Assessment of medical image quality with foveated search models

使用中心点搜索模型评估医学图像质量

• 批准号: 8889132
• 负责人: Miguel Patricio Eckstein
• 金额: $ 42.37万
• 依托单位: UNIVERSITY OF CALIFORNIA SANTA BARBARA
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-15 至 2019-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8889132
• 关键词: Algorithms; Anatomy; Brain; Breast; Breast Microcalcification; California; Clinical Research; Computers; Data Set; Detection; Development; Diagnostic; Digital Breast Tomosynthesis; Disease; Evaluation; Eye Movements; Generations; Geometry; Goals; Human; Hybrids; Image; Individual; Industry; Investigation; Laboratories; Lead; Lesion; Magnetic Resonance; Manufacturer Name; Measurement; Medical Imaging; Modeling; Neurosciences; Pennsylvania; Performance; Peripheral; Process; Protocols documentation; Psychophysics; Radiology Specialty; Reading; Resolution; Retinal; Rotation; Scanning; Slice; Technology; Texture; Three-Dimensional Image; Translating; United States Food and Drug Administration; Universities; University Hospitals; Visual; Visual Fields; Visual system structure; Woman; X-Ray Computed Tomography; base; clinically relevant; computational neuroscience; cost; density; image processing; improved; new technology; next generation; public health relevance; radiologist; sample fixation; technology development; tool; validation studies; virtual; visual process; visual processing; visual search

 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.

 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来客观定义。由于使用临床研究评估图像质量需要高昂的成本和精力，特别是在早期技术发展中，因此人们一直在努力开发可应用于图像的数值算法（模型观察者），以预测临床相关感知任务中的人类准确性。近年来，模型观察器已从实验室研究转变为行业技术开发中使用的实际工具，以及制造商用于图像质量评估以寻求食品和药物管理局批准的工具。然而，最近 3D 医学图像（计算机断层扫描、乳房断层合成、磁共振）使用的增加激发了开发下一代模型观察者的需求。当前模型观察者的一个基本限制是，他们忽视了人脑处理图像的空间分辨率从固定点开始逐渐降低。借助 3D 数据集，放射科医生很少能够详尽地固定每个区域的每个区域 片;相反，他们用视网膜周边处理大部分图像 截然不同的视觉处理。计算机能力的增强以及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，这些观察者可能可以更准确地描述放射科医生检查医学图像的方式及其决策准确性和错误。当前的项目建议开发第一个模型观察者来模拟放射科医生，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，通过模拟眼球运动搜索图像，并通过跨注视点的集成做出决策。中心凹搜索模型使眼球运动不同于医学成像中任何先前的模型观察者，这将是第一个模仿放射科医生犯两种不同类型错误的模型：搜索错误（遗漏未固定的病变）和感知错误（遗漏固定的病变）。超过 20 名放射科医生阅读数字乳腺断层合成 (DBT) 图像时的决策和眼球运动将与新提出的中心凹搜索模型以及现有非扫描和扫描模型观察者的综合列表进行比较，这将代表迄今为止模型观察者与实际放射科医生决策的最广泛的验证研究。新提出的模型将用于优化 DBT 采集几何结构，并与当前医学图像质量指标的使用进行比较。如果成功，新提出的中心点搜索模型将能够更准确地评估医学图像质量，可用于加速新技术的评估，优化当前技术的参数，并更好地了解放射科医生如何搜索并做出诊断决策。