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.
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.
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.
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