With perfusion magnetic resonance imaging (pMRI), perfusion describes the amount of blood passing through a block of tissue in a certain period of time. In pMRI, the tissue having more blood passing through will show higher intensity value as more contrast-labeled blood arrives. Perfusion reflects the delivery of essential nutrients to a block of tissue, and is an important parameter for the tissue status. Considering solitary pulmonary nodules (SPN), perfusion differences between malignant and benign nodules have been studied by different techniques. Much effort has been put into its characterization. In this paper, we proposed and implemented extraction of the SPN time intensity profile to measure blood delivery to solitary pulmonary nodules, describing their perfusion effects. In this method, a SPN time intensity profile is created based on intensity values of the solitary pulmonary nodule in lung pMRI images over time. This method has two steps: nodule tracking and profile clustering. Nodule tracking aligns the solitary pulmonary nodule in pMRI images taken at different time points, dealing with nodule movement resulted from breathing and body movement. Profile clustering implements segmentation of the nodule region and extraction of the time intensity profile of a solitary pulmonary nodule. SPN time intensity profiles reflect patterns of blood delivery to solitary pulmonary nodules, giving us a description of perfusion effect and indirect evidence of tumor angiogenesis. Analysis on SPN time intensity profiles will help the diagnosis of malignant nodules for early lung cancer detection.
The visualization and comparison of local deformation from 3D image sequences is of critical importance in understanding the etiology of Ischemic cardiac disease. In this paper we describe a framework to combine our previous fast spherical harmonic surface alignment algorithm with a new local special surface reconstruction method to reconstruct the surface of LV with Ischaemic cardiac disease. Our new surface computational model allows people to extract the valuable ischemic tissues behavior from the dynamic shape. We have demonstrated our approaches by the experiments on cardiac MRI. A brief description of motivation is put forth, as well as an overview of the approaches and some initial results are described.
To detect lung cancer at an earlier stage, a promising method is to apply perfusion magnetic resonance imaging (pMRI) modified to assess tumor angiogenesis. One key issue is to effectively characterize angiogenic patterns of pulmonary nodules. Based on our previous study addressing this issue, in this work, we develop STAT, a Spatio-Temporal Analysis Tool that implements not only our previously proposed pulmonary nodule modeling framework but also a user friendly interface and many extended functions. Our goal is to make STAT an easy-to-use tool that can be applied to more general cases. STAT employs the following overall strategy for modeling pulmonary nodules: (1) nodule identification using a correlation maximization method, (2) nodule segmentation using edge detection, morphological operations and model-based strategy, and (3) nodule registration using landmark approach and thin-plate spline interpolation. In nodule identification, STAT provides new schemes for selecting the template and refining results in difficult cases. In nodule segmentation, STAT provides additional flexibilities for creating the weighting mask, selecting morphological structure elements and individually fixing segmentation result. In nodule registration, our previous study uses principal component analysis for landmark extraction, which may not work in general. To overcome this limitation, STAT provides an enhanced approach that minimizes the bending energy of the thin plate spline interpolation or mean square distance between each landmark set and the template set. Our main application of STAT is to define blood arrival patterns in the lung to identify tumor angiogenesis as a means of early accurate diagnosis of cancer.
Visualization and quantification of cardiac function can provide direct and reliable indicators of cardiac health. The heart's operation occurs in three dimensions, and is dependent on three dimensional forces and ventricular geometry, making the observation of its shape important. Many approaches have been presented to extract cardiac shape and do functional analysis from a variety of imaging modalities. We apply a spherical harmonics (SPHARM) model to cardiac function analysis using magnetic resonance (MR) images. Our three dimensional SPHARM approach increases measurement accuracy over two dimensional approaches and also simplifies the management and indexing of clinical data by providing access to many important functional measures directly from the SPHARM representation.
