Visual Search in 3D Medical Imaging Modalities

3D 医学成像模式中的视觉搜索

• 批准号: 9977201
• 负责人: Miguel Patricio Eckstein
• 金额: $ 34万
• 依托单位: UNIVERSITY OF CALIFORNIA SANTA BARBARA
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-15 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9977201
• 关键词: 3-Dimensional; Adopted; Adoption; Area; Back; Benchmarking; Breast Microcalcification; Cancer Detection; Cancer Etiology; Categories; Cessation of life; Clinic; Clinical; Complement; Computer Models; Data; Death Rate; Detection; Diagnostic Errors; Digital Breast Tomosynthesis; Early Diagnosis; Ensure; Evaluation; Eye; Eye Movements; Goals; Human; Image; Imaging technology; Individual; Knowledge; Lead; Lesion; Malignant Neoplasms; Malignant neoplasm of lung; Mammographic screening; Mammography; Medical Imaging; Methodology; Modeling; Modernization; Noise; Pattern; Perception; Performance; Peripheral; Positioning Attribute; Procedures; Psychophysics; Radiology Specialty; Reading; Role; Scanning; Signal Transduction; Slice; Source; Specific qualifier value; Techniques; Technology; Three Dimensional Medical Imaging; Three-Dimensional Image; Three-Dimensional Imaging; Time; Training; Vision; Visual; Visual system structure; Woman; Work; breast imaging; breast lesion; clinical imaging; clinically relevant; computerized tools; design; digital; gaze; imaging modality; malignant breast neoplasm; new technology; programs; radiologist; real time monitoring; sample fixation; three-dimensional modeling; tool; vision science; visual processing; visual search; visual tracking

Project Summary Early detection through screening mammography has decreased death rates from breast cancer. There are approximately 39 million mammogram procedures conducted each year in US. However, there are still alarmingly high error rates in radiological interpretations, with missed cancer rates ranging from 10-18 percent and false positive rates as high as 67% over a 10-year period. In order to reduce errors rates, digital breast tomosynthesis, a new 3D imaging technology intended to make cancers more visible to the radiologist, is rapidly being introduced throughout clinics in the US. However, there is no thorough understanding of the potential impact of these new 3D imaging technologies on radiological errors, and no knowledge of what eye movement strategies should be used by radiologists to minimize errors when searching through these volumes, while keeping manageable reading times. The current proposal combines expertise in medical image perception and state of the art vision science to increase the theoretical and empirical understanding of 3D search. To achieve such goal we aim: a) To understand how the types of errors detecting masses and microcalcifications are impacted by 3D search in digital breast tomosynthesis images; b) To gain an understanding of the functional impact on errors of adopting different eye movement strategies to search through 3D volumes; c) To develop a computational model of 3D search that includes foveated visual processing, scanning and drilling. The model will be used to assess the adequacy and efficiency of different eye movement strategies and to identify potential suboptimalities associated with an individual’s eye movement strategies or visual capabilities in the visual periphery. The psychophysical studies, eye tracking and computational models will be initially developed with trained non-radiologists, filtered noise and digital breast tomosynthesis phantoms. Subsequently, the findings and model will be validated with radiologists and real clinical images. If successful, the proposed studies will provide a new theoretical understanding of the types of radiological errors that occur and the functional role of search patterns on 3D search with digital breast tomosynthesis images, and provide computational tools to assess whether a radiologist’s eye movement patterns are well matched to their detection capabilities in their visual peripheral. Together, these advances can potentially help reduce errors in cancer detection. Although the proposed methodology is in the context of breast cancer and digital breast tomosynthesis, the principles investigated are potentially applicable to other areas of 3D medical images in radiology.

项目摘要 通过筛查乳房摄影的早期发现使乳腺癌的死亡率恶化。有 每年在美国进行大约3900万个乳房X线照片程序。但是，仍然有 令人震惊的是放射学解释中的错误率，癌症率较高，范围为10-18个百分比 在10年期间，假阳性率高达67％。为了降低错误率，数字乳房 Tomosynthesis是一种新的3D成像技术，旨在使癌症对放射科医生更为明显，是 迅速被美国整个诊所引入。但是，对 这些新的3D成像技术对放射学错误的潜在影响，也不知道什么眼 放射科医生应使用运动策略来最大程度地减少搜索错误 数量，同时保留可管理的阅读时间。当前的建议结合了医学图像的专业知识 艺术愿景科学的感知和状态，以提高对3D的理论和经验理解 搜索。为了实现这样的目标，我们的目标是：a）了解错误的类型如何检测群众和 微钙化受到数字乳房断层图像中的3D搜索的影响； b）获得 理解功能对采用不同眼动策略搜索的错误的影响 通过3D卷； c）开发一个3D搜索的计算模型，其中包括foveated Visual 处理，扫描和钻探。该模型将用于评估不同的适当性和效率 眼动策略，并确定与个人眼睛相关的潜在次级优势 视觉外围的运动策略或视觉功能。心理物理研究，眼睛跟踪 最初将通过训练有素的非降级学家，过滤噪声和数字化开发计算模型 乳房间压幻象。随后，发现和模型将被放射学家和 真实的临床图像。如果成功，拟议的研究将为您提供新的理论理解 出现的放射学错误的类型以及搜索模式在3D搜索中使用数字乳房的功能作用 Tomosynthesis图像，并提供计算工具来评估放射科医生的眼动 图案与其视觉外围的检测功能非常匹配。这些进步在一起 可能有助于减少癌症检测的错误。尽管提出的方法是在 乳腺癌和数字乳房合成，研究原理可能适用于其他 放射学中3D医学图像的区域。