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

颅内动脉粥样硬化病(ICAD)可导致缺血性中风，越来越多的证据表明 ICAD，即使在没有狭窄的情况下，也与来源不明的栓塞性卒中(ESUS)有关。船舶 脑血管的壁磁共振成像(MRI)对诊断的需求越来越大 这样的ESU患者才能得到适当的治疗。多对比成像颅内血管壁 (IVW)具有有效分析的磁共振成像目前被美国神经放射学会推荐用于 诊断包括ICAD在内的各种血管壁病变。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(飞利浦的Vista、西门子的SPACE和 GE上的立方体)，临床医生还没有有效的方法来分析多对比IVW MRI 可以在现代临床核磁共振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启用多中心研究，以便在ESU中进行ICAD评估。 序列实施的可变性会影响多个扫描仪平台上的多中心研究，并且必须 克服这一点，以实现可靠的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程序管道。我们 我开创了多种半自动方法(3D配准、动脉跟踪、动脉标记、多平面 重新格式化、血管壁分割、多对比特征识别)。 利用这一专业知识，我们将开发一种新的人工智能(AI)支持的多平面观看 动脉(AI-MOCHA)管道的特征如下：在目标1中，我们将建设MOCHA管道并进行训练 使用标记IVW图像的转移学习的AI-MOCHA，并针对放射科医生标记的IVW测试AI-MOCHA 来自ICAD患者。我们还将测试AI-MOCHA是否提高了扫描间和读取器间的重复性 IVW图像分析。在目标2中，我们将开发域自适应，以克服扫描仪平台在 IVW图像并开发了一种领域自适应AI-MOCHA。然后我们将展示领域自适应AI-MOCHA 改进了基于AI-MOCHA的IVW图像分析的扫描间和读卡器间的重复性。在《目标3》中，我们将 检验AI-MOCHA在血管领域更频繁地检测到非狭窄ICAD的假设 使用领域自适应AI-MOCHA的ESU比在其他地区的ESU更多。为此，我们将扫描65个ESU 受试者分别在飞利浦、西门子和GE 3T扫描仪平台上的多中心设置(三种不同 医院)，并展示了领域自适应AI-MOCHA在稳健和高效的IVW分析中的实用性。在……里面 这样，我们不仅将确立非狭窄icad在esus中的重要性，而且还将在临床上开发一种 用于监测ICAD进展的适用IVW分析流水线，将有助于优化医疗治疗 在个别病人身上。此外，该管道将提供一种快速、可靠的工具来识别哪些患者没有 对参与临床试验的传统疗法作出反应。

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.