4D Dynamic Volume Rendering using Raycasting

2004

This paper reports on a new approach for visualizing multi-field MRI or CT datasets in an immersive environment with medical applications. Multi-field datasets combine multiple scanning modalities into a single 3D, multivalued, dataset. In our approach, they are classified and rendered using real-time hardware accelerated volume rendering, and displayed in a hybrid work environment, consisting of a dual power wall and a desktop PC. For practical reasons in this environment, the design and use of the transfer functions is subdivided into two steps, classification and exploration. The classification step is done at the desktop, taking advantage of the 2D mouse as a high accuracy input device. The exploration process takes place on the powerwall. We present our new approach, describe the underlying implementation issues, report on our experiences with different immersive environments, and suggest ways it can be used for collaborative medical diagnosis and treatment planning.

International Journal of Online and Biomedical Engineering (iJOE)

One of the most valuable medical imaging visualizations or computer-aided diagnosis is Volume rendering (VR). This survey’s objective is reviewing and comparing between several methods and techniques of VR, for a better and more comprehensive reading and learning of both pros and cons of each method, and their use cases.

Proceedings of the IEEE, 1998

High-resolution imaging modalities, such as magnetic resonance imaging (MRI) and computed tomography (CT), combined with advances in computer technology, have prompted a renewed interest and have led to significant progress in the volumetric reconstruction of medical images. Assessment of such techniques for various clinical applications and medical educational interpretations are currently under investigation by many medical and scientific groups. The purpose of this paper is to highlight various clinical applications that show potential for the utilization of volumetric rendering of medical images. Such applications include 1) diagnostics, 2) preoperative planning, 3) intraoperative navigation, 4) surgical robotics, 5) postoperative validation, 6) training, and 7) telesurgery. First, however, we will try to identify the sources of the patients' imaging data, outlining several popular segmentation and volumetric rendering techniques. At the conclusion, we will discuss the necessary requirements to deploy a practical three-dimensional (3-D) software for clinicians. This paper is intended for those who would like to obtain a general understanding of the clinical 3-D rendering process and its applications. Here, no effort is made at detailing the various volumetric rendering, segmentation, or user interface design techniques, nor is any effort made to review the commercially available software. Although some sample publications are listed to illustrate the various mythologies, no attempt is made at providing a complete coverage of the literature. Readers desiring more information or further clarification on the subjects below should refer to the cited references as a starting point for their inquires.

Seminars in Ultrasound Ct and Mri, 2001

Three-dimensional medical images can be generated with a variety of computer algorithms from computed tomography and magnetic resonance data sets. The most commonly used techniques are maximum intensity projection (MIP) and shaded surface display (SSD). Recently, volume rendering (VR) has become available on dedicated workstations, providing the possibility of interaction with data sets. All 3D rendering techniques represent a volume of data in 1 or more 2-dimensional (2D) planes, conveying the spatial relationships inherent in the data with the use of visual depth cues. Techniques and artefacts regarding MIP, SSD, and VR are described here, along with several models of clinical application.

2008 Fifth International Conference BioMedical Visualization: Information Visualization in Medical and Biomedical Informatics, 2008

This paper describes the Medical Visualizer, a real-time visualization system for analyzing medical volumetric data in various virtual environments, such as autostereoscopic displays, dual-projector screens and immersive environments such as the CAVE. Direct volume rendering is used for visualizing the details of medical volumetric data sets without intermediate geometric representations. By interactively manipulating the color and transparency functions through the friendly user interface, radiologists can either inspect the data set as a whole or focus on a specific region. In our system, 3D texture hardware is employed to accelerate the rendering process. The system is designed to be platform independent, as all virtual reality functions are separated from kernel functions. Due to its modular design, our system can be easily extended to other virtual environments, and new functions can be incorporated rapidly.

Lecture Notes in Computer Science, 2007

Volumetric data rendering has become an important tool in various medical procedures as it allows the unbiased visualization of fine details of volumetric medical data (CT, MRI, fMRI). However, due to the large amount of computation involved, the rendering time increases dramatically as the size of the data set grows. This paper presents several acceleration techniques of volume rendering using general-purpose GPU. Some techniques enhance the rendering speed of software ray casting based on voxels' opacity information, while the others improve traditional hardware-accelerated object-order volume rendering. Remarkable speedups are observed using the proposed GPU-based algorithm from experiments on routine medical data sets.

