ArticleThe British journal of radiology2017
The impact of simulated motion blur on lesion detection performance in full-field digital mammography.
Article in The British journal of radiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 12 citations in OpenAlex.
- From Standard Establishment to Precision Intelligence: Research Progress in Quality Control of Mammography.Diagnostics (Basel, Switzerland) · 2026Review
- Predicting the quality of digital mammography from the perspective of detectability of microcalcifications: A Radiomics approach.Medical physics · 2025Article
- Technical feasibility of automated blur detection in digital mammography using convolutional neural network.European radiology experimental · 2024Article
- Is it blur or is it texture? An analysis of computational blur and texture measures commonly used in medical image analysis.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Differences between human and machine perception in medical diagnosis.Scientific reports · 2022Article
- Errors in Mammography Cannot be Solved Through Technology AloneAsian Pacific journal of cancer prevention : APJCP · 2018Review
- Analysis of Technical Repeat Studies in Screening Mammography.Journal of breast imagingArticle
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Authors and funding
6 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveMotion blur is a known phenomenon in full-field digital mammography, but the impact on lesion detection is unknown. This is the first study to investigate detection performance with varying magnitudes of simulated motion blur.
methods7 observers (15 ± 5 years' reporting experience) evaluated 248 cases (62 containing malignant masses, 62 containing malignant microcalcifications and 124 normal cases) for 3 conditions: no blurring (0 mm) and 2 magnitudes of simulated blurring (0.7 and 1.5 mm). Abnormal cases were biopsy proven. Mathematical simulation was used to provide a pixel shift in order to simulate motion blur. A free-response observer study was conducted to compare lesion detection performance for the three conditions. The equally weighted jackknife alternative free-response receiver operating characteristic was used as the figure of merit. Test alpha was set at 0.05 to control probability of Type I error.
resultsThe equally weighted jackknife alternative free-response receiver operating characteristic analysis found a statistically significant difference in lesion detection performance for both masses [F(2,22) = 6.01, p = 0.0084] and microcalcifications [F(2,49) = 23.14, p < 0.0001]. The figures of merit reduced as the magnitude of simulated blurring increased. Statistical differences were found between some of the pairs investigated for the detection of masses (0.0 vs 0.7 and 0.0 vs 1.5 mm) and all pairs for microcalcifications (0.0 vs 0.7, 0.0 vs 1.5 and 0.7 vs 1.5 mm). No difference was detected between 0.7 and 1.5 mm for masses.
conclusionThe mathematical simulation of motion blur caused a statistically significant reduction in lesion detection performance. These false-negative decisions could have implications for clinical practice. Advances in knowledge: This research demonstrates for the first time that motion blur has a negative and statistically significant impact on lesion detection performance in digital mammography.
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