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（由适用提供）：可以客观地定义医疗图像质量，以临床相关的感知任务中的诊断决策准确性。由于使用临床研究尤其是在早期技术发展中评估图像质量的成本和精力，因此持续不断地开发数值算法（模型观察者），这些算法可以应用于图像，以预测临床相关的知觉任务中人类的准确性。近年来，模型观察者已从实验室调查过渡到行业技术开发中使用的实际工具，以及制造商的图像质量评估，以寻求食品药品监督管理局的批准。然而，最近使用3D医学图像（计算机断层扫描，乳房合成，磁共振）的使用促使下一代模型观察者的发展需要。当前模型观察者的基本局限性是，他们无视人脑从固定点开始处理图像，而空间分辨率下降。使用3D数据集，放射科医生很少详尽地修复每个区域的每个区域 片;相反，他们使用具有永久外围的图像处理很大一部分 截然不同的视觉处理。增加计算机功率以及对视觉搜索计算神经科学的了解的最新进展为开发下一代模型观察者提供了机会，这些观察者有可能更准确地表征放射科医生如何审查医学图像以及他们的决策准确性和错误。当前的项目建议是开发第一个模型观察者，以通过处理整个人类视野各种空间处理分辨率的医学图像来模拟放射科医生，并通过模拟的眼动搜索图像，并通过跨固定的整合来实现决策。与以前的医学成像中的任何一个模型观察者不同，该模型将成为视力运动，它将是模拟放射科医生在造成两种不同类型的错误时的第一型模型：搜索错误（未固定的遗漏病变）感知错误（固定的遗漏病变固定）。二十多名放射科医生阅读数字乳房间隔（DBT）图像的眼睛运动将与新提出的FOVEATEAT搜索模型进行比较，并全面列出了现有的非扫描和扫描模型观察者，其中代表了迄今为止最广泛的验证研究，迄今为止，与实际放射学家的决策一样。新提出的模型将用于优化DBT采集几何形状，并与使用当前医学图像质量指标相比。如果成功，新提出的FOVEAT搜索模型将允许对医疗图像质量进行更准确的评估，可以利用加速对新技术的评估，优化当前技术的参数，并更好地了解放射科医生如何搜索和达到诊断决策。