Visual Search in 3D Medical Imaging Modalities

项目摘要 通过筛查乳房X光检查的早期发现降低了乳腺癌的死亡率。确实有 在美国，每年大约有3900万例乳房X光检查。然而，仍然有 放射学解释的错误率高得惊人，漏诊率从10%到18%不等 在10年内，假阳性率高达67%。为了降低错误率，数字乳房 断层合成是一种新的3D成像技术，旨在使放射科医生更容易看到癌症。 在美国的诊所里迅速推广。然而，目前还没有对 这些新的3D成像技术对放射误差的潜在影响，并且不知道是什么眼睛 放射科医生应使用移动策略，以便在搜索时将错误降至最低 书卷，同时保持可管理的阅读时间。目前的方案结合了医学影像方面的专业知识。 感知和最先进的视觉科学，以增加对3D的理论和经验理解 搜索。为了实现这一目标，我们的目标是：a)了解检测肿块的错误类型和 数字乳腺断层合成图像中的微钙化受3D搜索的影响；b)为了获得 不同眼动策略搜索对错误功能影响的研究 通过3D体积；c)开发包括凹陷视觉的3D搜索的计算模型 加工、扫描和钻孔。该模型将被用来评估不同的 眼动策略和识别与个体眼睛相关的潜在次最佳状态 视觉外周的运动策略或视觉能力。心理物理研究，眼球跟踪 计算模型最初将与训练有素的非放射科医生、过滤噪声和数字技术一起开发 乳房断层合成幻影。随后，这些发现和模型将与放射科医生和 真实的临床图像。如果成功，建议的研究将提供一个新的理论理解 数字乳房3D搜索中出现的放射学错误类型和搜索模式的功能作用 断层合成图像，并提供计算工具来评估放射科医生的眼球运动 图案与它们在视觉外设中的检测能力很好地匹配。总而言之，这些进步 可能有助于减少癌症检测中的错误。尽管提议的方法是在 乳腺癌和数字乳腺断层合成，所研究的原理可能适用于其他 放射学中的3D医学图像领域。

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.