Automated Intracranial Vessel Wall Analysis Pipeline for Multi-contrast Multi-platform Applications

颅内动脉粥样硬化性疾病（ICAD）可导致缺血性卒中，越来越多的证据表明， 即使没有狭窄，ICAD也与不明来源的栓塞性卒中（ESUS）有关。容器 越来越需要颅内血管壁磁共振成像（MRI）来诊断 这样的ESUS患者，以便可以给予适当的治疗。多对比颅内血管壁 (IVW)具有有效分析的MRI目前由美国神经放射学会推荐， 诊断各种血管壁病变，包括ICAD。虽然对IVW的迫切需求刺激了 3D自旋回波序列在主要扫描仪平台上的可用性（Philips上的VISTA、Siemens上的SPACE和 CUBE on GE），临床医生还没有有效的方法来分析多对比IVW MRI 可以在现代临床MRI扫描仪上获得的序列。血管壁的定量测量 还需要跨扫描仪平台进行多中心研究，以便在ESUS中进行ICAD评估。 序列实施的可变性影响多个扫描仪平台上的多中心研究，必须 以实现稳健的IVW测量。因此，我们建议开发一个自动化的IVW分析 使用领域自适应和深度学习方法的多对比度多平台应用的流水线。我们 已经开创了多种半自动方法（3D配准、动脉追踪、动脉标记、多平面 重新格式化、血管壁分割、多对比度特征识别）。 利用这一专业知识，我们将开发一种新型的人工智能（AI）支持的多平面观看， 动脉表征（AI-MOCHA）管道如下：在目标1中，我们将构建MOCHA管道并训练 AI-MOCHA使用来自标记的IVW图像的迁移学习，并针对放射科医生标记的IVW测试AI-MOCHA ICAD患者我们还将测试AI-MOCHA是否提高了扫描间和阅读器间的再现性 IVW图像分析在目标2中，我们将开发领域自适应，以克服扫描器平台的差异， IVW图像和开发领域自适应AI-MOCHA。然后，我们将证明域自适应AI-MOCHA 提高了IVW图像分析在AI-MOCHA上的扫描间和读取器间的再现性。在目标3中，我们 检验以下假设，即AI-MOCHA在以下血管区域更常检测到非狭窄性ICAD： ESUS比其他地区使用域自适应AI-MOCHA。为了实现这一目标，我们将扫描65个ESUS 在多中心设置中，在Philips、Siemens和GE 3 T扫描仪平台上的每个受试者（三种不同的 医院），并证明了域自适应AI-MOCHA的实用性，用于强大和有效的IVW分析。在 这样做，我们不仅将建立非狭窄ICAD在ESUS中的重要性，而且还将开发一种临床 适用的IVW分析管道，用于监测ICAD进展，这将有助于优化药物治疗 在个别患者中。此外，该管道将提供一种快速，可靠的工具，用于识别不需要的患者。 对参与临床试验的常规治疗有反应。

Intracranial atherosclerotic disease (ICAD) can lead to ischemic stroke and there is increasing evidence that ICAD, even in the absence of stenosis, is associated with embolic stroke of undetermined source (ESUS). Vessel wall magnetic resonance imaging (MRI) of the intracranial vasculature is increasingly in demand to diagnose such ESUS patients so that appropriate treatment can be administered. Multi-contrast intracranial vessel wall (IVW) MRI with efficient analysis is currently recommended by the American Society of Neuroradiology to diagnose various vessel wall pathologies including ICAD. While this urgent need for IVW has stimulated availability of 3D spin echo sequences on major scanner platforms (VISTA on Philips, SPACE on Siemens and CUBE on GE), there are no efficient and effective methods for clinicians to analyze the multi-contrast IVW MRI sequences that can be obtained on modern clinical MRI scanners. Quantitative measurements of the vessel wall across scanner platforms are also required to enable multi-center studies for ICAD assessment in ESUS. Variability in sequence implementation affects multi-center studies on multiple scanner platforms and must be overcome to enable robust IVW measurements. Therefore, we propose to develop an automated IVW analysis pipeline for multi-contrast multi-platform application using a domain adaptive and deep learning approach. We have pioneered multiple semiautomatic approaches (3D-registration, artery tracing, artery labeling, multi-planar reformatting, vessel wall segmentation, multi-contrast feature identification) towards vessel wall quantification. Leveraging this expertise, we will develop a novel artificial intelligence (AI) empowered multiplanar viewing for artery characterization (AI-MOCHA) pipeline as follows: In Aim 1 we will construct the MOCHA pipeline and train AI-MOCHA using transfer learning from labeled IVW images and test AI-MOCHA against radiologist labeled IVW from ICAD patients. We will also test whether AI-MOCHA improves the inter-scan and inter-reader reproducibility of IVW image analysis. In Aim 2 we will develop domain adaptation to overcome scanner-platform differences in IVW images and develop a Domain Adaptive AI-MOCHA. We will then show that domain adaptive AI-MOCHA improves the inter-scan and inter-reader reproducibility of IVW image analysis over AI-MOCHA. In Aim 3, we will test the hypothesis that non-stenotic ICAD is more frequently detected by AI-MOCHA in the vascular territory of ESUS than in other territories using Domain Adaptive AI-MOCHA. To achieve this, we will scan 65 ESUS subjects each on the Philips, Siemens and GE 3T scanner platforms in a multi-center setting (three different hospitals) and demonstrate the utility of domain adaptive AI-MOCHA for robust and efficient IVW analysis. In doing so, we will not only establish the importance of non-stenotic ICAD in ESUS but also develop a clinically applicable IVW analysis pipeline for monitoring ICAD progression that will assist in optimizing medical therapies in individual patients. Further, the pipeline will provide a rapid, reliable tool for identifying patients who do not respond to conventional therapies for clinical trial participation.