A Machine Learning Approach to Classifying Time Since Stroke using Medical Imaging

项目总结/摘要 中风是美国死亡率和发病率的主要原因， 美国人每年经历一次新的或复发的中风。静脉注射组织型纤溶酶原激活剂（IV tPA）是主要的和最成熟的治疗选择，但它的使用仅适用于以下4.5小时内 中风不幸的是，高达30%的中风患者出现中风后时间未知（TSS）症状 发病，这使得他们没有资格接受IV tPA。这些人中的许多人可以免于严重的 发病率或死亡率，如果存在建立TSS的替代方法， 并接受治疗。该提案将开发机器学习方法，以创建一种基于生理学的方法 用于基于多参数磁共振（MR）和计算机断层扫描（CT）预测TSS 成像数据。我们相信，我们提出的技术将优于最先进的方法，是基于 主观图像解释，并有可能提供一个客观的数据点，可用于 结合专家的主观评估，或在缺乏卒中专业知识的临床环境中 成像 研究已经确定，MR和CT成像捕获与TSS相关的信息。然而，在这方面， 用于提取该信息的现有方法基于医生主观地解释图像 和划定感兴趣的区域，已经记录的过程只有弱到中度 经过培训的专家评审员之间的一致意见。一种自动化方法，全面分析 成像数据的频谱可以识别跨通道的复杂关系， TSS。例如，在MR中，扩散加权、灌注加权和流体衰减反转恢复 成像都在表征中风中发挥重要作用，但深入了解每个通道如何 如何组合来描述TSS是未知的。我们建议建立新的深度学习方法， 信息.具体来说，我们将：1）开发一个用于TSS分类的机器学习框架; 2）开发一个 深度卷积自动编码器，用于从MR和CT生成新的多模态图像表示， 改进分类; 3）实施可视化技术，阐明深层 特征和病理生理中风过程。在这个项目中，我们将使用来自UCLA和UCI的数据 中风中心，使我们能够研究不同的患者人群和成像技术。成功 这项研究的完成将提供一种新的方法，估计TSS从成像，导致新的 为未知TSS患者提供治疗的前瞻性试验。

PROJECT SUMMARY/ABSTRACT Stroke is a leading cause of mortality and morbidity in the United States, with approximately 795,000 Americans experiencing a new or recurrent stroke each year. Intravenous tissue plasminogen activator (IV tPA) is the dominant and most proven treatment option, but its use is only indicated within 4.5 hours following a stroke. Unfortunately, up to 30% of stroke patients present with an unknown time since stroke (TSS) symptom onset, which makes them ineligible to receive IV tPA. Many of these individuals could be spared severe morbidity or mortality if there existed an alternative method for establishing TSS, allowing them to be identified and treated. This proposal will develop machine learning methods to create a physiologically grounded method for predicting TSS based on multiparametric magnetic resonance (MR) and computed tomography (CT) imaging data. We believe our proposed techniques will outperform state-of-the-art methods that are based on subjective image interpretation, and have the potential to provide an objective data point that may be used in conjunction with the subjective assessments of experts, or in clinical environments that lack expertise in stroke imaging Research has established that MR and CT imaging captures information that correlates with TSS. However, existing methods for extracting this information are based on a physician subjectively interpreting the images and delineating regions of interest, processes that have been documented to have only weak to moderate agreement across trained expert reviewers. An automated approach that comprehensively analyzes the spectrum of imaging data could identify complex relationships across channels that more accurately classify TSS. For example, in MR, diffusion-weighted, perfusion-weighted, and fluid attenuated inversion recovery imaging all play important roles in characterizing a stroke, but a deep understanding of how each channel may be combined to describe TSS is unknown. We propose to establish new deep learning methods for fusing this information. Specifically, we will: 1) develop a machine learning framework for classifying TSS; 2) develop a deep convolutional autoencoder to generate novel multimodal image representations from MR and CT to improve classification; and 3) implement visualization techniques that elucidate the relationship between deep features and pathophysiological stroke processes. Under this project, we will use data from the UCLA and UCI Stroke Centers, allowing us to study different patient populations and imaging techniques. The successful completion of this research will provide a new method for estimating TSS from imaging, leading to new prospective trials for providing therapy to patients with unknown TSS.