Dynamic real-time reconstruction and rendering of breast tomosynthesis images

描述（由申请人提供）：基于初步临床结果，人们普遍认为数字乳腺断层合成摄影（DBT）有可能在未来取代乳腺X射线摄影。目前至少有八家公司正在开发DBT成像系统，其中三家公司几乎没有开发初级审查工作站的经验。实时断层扫描（RTT）具有开发医疗工作站的经验。RTT目前正在开发用于数字乳腺断层合成摄影图像初步诊断的医师审查工作站。我们认为，一个专用的工作站是必要的，基于大尺寸的DBT数据集，严格的图像质量要求的乳腺成像，并需要高通量的乳腺筛查和诊断。为了满足高吞吐量的需求，我们特别建议开发和实施一种重建算法，该算法将允许在专用高端基于PC的图形处理器单元（GPU）上通过乳房体积对任意平面进行动态实时重建和渲染（DRR）。我们相信，GPU技术的进步使得按需重建断层合成图像成为可能。有很多理由支持DRR。首先，当今存在的GPU硬件允许对断层图像进行几乎瞬时的反投影和滤波（BPF）重建;该硬件基本上是现成的并且随时可用。第二，当前生成的乳房断层合成数据集是各向异性的，具有大约0.07-0.1 mm 2的平面内分辨率和1 mm的平面外分辨率。这种各向异性是由所得图像集的大小和生成这些数据所需的时间所必需的。然而，在DBT中，可以在任何位置重建图像; DRR将允许任意重建，而不会对存储或速度产生不利影响。RTT的长期目标是开发一个基于DRR原则的主要审查工作站。在这个II期提案中，我们寻求改进我们现有的DBT重建算法和滤波器，以增强乳腺癌的不同临床适应症，这对正确诊断至关重要。我们的最终目标是实现至少20帧每秒（fps）的重建速率。在第二阶段，我们提出了以下具体目标：（1）使用通用GPU编程技术在GPU上优化我们的反投影算子。(2)使用通用GPU编程技术优化我们的成像过滤方法，以专门增强钙化和肿块的图像。(3)开发厚切片渲染方法并在GPU上实现。(4)探索在GPU上处理大型数据集的方法。宾夕法尼亚大学将提供图像重建的图像质量评估，并将提供模拟体模图像，以优化图像反投影和滤波方法。公共卫生相关性：乳房X线摄影（包括数字乳房X线摄影）受到许多基本限制，因为2D图像是由3D乳房解剖结构制成的。由于正常组织的叠加，乳房X光检查可能产生假阳性结果，并且癌症可能在乳房X光检查中被遗漏，因为正常组织隐藏或掩盖了癌症。数字乳腺断层合成摄影（DBT）是一种断层成像方法，有可能解决乳腺X射线摄影的上述两个问题。断层合成成像系统正由多个制造商开发。然而，正如临床数字乳腺X射线摄影系统的持续部署所表明的那样，采集技术的开发只是使DBT成为临床现实所需努力的一小部分。我们建议为DBT开发一个初级医师评审工作站。这是任何临床DBT系统的重要组成部分。

DESCRIPTION (provided by applicant): It is widely believed that digital breast tomosynthesis (DBT) has the potential to replace mammography in the future, based on preliminary clinical results. At least eight companies are currently developing DBT imaging systems, three of whom have little or no experience developing primary review workstations. Real-Time Tomography (RTT) has the experience to develop medical workstations. RTT is currently in the process of developing a physician review workstation for the primary diagnosis of digital breast tomosynthesis images. We believe that a dedicated workstation is necessary based upon the large size of DBT datasets, the stringent image quality requirements for breast imaging, and the need for high-throughput in both breast screening and diagnosis. To address the need for high-throughput, we specifically propose to develop and implement a reconstruction algorithm that will allow dynamic real-time reconstruction and rendering (DRR) of arbitrary planes through the breast volume on a dedicated high-end PC-based graphics processor unit (GPU). We believe that the advances in GPU technology make it both possible and preferential to reconstruct tomosynthesis images on demand. There are many reasons to favor DRR. First, the GPU hardware that exists today allows nearly instantaneous back-projection and filtered (BPF) reconstruction of tomographic images; this hardware is essentially off-the- shelf and readily available. Second, breast tomosynthesis datasets currently being produced are anisotropic, having in-plane resolution of approximately 0.07-0.1 mm2 and out-of-plane resolution of 1 mm. This anisotropy is necessitated by the size of the resultant image set, and the time required to produce those data. However, in DBT it is possible to reconstruct images at any location; DRR would allow arbitrary reconstruction without adversely impacting either storage or speed. The long-term goal of RTT is to develop a primary review workstation based on the DRR principle. In this Phase II proposal, we seek to refine our existing DBT reconstruction algorithm and filters to enhance the different clinical indications of breast cancer that is crucial for proper diagnosis. Our ultimate goal is to achieve a reconstruction rate of at least 20 frames per second (fps). In Phase II, we propose the following specific aims: (1) Refine our backprojection operators on a GPU using general-purpose GPU programming techniques. (2) Refine our imaging filtering methods to specifically enhance images of calcifications and masses using general-purpose GPU programming techniques. (3) Develop thick-slice rendering methods and implement them on a GPU. (4) Explore methods for handling large data sets on the GPU. The University of Pennsylvania will provide assessments of image quality of the image reconstructions and will provide simulated phantom images to allow optimization of the image backprojection and filtering methods. PUBLIC HEALTH RELEVANCE: Mammography (including digital mammography) is subject to a number of fundamental limitations because 2D images are made of the 3D breast anatomy. Mammograms can produce false positive findings due to the superposition of normal tissues, and cancers can be missed in mammograms because normal tissue hide or mask the cancer. Digital breast tomosynthesis (DBT) is a tomographic imaging method with the potential to solve both of the above problems of mammography. Tomosynthesis imaging systems are being developed by multiple manufacturers. However, as has been made clear from the ongoing deployment of clinical digital mammography systems, the development of the acquisition technology is only a small part of the effort that will be required to make DBT a clinical reality. We propose to develop a primary physician review workstation for DBT. This is an essential part of any clinical DBT system.