IEEE Transactions on Medical Imaging, 1994

Abstmct-In this paper we explore the application of volume rendering in medical ultrasonic imaging. Several volume rendering methods have been developed for X-ray Computed Tomography (X-CT), Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). Limited research has been done on applications of volume rendering techniques in medical ultrasound imaging because of a general lack of adequate equipment for 3D acquisitions. Severe noise sources and other limitations in the imaging system make volume rendering of ultrasonic data a challenge compared to rendering of MRI and X-CT data. Rendering algorithms that rely on an initial classification of the data into different tissue categories have been developed for high quality X-CT and MR-data. So far, there is a lack of general and reliable methods for tissue classification in ultrasonic imaging. This paper focuses on volume rendering methods which are not dependent on any classication into different tissue categories. Instead, features are extracted from the original 3D data-set, and projected onto the view plane. We found that some of these methods may give clinically useful information which is very difficult to get from ordinary 2D ultrasonic images, and in some cases renderings with very fine structural details. We have applied the methods to 3D ultrasound images from fetal examinations. The methods are now in use as clinical tools at the National Center of Fetal Medicine in 'kondheim, Norway.

Medical Imaging 2008: Visualization, Image-guided Procedures, and Modeling, 2008

The two major volume visualization methods used in biomedical applications are Maximum Intensity Projection (MIP) and Volume Rendering (VR), both of which involve the process of creating sets of 2D projections from 3D images. We have developed a new method for very fast, high-quality volume visualization of 3D biomedical images, based on the fact that the inverse of this process (transforming 2D projections into a 3D image) is essentially equivalent to tomographic image reconstruction. This new method uses the 2D projections acquired by the scanner, thereby obviating the need for the two computationally expensive steps currently required in the complete process of biomedical visualization, that is, (i) reconstructing the 3D image from 2D projection data, and (ii) computing the set of 2D projections from the reconstructed 3D image As well as improvements in computation speed, this method also results in improvements in visualization quality, and in the case of x-ray CT we can exploit this quality improvement to reduce radiation dosage. In this paper, demonstrate the benefits of developing biomedical visualization techniques by directly processing the sensor data acquired by body scanners, rather than by processing the image data reconstructed from the sensor data. We show results of using this approach for volume visualization for tomographic modalities, like x-ray CT, and as well as for MRI.

Medical Imaging 2008: Visualization, Image-guided Procedures, and Modeling, 2008

The two major volume visualization methods used in biomedical applications are Maximum Intensity Projection (MIP) and Volume Rendering (VR), both of which involve the process of creating sets of 2D projections from 3D images. We have developed a new method for very fast, high-quality volume visualization of 3D biomedical images, based on the fact that the inverse of this process (transforming 2D projections into a 3D image) is essentially equivalent to tomographic image reconstruction. This new method uses the 2D projections acquired by the scanner, thereby obviating the need for the two computationally expensive steps currently required in the complete process of biomedical visualization, that is, (i) reconstructing the 3D image from 2D projection data, and (ii) computing the set of 2D projections from the reconstructed 3D image As well as improvements in computation speed, this method also results in improvements in visualization quality, and in the case of x-ray CT we can exploit this quality improvement to reduce radiation dosage. In this paper, demonstrate the benefits of developing biomedical visualization techniques by directly processing the sensor data acquired by body scanners, rather than by processing the image data reconstructed from the sensor data. We show results of using this approach for volume visualization for tomographic modalities, like x-ray CT, and as well as for MRI.

IEEE Engineering in Medicine and Biology Magazine, 2000

This paper gives an overview of 3D display techniques for medical data including surface and volume rendering techniques. A special emphasis is done on the most recent methodology which becomes widely used to render medical volumetric data sets: Ray-Tracing which makes possible the volume rendering of voxel data. This paper describes the basic fundamentals of the fuzzy display algorithms involved in the volume rendering techniques. The different techniques presented in this paper will take example from applications using 3D display of CT, MRI or DSR data and mapping of functional and anatomical data by means of 3D textures. Finally this paper concludes by giving some perspectives of 3D display of volume and surface data.