Historically, nuclear medicine imaging has been done using 2D views. With the advent of computers and tomography, 3D images were acquired and stored in regular arrays of voxels. Although a logical extension from 2D, representation of a 3D object as a stack of 2D images may not be optimal for lesion detection. This is because voxels with rectangular geometry do not accurately approximate the geometry of the imaged objects. This mismatch is especially significant for nuclear medicine studies where degrading physical factors force the images to be reconstructed on grids that consist of large cubic voxels. Considering that the lesions are small and may be comparable in size to voxels, the inadequacy of cubic voxel approach becomes evident. Our hypothesis is that by using the multi-resolution image representation proposed in this work, the images can be reconstructed and presented more accurately and efficiently than using the regular grids, thus resulting in lesion detection improvement using FDG-PET. We chose to represent the image as a set of points in space (point cloud) with unrestricted locations and intensities assigned to each point (node) with volume represented by a set of non-overlapping tetrahedrons defined by the nodes. The selection of this representation was dictated by two factors. First, each image will have its own grid that will be designed to accurately and efficiently represent each image. By using unrestricted node positions, a geometry with arbitrary local resolution that varies across the image volume can be modeled. Second, the tetrahedral geometry of the image will take advantage of recent revolutionary progress in computer graphics hardware in order to use advanced visualization techniques for stereoscopic interactive 3D visualization of the imaging data. The work proposed here will create a framework for voxel-less multi- resolution representation of the image in nuclear medicine. It includes multidisciplinary development of the tomographic reconstruction and stereoscopic visualization methods using medical imaging and computer graphics. The work involves design and creation of software for reconstruction and stereoscopic display of tetrahedron based images and evaluation against human observers for the lesion detection task. Early detection of cancer tremendously improves the chances of recovery. This proposal directly benefits patient care by providing tools that will lead to early detection of small lesions that are undetectable using current technology. ? ? ?
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