A multi-channel reconstruction toolkit for computed tomography

摘要 根据FDA的说法，最近光子计数探测器X射线CT(PCCT)的临床应用标志着 “近十年来计算机断层扫描的第一个主要成像设备的进步”，因为它提供了 在图像噪声、空间分辨率、材质分辨率和低对比度方面的根本改进 可探测性。然而，在这个临床PCCT的新时代之前，我们的定量成像公司进行了多年的临床前研究 和分析实验室，以及我们使用NAEOTOM Alpha临床的初步研究项目 来自西门子的PCCT扫描仪，安装在杜克大学。支持这些研究项目，并在以下目标下 我们资助的NIA基金(1RF1AG070149；心脏光子计数CT及其在相互作用研究中的应用 阿尔茨海默氏症和心脏病)，我们正在开发一种基于GPU的多通道重建(MCR) CT工具包(通道指的是3D解剖结构的动态和/或多能量图像)。此外 到从头开始构建以应对多通道重建的独特挑战，我们的工具包 包括其他开放源码包中没有的几个独特功能：支持使用 临床和临床前数据的代码相同，用于执行迭代的健壮参考算法 低剂量多通道CT数据重建及重建参数的自动数据自适应 以减少手动参数调整。本附录的目标是(1)使MCR工具包的代码库 为CT专家提供更强大的可扩展性，以及(2)提供图形用户界面和参考 用于使用CT进行其他研究研究的重建方案。我们将实现这些目标 通过三个不同的目标实现目标。在目标1中，我们建议将我们的工具包代码库从当前的 C代码到用C++编写的面向对象代码，为流行的文件格式提供本机支持，消除了 冗余代码，减少用户出错的机会，并允许保存重建管道和 共享。Aim 2专注于非专家用户，为运行重建提供图形用户界面 对采集的数据进行管道处理，并将图像数据和元数据上传到XNAT数据库。最后，目标3重点 关于社区意识、可访问性和MCR工具包的文档，确保工具包可用 并且易于使用，超出了本补充条款和家长补助金的范围。圆满完成这项工作 将把MCR工具包从对该领域的专家有用的工具集合转变为开放科学的平台 在CT、基础科学和临床研究人员中。该工具包预计将被广泛采用，这要归功于 它为光子计数和动态CT提供了开源目前无法提供的独特优势 软件替代方案。

Abstract According to the FDA, the recent clinical availability of photon counting detector X-ray CT (PCCT) marks the “first major imaging device advancement for computed tomography in nearly a decade” because it provides fundamental improvements in terms of image noise, spatial resolution, material discrimination, and low contrast detectability. Yet this new era of clinical PCCT follows years of preclinical research at our Quantitative Imaging and Analysis Lab at Duke University and our preliminary research projects using the NAEOTOM Alpha clinical PCCT scanner from Siemens and installed at Duke. To support these research projects and under the Aims of our funded NIA grant (1RF1AG070149; Cardiac photon counting CT and its application in studying interactions between Alzheimer's and heart disease), we are developing a GPU-based Multi-Channel Reconstruction (MCR) Toolkit for CT (channels refer to dynamic and/or multi-energy images of 3D anatomical structures). In addition to being built from the ground up to handle the unique challenges of multi-channel reconstruction, our Toolkit includes several unique features not in other open-source packages: support for translational research using the same code on both clinical and preclinical data, robust reference algorithms for performing iterative reconstruction of low-dose multi-channel CT data, and automatic data adaptation of reconstruction parameters to reduce manual parameter tuning. The objectives of this supplement are (1) to make the MCR Toolkit codebase more robust and extensible for CT experts and (2) to provide a graphical user interface and reference reconstruction protocols for those using CT to conduct other research studies. We will accomplish these objectives through three distinct aims. In Aim 1 we propose to restructure our Toolkit codebase from its current C code to object oriented code written in C++, providing native support for popular file formats, eliminating redundant code, reducing opportunities for user errors, and allowing reconstruction pipelines to be saved and shared. Aim 2 focuses on non-expert users, producing a graphical user interface for running reconstruction pipelines on acquired data and uploading image data and metadata to an XNAT database. Finally, Aim 3 focuses on community awareness, accessibility, and documentation for the MCR Toolkit, ensuring the Toolkit is available and easy to use beyond the scope of this supplement and the parent grant. Successful completion of this work will transform the MCR Toolkit from a collection of tools useful to experts in the field to a platform for open science among CT, basic science, and clinical researchers. Widespread adoption of the Toolkit is expected thanks to the unique benefits it provides for photon counting and dynamic CT which are not currently provided by open-source software alternatives.