A fast display method for volumetric data

Journal of Mechanics in Medicine and Biology, 2007

The 3D volume visualization is to overcome the difficulties of the 2D imaging by using computer technology. A volume visualization approach has been successfully implemented for Surgical Planning System in National Neuroscience Institute (NNI). The system allows surgeons to plan a surgical approach on a set of 2D image slices and process into volume models and visualise them in 3D rapidly and interactively on PC. In our implementation, we have applied it in neurosurgical planning. The surgeon can visualize objects of interest like tumor and surgical path, and verify that the surgical plan avoids the critical features and the planning of the surgical path can thus be optimal.

Orthodontics and Craniofacial Research, 2003

The objective of our research is to develop computer methods to accurately visualize patients in 3-dimensions using advanced imaging and data acquisition devices such as conebeam CT (Computerized Tomography) and mandibular motion capture. Data from these devices were integrated for 3D patient-specific visualization, modeling and animation. We are developing generic methods that can be used with common CT image format (DICOM), mesh format (STL) and motion data (3D position over time).

Procedia Computer Science, 2015

The advances in Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) scanning techniques are improving the resolution and size of the volume datasets. The prevalence of three dimensional volumetric data is rapidly expanding with the internal information in the different resolution sizes of the dataset. In this paper we proposed an approach that can visualize the inner organs structure of the visible human male dataset in Multi-coordinate Viewing (MCV) framework. This can help to medical experts that are able to peer inside anatomy of the human medical dataset. The volume rendering part has been carried out by the utilization of enhanced ray casting algorithm for the crossing points of 3D square strategy for voxels. We present this system using Graphics Processing Unit or GPU-accelerated Compute Unified Device Architecture (CUDA) based approach for the focusing a specific region while zooming operation. The final results would allow the doctors to diagnose and analyze the atlas of 8-bit CT-scan data using three dimensional visualization with the efficient frame rate rendering speed in multi-operations like zooming, rotating, dragging. The framework is tested for visible human male dataset prepared by National Library of Medicine (NLM, USA) of size 1.2 GB.

Medical Imaging 2004: Visualization, Image-Guided Procedures, and Display, 2004

This work presents a set of tools developed to provide 3D visualization and interaction with large volumetric data that relies on recent programmable capabilities of consumer-level graphics cards. We are exploiting the programmable control of calculations performed by the graphics hardware for generating the appearance of each pixel on the screen to develop real-time, interactive volume manipulation tools. These tools allow real-time modification of visualization parameters, such as color and opacity classification or the selection of a volume of interest, extending the benefit of hardware acceleration beyond display, namely for computation of voxel visibility. Three interactive tools are proposed: a cutting tool that allows the selection of a convex volume of interest, an eraser-like tool to eliminate non-relevant parts of the image and a digger-like tool that allows the user to eliminate layers of a 3D image. To interactively apply the proposed tools on a volume, we are making use of some so known user interaction techniques, as the ones used in 2D painting systems. Our strategy is to minimize the user entrainment efforts involved in the tools learning. Finally, we illustrate the potential application of the conceived tools for preoperative planning of liver surgery and for liver vascular anatomy study. Preliminary results concerning the system performance and the images quality and resolution are presented and discussed.

Computers in Biology and Medicine, 2007

Volume data cutting plays a crucial part in medical image probing, computer assisted diagnosis, virtual surgery, etc. Based on hardwareaccelerated texture-based volume rendering algorithm, the paper proposes a method for volume cutting. With Boolean operations, the method is extended to multi-object clipping and can meet the needs of more complicated clipping applications. Due to hardware acceleration, proposed algorithms achieve interactive display rate and can be used in volume cutting applications such as surgery simulation and so on.

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.

2001

In this paper, a "Surgeon Assistant System" using Enhanced Reality (ER) approach is presented. The main idea is to use Virtual Reality (VR) device(s), and assist the surgeon with one and two-dimensional information of human body and three-dimensional volumetric surface models, of anatomic tissues, especially in brain operations. This opportunity may increase the concentration of the surgeon with the combinational

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.

Biological and Medical Physics, Biomedical Engineering, 2011

Computers & Graphics, 1992

This paper examines seven computer architectures specifically designed to rapidly render 3D medical images from voxel data. The paper opens with a discussion of work on architectures for 3D medical image rendering and then specifies parameters for assessing the performance of a 3D medical image rendering architecture. We then describe and assess the 3DP 4, the Cube, the INSIGHT, the PARCUM II, the PICAP II, the Voxel Flinger, and the Voxel Processor architectures. For each machine the rendering speed, image resolution, underlying data model, image quality, parallel processing strategy, and 3D display technique are discussed. The architecture for each machine is characterized by its data storage technique, computational architecture, and parallelism strategy.