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

用于多对比多平台应用的自动化颅内血管壁分析管道

• 批准号: 10451951
• 负责人: Mahmud Mossa-Basha
• 金额: $ 57.86万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10451951
• 关键词: 3-Dimensional; Adopted; Adoption; Affect; American; Angiography; Arterial Fatty Streak; Arteries; Artificial Intelligence; Atherosclerosis; Blood Vessels; Brain; Brain Infarction; Characteristics; Clinical; Clinical Research; Computer software; Computers; Consumption; Data; Detection; Development; Diagnosis; Disease; Disease Progression; Embolism; High Prevalence; Hospitals; Image; Image Analysis; Intracranial Atherosclerotic Disease; Ischemic Stroke; Knowledge; Label; Lacunar Infarctions; Lead; Lesion; Magnetic Resonance Imaging; Measurement; Measures; Medical; Methods; Modernization; Monitor; Morphologic artifacts; Multicenter Studies; Neurological outcome; Pathology; Patients; Pattern; Performance; Process; Protocols documentation; Public Health; Reader; Reproducibility; Resolution; Role; Scanning; Scheme; Signal Transduction; Societies; Source; Stenosis; Stroke; Structure; Symptoms; Techniques; Testing; Therapy Clinical Trials; Three-dimensional analysis; Time; Training; Trees; Vendor; Weight; adaptive learning; analysis pipeline; artificial intelligence algorithm; automated analysis; base; brain magnetic resonance imaging; burden of illness; cerebrovascular; clinical application; clinical decision-making; clinical diagnosis; clinical practice; conventional therapy; deep learning; dementia risk; disease diagnosis; embolic stroke; empowered; experience; feeding; image registration; improved; individual patient; intracranial artery; machine learning model; novel; prevent; radiologist; screening; systematic review; tool; transfer learning

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.

颅内动脉粥样硬化疾病（ICAD）可能导致缺血性中风，并且有越来越多的证据表明 ICAD即使在没有狭窄的情况下，ICAD也与不确定的来源（ESU）的栓塞卒中有关。血管 颅内脉管系统的壁磁共振成像（MRI）越来越需要诊断 这样的ESU患者可以进行适当的治疗。多对比颅内容器 （IVW）目前，美国神经放射学会建议进行有效分析的MRI 诊断包括ICAD在内的各种血管壁病理。虽然对IVW的迫切需求刺激了 在主要扫描仪平台上的3D自旋回声序列的可用性（飞利浦上的Vista，西门子的空间和 Cube在GE上），临床医生没有有效的方法来分析多对比度IVW MRI 可以在现代临床MRI扫描仪上获得的序列。容器壁的定量测量 还需要整个扫描仪平台来启用多中心研究以进行ESU中的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，必须是 克服以实现强大的IVW测量。因此，我们建议开发自动IVW分析 使用域自适应和深度学习方法的多对比多平台应用程序的管道。我们 已经开创了多种半自动方法（3D注册，动脉追踪，动脉标记，多平面 重新格式化，容器壁分割，多对比度特征识别）朝止容器壁定量。 利用这种专业知识，我们将开发一种新颖的人工智能（AI）授权多平台观看 动脉表征（AI-Mocha）管道如下：在AIM 1中，我们将构建摩卡管道和训练 AI-MOCHA使用从标记的IVW图像进行转移学习，并针对标有IVW的放射科医师测试AI-Mocha 来自ICAD患者。我们还将测试AI-Mocha是否改善了扫描间和读取器的可重复性 IVW图像分析。在AIM 2中，我们将开发适应域以克服扫描仪平台的差异 IVW图像并开发域自适应AI-Mocha。然后，我们将显示域自适应AI-Mocha 改善IVW图像分析在AI-Mocha上的扫描间和阅读器间可重复性。在AIM 3中，我们将 检验以下假设，即AI-Mocha在血管领域中更常见非静态ICAD。 ESU比使用域自适应AI-Mocha在其他领土上。为了实现这一目标，我们将扫描65 ESU 在多中心设置中 医院）并演示了域自适应AI-Mocha的实用性，以进行稳健有效的IVW分析。在 这样做，我们不仅会确定非静态ICAD在ESU中的重要性，而且还将在临床上发展 适用的IVW分析管道，用于监测ICAD进展，这将有助于优化医疗疗法 在个别患者中。此外，管道将提供一种快速，可靠的工具，用于识别未识别的患者 应对临床试验参与的常规疗法。