ArticleBiomedical optics express2015
Direct regularization from co-registered anatomical images for MRI-guided near-infrared spectral tomographic image reconstruction.
Article in Biomedical optics express, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Ex-Vivo Prostate Evaluation of Fused-Data TREIT Using Only Biopsy-Probe Electrodes.IEEE transactions on bio-medical engineering · 2026Article
- Deep Learning Enables Fast and Accurate Quantification of MRI-Guided Near-Infrared Spectral Tomography for Breast Cancer Diagnosis.IEEE transactions on medical imaging · 2025Article
- FDU-Net: Deep Learning-Based Three-Dimensional Diffuse Optical Image Reconstruction.IEEE transactions on medical imaging · 2023Article
- Selfrec-Net: self-supervised deep learning approach for the reconstruction of Cherenkov-excited luminescence scanned tomography.Biomedical optics express · 2023Article
- Method to improve the localization accuracy and contrast recovery of lesions in separately acquired X-ray and diffuse optical tomographic breast imaging.Biomedical optics express · 2022Article
- Deep-learning based image reconstruction for MRI-guided near-infrared spectral tomography.Optica · 2022Article
- Evaluation of a pipeline for simulation, reconstruction, and classification in ultrasound-aided diffuse optical tomography of breast tumors.Journal of biomedical optics · 2022Article
- Performance assessment of MRI guided continuous wave near-infrared spectral tomography for breast imaging.Biomedical optics express · 2021Article
- Enhanced diffuse optical tomographic reconstruction using concurrent ultrasound information.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2021Article
- A review of optical breast imaging: Multi-modality systems for breast cancer diagnosis.European journal of radiology · 2020Review
- Resolving tissue chromophore concentration at MRI resolution using multi-wavelength photo-magnetic imaging.Biomedical optics express · 2020Article
- Systematic study of the effect of ultrasound gel on the performances of time-domain diffuse optics and diffuse correlation spectroscopy.Biomedical optics express · 2019Article
- Back-propagation neural network-based reconstruction algorithm for diffuse optical tomography.Journal of biomedical optics · 2018Article
- Weighting function effects in a direct regularization method for image-guided near-infrared spectral tomography of breast cancer.Biomedical optics express · 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
- An Automated Preprocessing Method for Diffuse Optical Tomography to Improve Breast Cancer Diagnosis.Technology in cancer research & treatment · 2018Article
- Two step imaging reconstruction using truncated pseudoinverse as a preliminary estimate in ultrasound guided diffuse optical tomography.Biomedical optics express · 2017Article
- Addition of T2-guided optical tomography improves noncontrast breast magnetic resonance imaging diagnosis.Breast cancer research : BCR · 2017Article
- Diffuse optical tomography reconstruction method using ultrasound images as prior for regularization matrix.Journal of biomedical optics · 2017Article
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5 authors.
Funding
Abstract
Combining anatomical information from high resolution imaging modalities to guide near-infrared spectral tomography (NIRST) is an efficient strategy for improving the quality of the reconstructed spectral images. A new approach for incorporating image information directly into the inversion matrix regularization was examined using Direct Regularization from Images (DRI), which encodes the gray-scale data into the NIRST image reconstruction problem. This process has the benefit of eliminating user intervention such as image segmentation of distinct regions. Specifically, the Dynamic Contrast Enhanced Magnetic Resonance (DCE-MR) image intensity value differences within the anatomical image were used to implement an exponentially-weighted regularization function between the image pixels. The algorithm was validated using simulated reconstructions with noise, and the results showed that spatial resolution and robustness of the reconstructed images were significantly improved by appropriate choice of the regularization weight parameters. The proposed approach was also tested on in vivo breast data acquired in a recent clinical trial combining NIRST / MRI for cancer tumor characterization. Relative to the standard "no priors" diffuse recovery, the contrast of the tumor to the normal surrounding tissue increased from 2.4 to 3.6, and the difference between the tumor size segmented from DCE-MR images and reconstructed optical images decreased from 18% to 6%, while there was an overall decrease in surface artifacts.
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