# Visual Search in 3D Medical Imaging Modalities

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA SANTA BARBARA · 2020 · $340,035

## Abstract

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.

## Key facts

- **NIH application ID:** 9977201
- **Project number:** 5R01EB026427-03
- **Recipient organization:** UNIVERSITY OF CALIFORNIA SANTA BARBARA
- **Principal Investigator:** Miguel Patricio Eckstein
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $340,035
- **Award type:** 5
- **Project period:** 2018-09-15 → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9977201

## Citation

> US National Institutes of Health, RePORTER application 9977201, Visual Search in 3D Medical Imaging Modalities (5R01EB026427-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9977201. Licensed CC0.

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