Visual Search in 3D Medical Imaging Modalities

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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.