Optimizing MRI for Neurologic Screening using Radiologist Crowdsourcing

利用放射科医生众包优化 MRI 进行神经系统筛查

• 批准号: 10527680
• 负责人: Kevin Michael Johnson
• 金额: $ 41.43万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10527680
• 关键词: Acceleration; Accident and Emergency department; Acute; Address; Alteplase; Angiography; Brain; Clinical; Computer Systems; Data; Dependence; Development; Devices; Diagnosis; Diagnostic; Diffusion; Disease; Educational Background; Eligibility Determination; Emergency Situation; Engineering; Evaluation; Goals; Healthcare; Human; Image; Imaging Techniques; Knowledge; Learning; MRI Scans; Machine Learning; Magnetic Resonance Imaging; Medical center; Methodology; Methods; Modality; Modeling; Motion; Network-based; Neurologic; Neurologic Symptoms; Noise; Patient Triage; Patients; Pattern; Perception; Performance; Perfusion; Physiological; Protocols documentation; Radiology Specialty; Rapid screening; Research; Resolution; Role; Sampling; Scanning; Speed; Stroke; Study Subject; System; Techniques; Technology; Testing; Time; Tissues; Training; X-Ray Computed Tomography; acute stroke; base; cohort; convolutional neural network; cost; crowdsourcing; deep learning; demographics; diagnostic tool; digital; emergency settings; endovascular thrombectomy; heuristics; high end computer; human subject; image reconstruction; imaging modality; imaging probe; improved; improved outcome; learning strategy; nervous system disorder; neural network; novel; novel therapeutics; portability; preference; prospective; radiological imaging; radiologist; reconstruction; screening; treatment planning

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.

抽象的 在过去的几十年里，急性神经系统疾病患者的诊断和治疗 由于新疗法和成像技术的兴起，症状已大大改善 目前，影像学信息主要由 CT 提供。 大血管闭塞和组织灌注的表现还提供了根本无法提供的对比。 可以从 CT 和组织损伤和中风模拟中获得更好的隐喻。 在紧急情况下使用 MRI 作为一线诊断工具已证明结果比 不幸的是，MRI 在紧急情况和筛查应用中的使用受到其自身的严重限制。 延长成像时间会增加成本，延迟及时治疗，并使扫描对批量和敏感度敏感。 虽然之前已经提出了多种技术来加速 MRI 采集， 目前正在开发颠覆性和范式转变的深度学习图像重建技术 该技术可前所未有地缩短扫描时间，因此具有将 MRI 转变为 MRI 的潜力。 然而，深度学习图像重建能够快速筛查神经系统疾病。 需要一个性能指标来设置在重建过程中可能丢失哪些信息（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）。 指标是具有地面的重建图像的均方误差（MSE）或结构相似度（SSIM） 但事实是，众所周知，这些基于工程的指标通常不能很好地反映放射图像。 该项目的总体目标是开发放射学上最佳的五分钟多重对比深度。 我们的具体目标是开发探测和加速 MRI 筛查方法。 将放射学偏好纳入基于深度学习的 MRI 重建中。 开发方法来根据人类观察者对不同损坏的 MRI 的排名来探究图像偏好 通过众包排名研究，我们旨在调查同一主题的图像之间的差异。 在多个任务中，专家和非专家观察者之间感知的图像质量，并参考 随后，来自专家放射科医生排名的数据将用于训练图像。 近似放射科医生偏好的感知模型 该模型将用于优化 多对比神经系统筛查方案的采样模式和重建，将在 一项试点人类受试者研究，将深度学习协议与使用传统方法的简化协议进行比较 该项目的成功完成将提供一种能够及时提供快速MRI方法。 以及神经系统筛查的相关信息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 MRI 发展中的作用。