Real-time Computing Of Dynamic Volume Data In The Clinical Environment

Recent years, because of fast development of modern medical imagingequipments, they are playing more and more important roles in clinical diagnosis andmedicine research. Multi-dimensioned real-time screening of medical imaging isbecoming popular worldwide. Thus high volume medical information must beprocessed, integrated and mined effectively before clinical diagnosis. Intelligentanalysis of multi-dimensioned medical data is becoming necessary. The demand ofclinical diagnosis and research of medical radiology raise appeal for high-throuputanalysis methods of medical images, which is shown in two ways.Firstly, new imaging technology creates new digital analysis in medicine. Forexample, now four-dimensional (4D) ultrasound lets women and doctors look atfacial features and watch the growing baby move. With 3D ultrasound, a volume ofechoes is taken, stored digitally, and shaded to produce life-like images of the fetus. A4D ultrasound takes the images produced by 3D ultrasound and adds the element ofmovement. Now, the life-like pictures can move and the activity of the fetus can bestudied. To doctors, 4D reveals more detail about fetal health and small movement.Just as a pediatrician begins an exam by observing a newborn, doctors assess the fetusfrom head to toe on screen. Watching him or her shift position and breathe, doctor cancheck for problems.Secondly, high resolution images, ultra-fast scanning speed and a broad range ofclinical applications make postprocessing study necessary. For example, multisliceCT is no longer constrained by a patient's limited breath-hold time, allowing imagingof the heart and peripheral vessels. With a combination of more slices and thinner slices, multislice CT captures significantly larger data sets and yields sharper, moredetailed images. Thus it is useful to utilize sophisticated computer technology toenable radiologists to capture enormous value from advanced multislice images. With3D, radiologists can review large image sets quickly and easily for importantdiagnostic information that might otherwise be missed. Another example is VirtualCT Colonoscopy, which provides a comprehensive assessment of the inner colon aswell as the surrounding anatomy in one fast and easy exam compared to the invasiveoptical colonoscopy.For this purpose, the thesis mainly focuses on real-time computation ofmulti-dimensioned medical data in the clinical environment. Main topics are listedbelow.1. Study of medical image processing based on Graphics Processing Unit (GPU)is presented. Along with the fast improvement of personal computer, lots ofapplications in computer graphics are migrating from workstation to normal pc,which is mainly promoted by fast innovation of graphics hardware in pc platform.Except to applications of graphics itself, GPU is used as general-purpose processingunit in many other fields, which comes up very hot in research these years. Combinedwith a history introduction of GPU evolution in pc platform, a discussion is given toprobe into the development of GPU in medical image processing.2. Image segmentation means to split an image into a lot of homogeneousregions which are separable each other. It is a fundamental problem in imageprocessing and computer vision and is a key step to image analysis and even to imageunderstand with numerous applications.The Gibbs random fields model is animportant theory in solving the ill-posed inverse problem that needs properregularization in a degraded image, which has widely applied to medical Bayesiansegmentation due to providing an excellent spatial contextual constraints information.However, the classical GRF models must be revised because of computing speed inclinical environment. Thus, in the paper, an improved C-means segment methodbased on Gibbs random field accelerated by GPU is proposed.3. Automated bone segmentation by marker-controlled watershed based onGibbs morphological gradient and validation of segmentation using contourcoherency is proposed. Automated bone segmentation of CT image sequences is a key technology in computer-aided surgery. Elements such as the inhomogeneous ofbone, pathologies, and the inherent blurring of CT images all lead to difficulties ofcompletely automated bone segmentation. In this paper, an effective solution ispresented, which not only saves time-consuming human interaction, but also avoidsfatal errors caused by automated segmentation. Firstly, a new automated segmentationis implemented to extract bone contours. Secondly, wrong contours are detected byvalidation of segmentation using bone contour coherency in neighbor CT images.Some judgment mechanism can be adopted to reanalysis wrong contours.4. Real-time marching-cubes algorithm accelerated by GPU hardware isproposed. Isocontouring is widely used in the visualization of scalar data. Especiallyin medicine, computation of isocontours has huge applications in visualization ofsurfaces from medical volume data. An acceleration approach for renderingisosurfaces of a scalar field is presented. Using the Vertex Programming capability ofcommodity graphics cards, the cost of computing an isosurface from the CentralProcessing Unit (CPU), running the main application, is transfered to the GraphicsProcessing Unit (GPU), rendering the images.By the study, surfaces of time-varyingdatasets at distinguished threshold values can be extracted in real-time in clinicalexamination environment.5. Real-time volume rendering algorithm of dynamic 3D ultrasound acceleratedby GPU hardware is proposed. Dynamic 3D ultrasound is a very promisingtechnology in clinical use. But 3D ultrasound data can not be visualized directly withreal-time volume rendering accelerated by 3D texture hardware, because it is not aCartesian 3D dataset. In this paper, fast volume rendering using 3D texture hardwareacceleration is briefly introduced at first. Then by analyzing modern pc graphics cardarchitecture, a modified method is proposed to obtain real-time volume rendering ofdynamic 3D ultrasound data by Programming Vertex Shader in GPU. Experimentalresult shows that 3D ultrasound data can be rendered with real-time speed in normalpc platform. The method can be applied wide in future 3D dynamic ultrasoundclinical research.In all, with fast development of modern medical imaging equipments, intelligentanalysis of multi-dimensioned medical data is becoming more and more importantpart in clinical diagnosises. It is wide recognized that new medical imaging equipments can only be fully utilized by more and more study of new post-processingalgorithms and visualization methods of of medical imaging data. Some usefulmethods are studied in the paper.