Advanced Medical Image Reconstruction

背景 医学影像学在心血管疾病的诊断和治疗中发挥着重要作用。生成医学图像的过程包括两个部分：数据采集和图像重建。图像重建将采集的原始数据信号转换为临床医生可以解释的图像，以帮助诊断疾病或用于指导手术。原始数据经常被仪器缺陷和患者运动破坏。数据集不完整也很常见，因为可用于数据采集的时间、辐射剂量或其他患者暴露量有限。因此，现代图像重建软件相当复杂。这些复杂图像重建算法的源代码通常是医疗设备制造商保留的专有信息，并且在医学图像重建领域工作的科学家被迫实现他们自己的现有算法版本，然后在其基础上进行构建和改进。由于这种重新实施和缺乏开放标准，许多关于医学图像重建的已发表文献是不可复制的。该项目的总体目标是开发新的图像重建算法，并以其他科学家（和设备制造商）可以重现所呈现的结果并在未来工作中使用该方法的方式进行。成像技术实验室，NHLBI特别专注于磁共振成像（MRI）技术，但开发的原则也适用于其他技术。 目标/目的 成像技术实验室开发和维护两个主要的软件包，支持正在进行的研究项目。第一种是ISMRM原始数据格式（https：ismrmrd.github.io），这是MR实验的开放原始数据标准。共享算法和方法的一个要求是，对如何描述原始数据有共同的理解，本软件包为此提供了框架。我们还致力于维护数据转换工具，从主要设备制造商的专有格式转换为这种独立于供应商的格式。第二个软件包是Gadgetron（https：gadgetron.github.io），它是一个高级图像重建包，包含用于处理成像仪器采集的原始数据的工具箱和流式流水线架构。我们的目标是扩展这个软件包，并支持世界各地不断增长的用户群。我们目前正在进行的技术创新有： * 扩展ISMRMRD格式，以包括来自其他仪器的波形和遥测。 * ISMRMRD通信协议的形式化定义与实现。 * 使用云计算进行MRI重建。 * MRI原始数据压缩。 * 测量系统缺陷的纠正。 * Gadgetron与特定供应商仪器的紧密集成。 除了这些基础设施的目标，我们正在开发和测试新的图像重建技术，以解决特定的临床问题： * 介入性MRI的实时成像序列。 * 实时测量血流。 * 用于测量心脏功能和参数图的运动校正、自由呼吸技术。 * 心肌灌注定量评价。 2016财年进展 在过去的一年中，我们在基于云的MRI重建方法方面取得了重大进展。我们在过去已经证明，使用Gadgetron的基于云的图像重建可用于实现临床实用的图像重建时间。最近，我们已经证明，这种方法可以改善临床工作流程时，研究儿科患者。由于我们的目标是在世界各地的协作站点部署此类技术，因此我们对基础设施进行了改进，使其可以部署在几个主要的云提供商（Amazon AWS和Miscrosoft Azure）中。我们的新架构具有自动扩展以适应需求的能力，被称为Gadgetron灯塔。 我们还在纠正系统缺陷方面取得了重大进展。具体来说，几个步骤所需的梯度缺陷的校正已经实施，我们开始测试这些technqiues的实用程序，包括螺旋和径向轨迹的实时成像应用范围广泛。 在心肌灌注领域，我们已经取得了重大进展，实现了全自动，内联包定量心肌灌注。这是一个正在进行的广泛项目，我们的目标是在未来一年继续下去。

Background Medical imaging plays a major role in the diagnosis and treatment of cardiovascular disease. The process of generating a medical image consists of two parts; data acquisition and image reconstruction. Image reconstruction transforms the acquired raw data signals into images that can be interpreted by clinician to aid in the diagnosis of a disease or used to guide a procedure. The raw data is frequently corrupted by instrument imperfections and patient motion. It is also common for datasets to be incomplete since there is a limited amount of time, radiation dose, or other patient exposure available for data acquisition. Consequently, modern image reconstruction software is fairly complex. The source code for these complex image reconstruction algorithms is most often proprietary information retained by the medical device manufacturers and scientists working in the field of medical image reconstruction are forced to implement their own versions of existing algorithms that they then build on and improve. Because of this reimplementation and a lack of open standards, much of the published literature on medical image reconstruction is not reproducible. The overarching goal of this project is to develop novel image reconstruction algorithms and to do that in such a way that other scientists (and device manufacturers) can reproduce the presented results and use the methods in future work. The Laboratory of Imaging Technology, NHLBI is particularly focused on Magnetic Resonance Imaging (MRI) techniques, but the developed principles apply to other technqiques as well. Goals/Objectives The Laboratory of Imaging Technology developes and maintains two major software packages that support ongoing research projects. The first is the ISMRM Raw Data format (https://ismrmrd.github.io), which is an open raw data standard for MR experiments. It is a requirement for sharing algorithms and methods that there is common understanding of how to describe the raw data and this package provides the framework for this. We also aim to maintain data conversion tools from major device manufacturers proprietary formats to this vendor independent format. The second software package is the Gadgetron (https://gadgetron.github.io), which is an advance image reconstruction package that contains toolboxes and a streaming pipeline architecture for processing the raw data that is acquired by the imaging instrument. We aim to expand this software package and support the growing user base around the world. The are a number of technical innovations are we are currently pursuing: * Expansion of the ISMRMRD format to include waveforms and telemetry from other instruments. * Formal definition and implementation of an ISMRMRD communication protocol. * The use of cloud computing for MRI reconstruction. * MRI raw data compression. * Correct of measurment system imperfections. * Tight integration of the Gadgetron with specific vendor instruments. In addition to these infrastructure goals, we are developing and testing new image reconstruction techniques to solve specific clinical questions: * Real-time imaging sequence for interventional MRI. * Real-time measurements of blood flow. * Motion corrected, free-breathing techniques for measuring cardiac function and parametric maps. * Quantitative assessment of myocardial perfusion. Progress in fiscal year 2016 In the past year, we have made significant progress in our approach to cloud based MRI reconstruction. We have demonstrated in the past that cloud based image reconstruction using the Gadgetron can be used to achieve clinically practical image reconstruction times. More recently, we have demonstrated that such an approch can improve the clinical workflow when studying pediatric patients. As we aim to deploy such technology at collaboration sites across the world, we made improvements to the infrastructure such that it can be deployed in several major cloud providers (Amazon AWS and Miscrosoft Azure). Our new architecture which has the capability to scale automatically to accomodate demand is know as the Gadgetron Lighthouse. We have also made significant progress in the area of system imperfection correction. Specifically, several steps needed for correction of gradient imperfections have been implemented and we are starting to test the utility of these technqiues for a wide range of real-time imaging applications including spiral and radial trajectories. In the area of myocardial perfusion, we have made significant progress towards a fully automated, inline package for quantitative myocardial perfusion. This is an expansive and ongoing project that we aim to continue in the coming year.