ArticleOptica2022
Deep-learning based image reconstruction for MRI-guided near-infrared spectral tomography.
Article in Optica, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 30 citations in OpenAlex.
- Ultrasound-guided diffuse optical tomography: an adjunct to ultrasound that can reduce unnecessary breast biopsies.Breast cancer research : BCR · 2025Article
- Deep Learning Enables Fast and Accurate Quantification of MRI-Guided Near-Infrared Spectral Tomography for Breast Cancer Diagnosis.IEEE transactions on medical imaging · 2025Article
- Model-Based Convolution Neural Network for 3D Near-Infrared Spectral Tomography.IEEE transactions on medical imaging · 2025Article
- Self-supervised hybrid neural network to achieve quantitative bioluminescence tomography for cancer research.Biomedical optics express · 2024Article
- Automated pipeline for breast cancer diagnosis using US assisted diffuse optical tomography.Biomedical optics express · 2023Article
- FDU-Net: Deep Learning-Based Three-Dimensional Diffuse Optical Image Reconstruction.IEEE transactions on medical imaging · 2023Article
- Two-stage classification strategy for breast cancer diagnosis using ultrasound-guided diffuse optical tomography and deep learning.Journal of biomedical optics · 2023Article
- Deep Learning for Medical Image-Based Cancer Diagnosis.Cancers · 2023Review
- Two-layered blood-lipid phantom and method to determine absorption and oxygenation employing changes in moments of DTOFs.Biomedical optics express · 2023Article
- Fusion deep learning approach combining diffuse optical tomography and ultrasound for improving breast cancer classification.Biomedical optics express · 2023Article
- Deep learning-based fusion of widefield diffuse optical tomography and micro-CT structural priors for accurate 3D reconstructions.Biomedical optics express · 2023Article
- Unrolled-DOT: an interpretable deep network for diffuse optical tomography.Journal of biomedical optics · 2023Article
- Selfrec-Net: self-supervised deep learning approach for the reconstruction of Cherenkov-excited luminescence scanned tomography.Biomedical optics express · 2023Article
- Periodic-net: an end-to-end data driven framework for diffuse optical imaging of breast cancer from noisy boundary data.Journal of biomedical optics · 2023Article
- Quantitative molecular bioluminescence tomography.Journal of biomedical optics · 2022Article
- Monte Carlo-based data generation for efficient deep learning reconstruction of macroscopic diffuse optical tomography and topography applications.Journal of biomedical optics · 2022Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 3 countries.
Funding
Abstract
Non-invasive near-infrared spectral tomography (NIRST) can incorporate the structural information provided by simultaneous magnetic resonance imaging (MRI), and this has significantly improved the images obtained of tissue function. However, the process of MRI guidance in NIRST has been time consuming because of the needs for tissue-type segmentation and forward diffuse modeling of light propagation. To overcome these problems, a reconstruction algorithm for MRI-guided NIRST based on deep learning is proposed and validated by simulation and real patient imaging data for breast cancer characterization. In this approach, diffused optical signals and MRI images were both used as the input to the neural network, and simultaneously recovered the concentrations of oxy-hemoglobin, deoxy-hemoglobin, and water via end-to-end training by using 20,000 sets of computer-generated simulation phantoms. The simulation phantom studies showed that the quality of the reconstructed images was improved, compared to that obtained by other existing reconstruction methods. Reconstructed patient images show that the well-trained neural network with only simulation data sets can be directly used for differentiating malignant from benign breast tumors.
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Registered trials
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.