Visualization of Multi-Resolution Reconstructions Using Interactive Stereoscopy

描述(申请人提供)：从历史上看，核医学成像是使用2D视图进行的。随着计算机和断层摄影术的出现，3D图像被采集并存储在规则的体素阵列中。尽管3D对象是2D的逻辑扩展，但将3D对象表示为2D图像堆叠可能不是病变检测的最佳选择。这是因为具有矩形几何体的体素不能准确地近似成像对象的几何体。这种不匹配对于核医学研究尤其重要，在这些研究中，退化的物理因素迫使图像在由大的立方体素组成的网格上重建。考虑到病变很小，而且可能在大小上与体素相当，立方体素方法的不足变得明显。我们的假设是，通过使用本文提出的多分辨率图像表示法，可以比使用规则网格更准确和高效地重建和显示图像，从而提高FDG-PET的病变检测能力。我们选择将图像表示为空间(点云)中的一组点，每个点(节点)具有不受限制的位置和强度，体积由节点定义的一组不重叠的四面体表示。选择这个代表是由两个因素决定的。首先，每个图像都将有其自己的网格，该网格将被设计为准确而高效地表示每个图像。通过使用不受限制的节点位置，可以对在整个图像体积上变化的具有任意局部分辨率的几何图形进行建模。其次，图像的四面体几何将利用计算机图形硬件的最新革命性进展，以便使用先进的可视化技术对成像数据进行立体交互3D可视化。本文提出的工作将为核医学中图像的无体素多分辨率表示创建一个框架。它包括使用医学成像和计算机图形学的层析重建和立体可视化方法的多学科发展。这项工作包括设计和创建软件，用于重建和立体显示基于四面体的图像，并针对病变检测任务的人类观察者进行评估。癌症的早期发现极大地提高了康复的机会。这项建议通过提供工具，使患者护理直接受益，这些工具将导致早期发现使用当前技术无法检测到的小病变。

DESCRIPTION (provided by applicant): 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.