novel non-contact accurate in-vitro intra-ocular lens power measurement method was proposed and validated based on CP-OCT. Second, CP-OCT was integrated with a ber bundle based confocal microscope to achieve motion-compensated imaging. Distance between the probe and imaged target was monitored by the CP-OCT system in real-time.The distance signal from the CP-OCT system was routed to a high speed, high resolution linear motor to compensate for the axial motion of the sample in a closed-loop control. Finally a motion-compensated hand-held common-path Fourier domain optical coherence tomography probe was developed for image-guided intervention. Both phantom and ex vivo models were used to test and evaluate the probe. As the data acquisition speed of current OCT systems continue to increase, the means to process the data in real-time are in critically needed. Previous graphics processing unit accelerated OCT signal processing methods have shown their potential to achieve real-time imaging. In this dissertation, algorithms to perform real-time reference A-line subtraction and saturation artifact removal were proposed, realized and integrated into previously developed FD-OCT system CPU-GPU heterogeneous structure. Fourier domain phase resolved Doppler OCT (PRDOCT) system capable of real-time simultaneous structure and flow imaging based on dual GPUs was also developed and implemented. Finally, systematic experiments were conducted to validate the system for surgical applications. FD-OCT system was used to detect atherosclerotic plaque and drug effi ciency test in mouse model. Application of PRDOCT for both suture and cu ff based microvascular anastomosis guidance and assessment was extensively stuided in rodent model

Imaging and Computational Methods for Exploring Sub-cellular Anatomy

The ability to create large-scale high-resolution models of biological tissue provides an excellent opportunity for expanding our understanding of tissue structure and function. This is particularly important for brain tissue, where the majority of function occurs at the cellular and sub-cellular level. However, reconstructing tissue at sub-cellular resolution is a complex problem that requires new methods for imaging and data analysis. In this dissertation, I describe a prototype microscopy technique that can image large volumes of tissue at sub-cellular resolution. This method, known as Knife-Edge Scanning Microscopy (KESM), has an extremely high data rate and can capture large tissue samples in a reasonable time frame. We can therefore image complete systems of cells, such as whole small animal organs, in a matter of days. I then describe algorithms that I have developed to cope with large and complex data sets. These include methods for improving image quality, tracing filament networks, and constructing high-resolution anatomical models. These methods are highly parallel and designed to allow users to segment and visualize structures that are unique to high-throughput microscopy data. The resulting models of large-scale tissue structure provide much more detail than those created using standard imaging and segmentation techniques

Dynamic Image Processing for Guidance of Off-pump Beating Heart Mitral Valve Repair

Compared to conventional open heart procedures, minimally invasive off-pump beating heart mitral valve repair aims to deliver equivalent treatment for mitral regurgitation with reduced trauma and side effects. However, minimally invasive approaches are often limited by the lack of a direct view to surgical targets and/or tools, a challenge that is compounded by potential movement of the target during the cardiac cycle. For this reason, sophisticated image guidance systems are required in achieving procedural efficiency and therapeutic success. The development of such guidance systems is associated with many challenges. For example, the system should be able to provide high quality visualization of both cardiac anatomy and motion, as well as augmenting it with virtual models of tracked tools and targets. It should have the capability of integrating pre-operative images to the intra-operative scenario through registration techniques. The computation speed must be sufficiently fast to capture the rapid cardiac motion. Meanwhile, the system should be cost effective and easily integrated into standard clinical workflow. This thesis develops image processing techniques to address these challenges, aiming to achieve a safe and efficient guidance system for off-pump beating heart mitral valve repair. These techniques can be divided into two categories, using 3D and 2D image data respectively. When 3D images are accessible, a rapid multi-modal registration approach is proposed to link the pre-operative CT images to the intra-operative ultrasound images. The ultrasound images are used to display the real time cardiac motion, enhanced by CT data serving as high quality 3D context with annotated features. I also developed a method to generate synthetic dynamic CT images, aiming to replace real dynamic CT data in such a guidance system to reduce the radiation dose applied to the patients. When only 2D images are available, an approach is developed to track the feature of interest, i.e. the mitral annulus, based on bi-plane ultrasound images and a magnetic tracking system. The concept of modern GPU-based parallel computing is employed in most of these approaches to accelerate the computation in order to capture the rapid cardiac motion with desired accuracy. Validation experiments were performed on phantom, animal and human data. The overall accuracy of registration and feature tracking with respect to the mitral annulus was about 2-3mm with computation time of 60-400ms per frame, sufficient for one update per cardiac cycle. It was also demonstrated in the results that the synthetic CT images can provide very similar anatomical representations and registration accuracy compared to that of the real dynamic CT images. These results suggest that the approaches developed in the thesis have good potential for a safer and more effective guidance system for off-pump beating heart mitral valve repair