Optimizing MRI for Neurologic Screening using Radiologist Crowdsourcing

摘要 在过去的几十年里，表现为急性神经系统疾病的患者的诊断和治疗 由于新的治疗方法和成像技术的兴起，症状已经大大恶化 对病人进行分流治疗。目前，成像信息主要由CT提供，CT提供 大血管闭塞和组织灌注的描述。磁共振成像还提供了根本不能提供的对比 可从CT获得，对组织损伤和中风模拟的描绘要好得多。在这一点上，最近的研究 在紧急情况下使用核磁共振作为一线诊断工具已显示出比 CT的使用。不幸的是，磁共振成像在急诊和筛查应用中的使用受到其高度限制 延长成像时间。这一时间增加了成本，延误了及时治疗，并使扫描变得对大块和 生理运动。虽然先前已经提出了各种技术来加速MRI获取， 颠覆性和范式转换的深度学习图像重建技术目前正在开发中 提供前所未有的扫描时间缩短。因此，这项技术有可能将MRI转变为 能够快速筛查神经性疾病的模式。然而，深度学习图像重建 需要性能指标来设置重建过程中可能丢失的信息(例如噪声、失真) 以及必须保留哪些信息(例如对比度、分辨率、成像特征)。常见性能 度量是重建图像与地面的均方误差(MSE)或结构相似度(SSIM 尽管如此，众所周知，这些基于工程的度量标准往往不能很好地反映放射学图像。 质量。这个项目的总体目标是开发一种放射效果最佳的、五分钟的、多对比度的深度 学习MRI快速筛查方案。我们的具体目标是开发探测和 将放射学偏好纳入基于深度学习的MRI重建。为了实现这一目标，我们将 开发从人类观察者对不同损坏的MRI进行排序来探测图像偏好的方法 同一主题的图像。通过众包排名研究，我们的目标是调查在 在多项任务中，专家和非专家观察员之间的感知图像质量，并参考 基于工程的指标。随后，将使用来自专家放射科医生排名的数据来训练图像 接近放射科医生喜好的感知模型。该模型将用于优化 多对比神经学筛查方案的采样模式和重建，该方案将在 一项试验性人体研究，比较了深度学习协议和使用传统方法的简化协议 方法：研究方法。该项目的成功完成将提供一种能够及时提供快速MRI检查的方法 以及用于神经学筛查的相关信息。它将进一步提高我们对放射成像的理解。 质量感知及其在深度学习加速磁共振成像发展中的作用。

ABSTRACT Over the past several decades, the diagnosis and treatment of patients presenting with acute neurologic symptoms has advanced tremendously due to new therapies and the rise of imaging techniques as means to triage patients for treatment. Currently, imaging information is predominately provided by CT which provides depiction of large vessel occlusions and tissue perfusion. MRI additionally provides contrasts that are simply not available from CT and a far superior depiction of tissue damage and stroke mimics. To this point, recent studies using MRI as a frontline diagnostic tool in the emergency setting have demonstrated improved outcomes over the use of CT. Unfortunately, the use of MRI in emergency and screening applications is highly limited by its extended imaging time. This time increases costs, delays timely treatment, and sensitizes the scan to bulk and physiologic motion. While a variety of techniques have been previously proposed to accelerate MRI acquisitions, disruptive and paradigm shifting deep learning image reconstruction technology is currently being developed offering unprecedented reductions in scan times. This technology thus holds potential to transform MRI into a modality capable of rapid screening for neurologic disorders. However, deep learning image reconstructions require a performance metric to set what information can be lost across the reconstruction (e.g. noise, distortions) and what information must be retained (e.g. contrast, resolution, imaging features). Common performance metrics are the mean squared error (MSE) or structural similarity (SSIM) of reconstructed images with a ground truth; though, it is well known that these engineering-based metrics are often poor reflections of radiologic image quality. The overall goal of this project is to develop a radiologically optimal, five-minute, multi-contrast deep learning accelerated MRI screening protocol. We specifically aim to develop methods for probing and incorporating radiologic preference into deep learning based MRI reconstructions. To achieve this, we will develop methodology to probe image preference from human observer ranking of differentially corrupted MRI images of the same subject. Through crowdsourced ranking studies, we aim to investigate differences in perceived image quality between expert and non-expert observers, among multiple tasks, and in reference to engineering based metrics. Subsequently, data from the expert radiologist ranking will be used to train an image perception model that approximates the radiologist’s preferences. This model will be used to optimize the sampling patterns and reconstruction for a multi-contrast neurologic screen protocol, which will be evaluated in a pilot human subject study comparing the deep learning protocol to an abbreviated protocol using traditional methods. The successful completion of this project will provide a rapid MRI method capable of providing timely and relevant information for neurologic screening. It will further improve our understanding of radiologic imaging quality perception and its role in the development of deep learning accelerated MRI.