Assessment of medical image quality with foveated search models

 描述(由申请人提供)：医学图像质量可以根据临床相关感知任务中的诊断决策准确性进行客观定义。由于使用临床研究评估图像质量的成本和工作量很高，特别是在早期技术开发中，一直在努力开发可应用于图像的数值算法(模型观察者)，以预测人类在与临床相关的感知任务中的准确性。近年来，模型观察员已从实验室调查过渡到用于该行业技术开发和制造商图像质量评估的实际工具，以寻求食品和药物管理局的批准。然而，最近3D医学图像(计算机断层扫描、乳房断层成像、磁共振)的使用增加了对下一代模型观察者的开发的需求。目前的模型观察者的一个基本局限性是，他们忽略了人脑从注视的角度处理空间分辨率降低的图像。有了3D数据集，放射科医生很少穷尽地固定每个区域 切片；取而代之的是，他们用视网膜外围处理图像的很大一部分 截然不同的视觉处理。计算机能力的增强和对视觉搜索计算神经科学的理解的最新进展为开发下一代模型观察者提供了机会，该模型观察者可能更准确地表征放射科医生如何仔细检查医学图像，以及他们的决策准确性和错误。目前的项目建议开发第一个模型观察器来模拟放射科医生，通过在人类视野中处理具有不同空间处理分辨率的医学图像，通过模拟眼动来搜索图像，并通过跨注视的整合来做出决定。凹陷搜索模型使眼球运动与医学成像中任何以前的模型观察者不同，它将是第一个模仿放射科医生犯下两种不同类型错误的模型：搜索错误(未固定的遗漏病变)感知错误(已固定的遗漏病变)。阅读数字乳房断层合成(DBT)图像的20多名放射科医生的决定和眼球运动将与新提出的凹陷搜索模型和现有的非扫描和扫描模型观察者的全面名单进行比较，这将是迄今为止最广泛的模型观察者与放射科医生的决定的验证研究。新提出的模型将被用来优化DBT采集几何结构，并与目前使用的医学图像质量指标进行比较。如果成功，新提出的凹陷搜索模型将允许对医学图像质量进行更准确的评估，可以用于加快新技术的评估，优化现有技术的参数，并更好地了解放射科医生如何搜索和做出诊断决策。

 DESCRIPTION (provided by applicant): Medical image quality can be objectively defined in terms of diagnostic decision accuracy in clinically relevant perceptual tasks. Because of the high cost and effort involved in evaluating image quality using clinical studies, especially in early technological developments, there has been an ongoing effort to develop numerical algorithms (model observers) that can be applied to images to predict human accuracy in clinically relevant perceptual tasks. In recent years model observers have transitioned from laboratory investigations to actual tools used in technology development in the industry and for image quality evaluation by manufacturers to seek approval from the Food and Drug Administration. However, the recent increase of the use of 3D medical images (computed tomography, breast tomosynthesis, magnetic resonance) has motivated a need for the development of the next generation of model observers. A fundamental limitation of current model observers is that they disregard that the human brain processes an image with decreasing spatial resolution from the point of fixation. With 3D data-sets, radiologists rarely exhaustively fixate every region of every slice; instead, they process a significant portion of images with their retinal periphery which has drastically different visual processing. Increased computer power and recent advances in the understanding of the computational neuroscience of visual search provide the opportunity to develop the next generation model observers which potentially can more accurately characterize how radiologists scrutinize medical images, as well as their decision accuracy and errors. The current project proposes to develop the 1st model observer to emulate radiologists by processing medical images with varying spatial processing resolution across the human visual field, searching through the image with simulated eye movements, and reaching a decision through integration across fixations. The foveated search model, which makes eye movements unlike any previous model observer in medical imaging, will be the 1st model to emulate radiologists in making two distinct types of errors: search errors ( missed lesions that are not fixated) perceptual errors (missed lesion that are fixated). The decisions and eye movements of over twenty radiologists reading digital breast tomosynthesis (DBT) images will be compared to the newly proposed foveated search model and a comprehensive list of existing non-scanning and scanning model observers in what will represent the most extensive validation study to date of model observers with actual radiologists' decisions. The newly proposed model will be utilized to optimize DBT acquisition geometry and compared to use of current metrics of medical image quality. If successful, the newly proposed foveated search model will allow for more accuracy assessment of medical image quality, could be utilized to accelerate the evaluation of new technology, optimize parameters of current technology and gain a better understanding how radiologists search and reach diagnostic decisions.