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Updated: Oct 27, 2026
Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
Published on: August 30, 2013
Model-based image restoration targeting higher-dose digital mammography: Impact on radiologists' detection of
Renann F Brandão1, Lucas R Borges2, Silvia M P S Sabino3
1Department of Electrical and Computer Engineering, São Carlos School of Engineering, University of São Paulo, São Carlos, Brazil.
Background:
Image noise in digital mammography (DM) can obscure image details and reduce the detectability of clinically relevant findings such as microcalcifications.
Purpose:
To present and evaluate an extended model-based (MB) image restoration pipeline for DM that incorporates quantum, electronic, and structural noise components, as well as spatial noise correlation, and to assess its effect on breast radiologists' detection of simulated microcalcification clusters in clinical mammograms.
Methods:
A total of 120 clinical DM images acquired using a GE Senographe Pristina system were used. Simulated microcalcification clusters were inserted into locally dense fibroglandular regions at controlled contrast levels. The restoration pipeline was configured to target the signal and noise characteristics predicted by the model for an acquisition at approximately twice the original radiation dose. Six breast radiologists with different levels of experience, classified as Junior or Senior, participated in two human observer experiments: (i) a pilot study using original clinical images with simulated clusters and a four-alternative forced-choice (4AFC) protocol combined with a three-down-one-up (3D1U) staircase to estimate reader-specific contrast detection thresholds and achieve comparable baseline task difficulty across readers; and (ii) a main 4AFC observer study using individualized datasets generated according to each reader's estimated contrast threshold, in which microcalcification detection performance was compared between the original and restored image conditions based on the percentage of correct responses (PC). Two generalized linear mixed models (GLMMs) were used: the first evaluated the effect of image condition as a fixed effect, with reader and case as crossed random intercepts, and the second evaluated the effect of reader experience on detection performance in the original image condition.
Results:
At the reader-specific contrast thresholds estimated in the pilot study, the mean PC for the original images was 78.3%, within the prespecified target performance range. Mean PC increased to 84.3% for the restored images, corresponding to an absolute improvement of 6.0 percentage points. The primary GLMM indicated a significant restoration effect on detection performance (OR = 1.57, 95% CI: 1.15-2.14, ). Baseline mean PC was 78.7% for Junior readers and 78.0% for Senior readers, and the separate GLMM found no evidence of an experience-group effect under the original image condition ( ).
Conclusions:
The proposed extended MB restoration pipeline improved the detectability of simulated microcalcification clusters by breast radiologists under the controlled 4AFC conditions investigated. By targeting the signal and noise characteristics predicted by the model for an acquisition at approximately twice the original radiation dose without requiring additional radiation exposure, the method represents a promising approach for improving microcalcification detectability in DM.
