ArticleJournal of biomedical optics2015
Optimization of image reconstruction for magnetic resonance imaging-guided near-infrared diffuse optical spectroscopy in breast.
Article in Journal of biomedical optics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 21 citations in OpenAlex.
- Development of digital breast tomosynthesis and diffuse optical tomography fusion imaging for breast cancer detection.Scientific reports · 2020Trial
- Deep Learning Enables Fast and Accurate Quantification of MRI-Guided Near-Infrared Spectral Tomography for Breast Cancer Diagnosis.IEEE transactions on medical imaging · 2025Article
- Molecular Imaging Biomarkers for Early Cancer Detection: A Systematic Review of Emerging Technologies and Clinical Applications.Diagnostics (Basel, Switzerland) · 2024Review
- Performance assessment of MRI guided continuous wave near-infrared spectral tomography for breast imaging.Biomedical optics express · 2021Article
- Hybrid time-domain and continuous-wave diffuse optical tomography instrument with concurrent, clinical magnetic resonance imaging for breast cancer imaging.Journal of biomedical optics · 2019Article
- Back-propagation neural network-based reconstruction algorithm for diffuse optical tomography.Journal of biomedical optics · 2018Article
- Direct Regularization From Co-Registered Contrast MRI Improves Image Quality of MRI-Guided Near-Infrared Spectral Tomography of Breast Lesions.IEEE transactions on medical imaging · 2018Article
- Impact of errors in experimental parameters on reconstructed breast images using diffuse optical tomography.Biomedical optics express · 2018Article
- Addition of T2-guided optical tomography improves noncontrast breast magnetic resonance imaging diagnosis.Breast cancer research : BCR · 2017Article
- Collagen quantification in breast tissue using a 12-wavelength near infrared spectral tomography (NIRST) system.Biomedical optics express · 2017Article
- Non-invasive optical estimate of tissue composition to differentiate malignant from benign breast lesions: A pilot study.Scientific reports · 2017Article
- Multiobjective guided priors improve the accuracy of near-infrared spectral tomography for breast imaging.Journal of biomedical optics · 2016Article
- Portable, parallel 9-wavelength near-infrared spectral tomography (NIRST) system for efficient characterization of breast cancer within the clinical oncology infusion suite.Biomedical optics express · 2016Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
An optimized approach to nonlinear iterative reconstruction of magnetic resonance imaging (MRI)-guided near-infrared spectral tomography (NIRST) images was developed using an L-curve-based algorithm for the choice of regularization parameter. This approach was applied to clinical exam data to maximize the reconstructed values differentiating malignant and benign lesions. MRI/NIRST data from 25 patients with abnormal breast readings (BI-RADS category 4-5) were analyzed using this optimal regularization methodology, and the results showed enhanced p values and area under the curve (AUC) for the task of differentiating malignant from benign lesions. Of the four absorption parameters and two scatter parameters, the most significant differences for benign versus malignant were total hemoglobin (HbT) and tissue optical index (TOI) with p values = 0.01 and 0.001, and AUC values = 0.79 and 0.94, respectively, in terms of HbT and TOI. This dramatically improved the values relative to fixed regularization (p value = 0.02 and 0.003; AUC = 0.75 and 0.83) showing that more differentiation was possible with the optimal method. Through a combination of both biomarkers, HbT and TOI, the AUC increased from 82.9% (fixed regulation = 0.1) to 94.3% (optimal method).
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.