KEYWORDS: 3D image processing, Visualization, Binary data, 3D vision, Image visualization, 3D metrology, 3D visualizations, Tomography, Angiography, Bone
This paper presents IVM, an Interactive Vessel Manipulation tool that can help make effective and efficient assessment of angiogenesis and arteriogenesis in computed tomographic angiography (CTA) studies. IVM consists of three fundamental components: (1) a visualization component, (2) a tracing component, and (3) a measurement component. Given a user-specified threshold, IVM can create a 3D surface visualization based on it. Since vessels are thin and tubular structures, using standard isosurface extraction techniques usually cannot yield satisfactory reconstructions. Instead, IVM directly renders the surface of a derived binary 3D image. The image volumes collected in CTA studies often have a relatively high resolution. Thus, compared with more complicated vessel extraction and visualization techniques, rendering the binary image surface has the advantages of being effective, simple and fast. IVM employs a semi-automatic approach to determine the threshold: a user can adjust the threshold by checking the corresponding 3D surface reconstruction and make the choice. Typical tracing software often defines ROIs on 3D image volumes using three orthogonal views. The tracing component in IVM takes one step further: it can perform tracing not only on image slices but also in a 3D view. We observe that directly operating on a 3D view can help a tracer identify ROIs more easily. After setting a threshold and tracing an ROI, a user can use IVM's measurement component to estimate the volume and other parameters of vessels in the ROI. The effectiveness of the IVM tool is demonstrated on rat vessel/bone images collected in a previous CTA study.
Shape analysis of hippocampi in schizophrenia has been preformed previously using the spherical harmonic SPHARM description. In these studies, the left and right hippocampi are aligned independently and the spatial relation between them is not explored. This paper presents a new SPHARM-based technique which examines not only the individual shape information of the two hippocampi but also the spatial relation between them.
The left and right hippocampi are treated as a single shape configuration. A ploy-shape alignment algorithm is developed for aligning configurations of multiple SPHARM surfaces as follows: (1) the total volume is normalized; (2) the parameter space is aligned for creating the surface correspondence; (3) landmarks are created by a uniform sampling of multiple surfaces for each configuration; (4) a quaternion-based algorithm is employed to align each landmark representation to the mean configuration through the least square rotation and translation iteratively until the mean converges.
After applying the poly-shape alignment algorithm, a point distribution model is applied to aligned landmarks for feature extraction. Classification is performed using Fisher's linear discriminant with an effective feature selection scheme. Applying the above procedure to our hippocampal data (14 controls versus 25 schizophrenics, all right-handed males), we achieve the best cross-validation accuracy of 92%, supporting the idea that the whole shape configuration of the two hippocampi provides valuable information in detecting schizophrenia. The results of an ROC analysis and a visualization of discriminative patterns are also included.
Surface-based representation and classification techniques are studied for hippocampal shape analysis. The goal is twofold: (1) develop a new framework of salient feature extraction and accurate classification for 3D shape data; (2) detect hippocampal abnormalities in schizophrenia using this technique. A fine-scale spherical harmonic expansion is employed to describe a closed 3D surface object. The expansion can then easily be transformed to extract only shape information (i.e., excluding translation, rotation, and scaling) and create a shape descriptor comparable across different individuals. This representation captures shape features and is flexible enough to do shape modeling, identify statistical group differences, and generate similar synthetic shapes. Principal component analysis is used to extract a small number of independent features from high dimensional shape descriptors, and Fisher's linear discriminant is applied for pattern classification. This framework is shown to be able to perform well in distinguishing clear group differences as well as small and noisy group differences using simulated shape data. In addition, the application of this technique to real data indicates that group shape differences exist in hippocampi between healthy controls and schizophrenic patients.
Lectures and similar presentations are increasingly available in digital format as tools for presenting and recording become rich in features and readily available. We discuss extensions to a representative of such tools that facilitate better retrieval functionality while keeping the system simple and the instructor focused on the presented material, rather than on adding the necessary meta- information by hand.
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