Visual adaptation to medical images : a comparison of digital mammography and tomosynthesis

© 2023 Society of Photo-Optical Instrumentation Engineers (SPIE)..

Purpose: Radiologists and other image readers spend prolonged periods inspecting medical images. The visual system can rapidly adapt or adjust sensitivity to the images that an observer is currently viewing, and previous studies have demonstrated that this can lead to pronounced changes in the perception of mammogram images. We compared these adaptation effects for images from different imaging modalities to explore both general and modality-specific consequences of adaptation in medical image perception.

Approach: We measured perceptual changes induced by adaptation to images acquired by digital mammography (DM) or digital breast tomosynthesis (DBT), which have both similar and distinct textural properties. Participants (nonradiologists) adapted to images from the same patient acquired from each modality or for different patients with American College of Radiology-Breast Imaging Reporting and Data System (BI-RADS) classification of dense or fatty tissue. The participants then judged the appearance of composite images formed by blending the two adapting images (i.e., DM versus DBT or dense versus fatty in each modality).

Results: Adaptation to either modality produced similar significant shifts in the perception of dense and fatty textures, reducing the salience of the adapted component in the test images. In side-by-side judgments, a modality-specific adaptation effect was not observed. However, when the images were directly fixated during adaptation and testing, so that the textural differences between the modalities were more visible, significantly different changes in the sensitivity to the noise in the images were observed.

Conclusions: These results confirm that observers can readily adapt to the visual properties or spatial textures of medical images in ways that can bias their perception of the images, and that adaptation can also be selective for the distinctive visual features of images acquired by different modalities.

Medienart:

E-Artikel

Erscheinungsjahr:

2023

Erschienen:

2023

Enthalten in:

Zur Gesamtaufnahme - volume:10

Enthalten in:

Journal of medical imaging (Bellingham, Wash.) - 10(2023), Suppl 1 vom: 30. Feb., Seite S11909

Sprache:

Englisch

Beteiligte Personen:

Parthasarathy, Mohana Kuppuswamy [VerfasserIn]
Zuley, Margarita L [VerfasserIn]
Bandos, Andriy I [VerfasserIn]
Abbey, Craig K [VerfasserIn]
Webster, Michael A [VerfasserIn]

Links:

Volltext

Themen:

Adaptation
Journal Article
Mammography
Noise perception
Texture perception
Tomosynthesis

Anmerkungen:

Date Revised 26.04.2024

published: Print-Electronic

Citation Status PubMed-not-MEDLINE

doi:

10.1117/1.JMI.10.S1.S11909

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM35617767X