ArticleBiomedical optics express2016
Automated data selection method to improve robustness of diffuse optical tomography for breast cancer imaging.
Article in Biomedical optics express, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Ultrasound-guided diffuse optical tomography: an adjunct to ultrasound that can reduce unnecessary breast biopsies.Breast cancer research : BCR · 2025Article
- Automated pipeline for breast cancer diagnosis using US assisted diffuse optical tomography.Biomedical optics express · 2023Article
- Difference imaging from single measurements in diffuse optical tomography: a deep learning approach.Journal of biomedical optics · 2022Article
- Deep learning-based method to accurately estimate breast tissue optical properties in the presence of the chest wall.Journal of biomedical optics · 2021Article
- Reducing image artifact in diffuse optical tomography by iterative perturbation correction based on multiwavelength measurements.Journal of biomedical optics · 2019Article
- Compact ultrasound-guided diffuse optical tomography system for breast cancer imaging.Journal of biomedical optics · 2018Article
- An Automated Preprocessing Method for Diffuse Optical Tomography to Improve Breast Cancer Diagnosis.Technology in cancer research & treatment · 2018Article
- Diffuse optical tomography using semiautomated coregistered ultrasound measurements.Journal of biomedical optics · 2017Article
- Two step imaging reconstruction using truncated pseudoinverse as a preliminary estimate in ultrasound guided diffuse optical tomography.Biomedical optics express · 2017Article
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2 authors.
Funding
Abstract
Imaging-guided near infrared diffuse optical tomography (DOT) has demonstrated a great potential as an adjunct modality for differentiation of malignant and benign breast lesions and for monitoring treatment response of breast cancers. However, diffused light measurements are sensitive to artifacts caused by outliers and errors in measurements due to probe-tissue coupling, patient and probe motions, and tissue heterogeneity. In general, pre-processing of the measurements is needed by experienced users to manually remove these outliers and therefore reduce imaging artifacts. An automated method of outlier removal, data selection, and filtering for diffuse optical tomography is introduced in this manuscript. This method consists of multiple steps to first combine several data sets collected from the same patient at contralateral normal breast and form a single robust reference data set using statistical tests and linear fitting of the measurements. The second step improves the perturbation measurements by filtering out outliers from the lesion site measurements using model based analysis. The results of 20 malignant and benign cases show similar performance between manual data processing and automated processing and improvement in tissue characterization of malignant to benign ratio by about 27%.
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