Optimizing MRI for Neurologic Screening using Radiologist Crowdsourcing

摘要

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.