ArticleScientific reports2024
Lesion-conditioning of synthetic MRI-derived subtraction-MIPs of the breast using a latent diffusion model.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 4 citations in OpenAlex.
- Influence of co-registration on lesion characterization in diffusion-weighted breast MRI.Magma (New York, N.Y.) · 2026Article
- Can AI Detect What Is Not Injected? Evaluation of Lesion Detection in Virtual Contrast-Enhanced Breast MRI Using a Large-Scale AI Model Trained on GBCA-Enhanced Data.Tomography (Ann Arbor, Mich.) · 2026Article
- Including AI in diffusion-weighted breast MRI has potential to increase reader confidence and reduce workload.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- The Evolution and Clinical Impact of Deep Learning Technologies in Breast MRI.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Review
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Authors and funding
11 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The purpose of this feasibility study is to investigate if latent diffusion models (LDMs) are capable to generate contrast enhanced (CE) MRI-derived subtraction maximum intensity projections (MIPs) of the breast, which are conditioned by lesions. We trained an LDM with n = 2832 CE-MIPs of breast MRI examinations of n = 1966 patients (median age: 50 years) acquired between the years 2015 and 2020. The LDM was subsequently conditioned with n = 756 segmented lesions from n = 407 examinations, indicating their location and BI-RADS scores. By applying the LDM, synthetic images were generated from the segmentations of an independent validation dataset. Lesions, anatomical correctness, and realistic impression of synthetic and real MIP images were further assessed in a multi-rater study with five independent raters, each evaluating n = 204 MIPs (50% real/50% synthetic images). The detection of synthetic MIPs by the raters was akin to random guessing with an AUC of 0.58. Interrater reliability of the lesion assessment was high both for real (Kendall's W = 0.77) and synthetic images (W = 0.85). A higher AUC was observed for the detection of suspicious lesions (BI-RADS
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