ArticleBiomedical optics express2021
Ultrasound segmentation-guided edge artifact reduction in diffuse optical tomography using connected component analysis.
Article in Biomedical optics express, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Diffuse correlation tomography: a technique to characterize tissue blood flow abnormalities in benign and malignant breast lesions.Biomedical optics express · 2024Article
- Improving diffuse optical tomography imaging quality using APU-Net: an attention-based physical U-Net model.Journal of biomedical optics · 2024Article
- Real-time breast lesion classification combining diffuse optical tomography frequency domain data and BI-RADS assessment.Journal of biophotonics · 2024Article
- Enhanced model iteration algorithm with graph neural network for diffuse optical tomography.Biomedical optics express · 2024Article
- Automated pipeline for breast cancer diagnosis using US assisted diffuse optical tomography.Biomedical optics express · 2023Article
- Fast ICCD-based temperature modulated fluorescence tomography.Applied optics · 2023Article
- Two-stage classification strategy for breast cancer diagnosis using ultrasound-guided diffuse optical tomography and deep learning.Journal of biomedical optics · 2023Article
- Fusion deep learning approach combining diffuse optical tomography and ultrasound for improving breast cancer classification.Biomedical optics express · 2023Article
- Difference imaging from single measurements in diffuse optical tomography: a deep learning approach.Journal of biomedical optics · 2022Article
- Evaluation of a pipeline for simulation, reconstruction, and classification in ultrasound-aided diffuse optical tomography of breast tumors.Journal of biomedical optics · 2022Article
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Authors and funding
3 authors.
Funding
Abstract
Ultrasound (US)-guided diffuse optical tomography (DOT) has demonstrated potential value for breast cancer diagnosis and treatment response assessment. However, in clinical use, the chest wall, poor probe-tissue contact, and tissue heterogeneity can all cause image artifacts. These image artifacts, appearing commonly as hot spots in the non-lesion regions (edge artifacts), can decrease the reconstruction accuracy and cause misinterpretation of lesion images. Here we introduce an iterative, connected component analysis-based image artifact reduction algorithm. A convolutional neural network (CNN) is used to segment co-registered US images to extract the lesion location and size to guide the artifact reduction. We demonstrate its performance using Monte Carlo simulations on VICTRE digital breast phantoms and breast patient images. In simulated tissue mismatch models, this algorithm successfully reduces edge artifacts without significantly changing the reconstructed target absorption coefficients. With clinical data it improves the optical contrast between malignant and benign groups, from 1.55 without artifact reduction to 1.91 with artifact reduction. The proposed algorithm has a broad range of applications in other modality-guided DOT imaging.
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Registered trials
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