A multi-channel reconstruction toolkit for computed tomography

用于计算机断层扫描的多通道重建工具包

基本信息

批准号：
10605585
负责人：
CRISTIAN T BADEA
金额：
$ 22.63万
依托单位：
DUKE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-02-15 至 2024-01-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10605585
关键词：
3-Dimensional Adoption Algorithms Alzheimer&apos s Disease Anatomy Awareness Basic Science Calibration Cardiac Clinical Code Collaborations Collection Communities Computer software Data Database Management Systems Databases Dependence Development Digital Imaging and Communications in Medicine Discrimination Docking Documentation Dose Ensure Environment Funding Generations Geometry Grant Heart Diseases Image Imaging Device International Knowledge Linux Maintenance Malignant Neoplasms Manuals Medical Imaging Metadata Modeling Noise Outcome Photons Physiological Programming Languages Protocols documentation Publications Publishing Pythons Radiation Dose Unit Reference Document Reproducibility Research Research Personnel Research Project Grants Research Support Resolution Running Site Source Structure System TensorFlow Time Translational Research Translations Universities Work X-Ray Computed Tomography algorithmic methodologies base biological research cone-beam computed tomography data acquisition deep learning denoising design detector experimental study file format graphical user interface improved innovation open data open source parent grant photon-counting detector pre-clinical pre-clinical research quantitative imaging reconstruction research study scripting interface symposium tool

项目摘要

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.

抽象的据 FDA 称，光子计数探测器 X 射线 CT (PCCT) 最近的临床应用标志着 “近十年来计算机断层扫描成像设备的首次重大进步”，因为它提供了在图像噪声、空间分辨率、材料辨别和低对比度方面有了根本性的改进然而，临床 PCCT 的新时代是在我们的定量成像领域经过多年的临床前研究之后出现的。杜克大学的分析实验室以及我们使用 NAEOTOM Alpha 临床的初步研究项目西门子的 PCCT 扫描仪安装在杜克大学，以支持这些研究项目并遵循该项目的目标。我们资助的 NIA 赠款（1RF1AG070149；心脏光子计数 CT 及其在研究相互作用中的应用阿尔茨海默病和心脏病之间），我们正在开发基于 GPU 的多通道重建 (MCR) CT 工具包（通道是指 3D 解剖结构的动态和/或多能量图像）。为了应对多通道重建的独特挑战，我们的工具包是从头开始构建的包括其他开源软件包中没有的几个独特功能：支持使用临床和临床前数据的相同代码，用于执行迭代的强大参考算法低剂量多通道CT数据重建，以及重建参数的自动数据自适应减少手动参数调整。本补充的目标是 (1) 制作 MCR 工具包代码库。对于 CT 专家来说更加强大和可扩展，并且 (2) 提供图形用户界面和参考我们将完成这些使用 CT 进行其他研究的重建方案。在目标 1 中，我们建议在当前的基础上重组我们的 Toolkit 代码库。 C 代码到 C++ 编写的面向对象代码，为流行的文件格式提供本机支持，消除冗余代码，减少用户错误的机会，并允许保存和重建重建管道目标 2 专注于非专家用户，生成用于运行重建的图形用户界面。最后，目标 3 重点关注获取的数据以及将图像数据和元数据上传到 XNAT 数据库。关于 MCR 工具包的社区意识、可访问性和文档，确保工具包可用并且易于使用，超出了本补充材料和家长资助的范围成功完成了这项工作。将使 MCR 工具包从对该领域专家有用的工具集转变为开放科学平台由于该工具包的出现，预计将在 CT、基础科学和临床研究人员中得到广泛采用。它为光子计数和动态 CT 提供了目前开源尚未提供的独特优势软件替代品。