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Yang M, Han Y, Bianco A, Ji DK. Recent Progress on Second Near-Infrared Emitting Carbon Dots in Biomedicine. ACS NANO 2024; 18:11560-11572. [PMID: 38682810 DOI: 10.1021/acsnano.4c00820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/01/2024]
Abstract
Second near-infrared (NIR-II) carbon dots, with absorption or emission between 1000 and 1700 nm, are gaining increasing attention in the biomaterial field due to their distinctive properties, which include straightforward preparation processes, stable photophysical characteristics, excellent biocompatibility, and low cost. As a result, there is a growing focus on the controlled synthesis and modulation of the photochemical and photophysical properties of NIR-II carbon dots, with the aim to further expand their biomedical applications, a current research hotspot. This account aims to provide a comprehensive overview of the recent advancements in NIR-II carbon dots within the biomedical field. The review will cover the following topics: (i) the design, synthesis, and purification of NIR-II carbon dots, (ii) the surface modification strategies, and (iii) the biomedical applications, particularly in the domain of cancer theranostics. Additionally, this account addresses the challenges encountered by NIR-II carbon dots and will outline future directions in the realm of cancer theranostics. By exploring carbon-based NIR-II biomaterials, we can anticipate that this contribution will garner increased attention and contribute to the development of next-generation advanced functional carbon dots, thereby offering enhanced tools and strategies in the biomedical field.
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Affiliation(s)
- Mei Yang
- Institute of Molecular Medicine (IMM), Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200240, China
- School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - Yongqi Han
- Institute of Molecular Medicine (IMM), Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200240, China
| | - Alberto Bianco
- CNRS, UPR3572, Immunology, Immunopathology and Therapeutic Chemistry, ISIS, University of Strasbourg, 67000 Strasbourg, France
| | - Ding-Kun Ji
- Institute of Molecular Medicine (IMM), Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200240, China
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Li Y, Zhang Y, Yu Q, He C, Yuan X. Intelligent scoring system based on dynamic optical breast imaging for early detection of breast cancer. BIOMEDICAL OPTICS EXPRESS 2024; 15:1515-1527. [PMID: 38495695 PMCID: PMC10942703 DOI: 10.1364/boe.515135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 01/06/2024] [Accepted: 01/31/2024] [Indexed: 03/19/2024]
Abstract
Early detection of breast cancer can significantly improve patient outcomes and five-year survival in clinical screening. Dynamic optical breast imaging (DOBI) technology reflects the blood oxygen metabolism level of tumors based on the theory of tumor neovascularization, which offers a technical possibility for early detection of breast cancer. In this paper, we propose an intelligent scoring system integrating DOBI features assessment and a malignancy score grading reporting system for early detection of breast cancer. Specifically, we build six intelligent feature definition models to depict characteristics of regions of interest (ROIs) from location, space, time and context separately. Similar to the breast imaging-reporting and data system (BI-RADS), we conclude the malignancy score grading reporting system to score and evaluate ROIs as follows: Malignant (≥ 80 score), Likely Malignant (60-80 score), Intermediate (35-60 score), Likely Benign (10-35 score), and Benign (<10 score). This system eliminates the influence of subjective physician judgments on the assessment of the malignant probability of ROIs. Extensive experiments on 352 Chinese patients demonstrate the effectiveness of the proposed system compared to state-of-the-art methods.
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Affiliation(s)
- Yaoyao Li
- Hangzhou Institute of Technology, Xidian University, Qiannong Dong Road No. 8, Hangzhou, Zhejiang, 311231, China
| | - Yipei Zhang
- Hangzhou Institute of Technology, Xidian University, Qiannong Dong Road No. 8, Hangzhou, Zhejiang, 311231, China
| | - Qiang Yu
- Hangzhou Institute of Technology, Xidian University, Qiannong Dong Road No. 8, Hangzhou, Zhejiang, 311231, China
| | - Chenglong He
- Hangzhou Institute of Technology, Xidian University, Qiannong Dong Road No. 8, Hangzhou, Zhejiang, 311231, China
| | - Xiguo Yuan
- Hangzhou Institute of Technology, Xidian University, Qiannong Dong Road No. 8, Hangzhou, Zhejiang, 311231, China
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Mahmoud A, El-Sharkawy YH. Multi-wavelength interference phase imaging for automatic breast cancer detection and delineation using diffuse reflection imaging. Sci Rep 2024; 14:415. [PMID: 38172105 PMCID: PMC10764793 DOI: 10.1038/s41598-023-50475-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Accepted: 12/20/2023] [Indexed: 01/05/2024] Open
Abstract
Millions of women globally are impacted by the major health problem of breast cancer (BC). Early detection of BC is critical for successful treatment and improved survival rates. In this study, we provide a progressive approach for BC detection using multi-wavelength interference (MWI) phase imaging based on diffuse reflection hyperspectral (HS) imaging. The proposed findings are based on the measurement of the interference pattern between the blue (446.6 nm) and red (632 nm) wavelengths. We consider implementing a comprehensive image processing and categorization method based on the use of Fast Fourier (FF) transform analysis pertaining to a change in the refractive index between tumor and normal tissue. We observed that cancer growth affects tissue organization dramatically, as seen by persistently increased refractive index variance in tumors compared normal areas. Both malignant and normal tissue had different depth data collected from it that was analyzed. To enhance the categorization of ex-vivo BC tissue, we developed and validated a training classifier algorithm specifically designed for categorizing HS cube data. Following the application of signal normalization with the FF transform algorithm, our methodology achieved a high level of performance with a specificity (Spec) of 94% and a sensitivity (Sen) of 90.9% for the 632 nm acquired image categorization, based on preliminary findings from breast specimens under investigation. Notably, we successfully leveraged unstained tissue samples to create 3D phase-resolved images that effectively highlight the distinctions in diffuse reflectance features between cancerous and healthy tissue. Preliminary data revealed that our imaging method might be able to assist specialists in safely excising malignant areas and assessing the tumor bed following resection automatically at different depths. This preliminary investigation might result in an effective "in-vivo" disease description utilizing optical technology using a typical RGB camera with wavelength-specific operation with our quantitative phase MWI imaging methodology.
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Affiliation(s)
- Alaaeldin Mahmoud
- Optoelectronics and Automatic Control Systems Department, Military Technical College, Kobry El-Kobba, Cairo, Egypt.
| | - Yasser H El-Sharkawy
- Optoelectronics and Automatic Control Systems Department, Military Technical College, Kobry El-Kobba, Cairo, Egypt
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Deng B, Gu H, Zhu H, Chang K, Hoebel KV, Patel JB, Kalpathy-Cramer J, Carp SA. FDU-Net: Deep Learning-Based Three-Dimensional Diffuse Optical Image Reconstruction. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:2439-2450. [PMID: 37028063 PMCID: PMC10446911 DOI: 10.1109/tmi.2023.3252576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Near-infrared diffuse optical tomography (DOT) is a promising functional modality for breast cancer imaging; however, the clinical translation of DOT is hampered by technical limitations. Specifically, conventional finite element method (FEM)-based optical image reconstruction approaches are time-consuming and ineffective in recovering full lesion contrast. To address this, we developed a deep learning-based reconstruction model (FDU-Net) comprised of a Fully connected subnet, followed by a convolutional encoder-Decoder subnet, and a U-Net for fast, end-to-end 3D DOT image reconstruction. The FDU-Net was trained on digital phantoms that include randomly located singular spherical inclusions of various sizes and contrasts. Reconstruction performance was evaluated in 400 simulated cases with realistic noise profiles for the FDU-Net and conventional FEM approaches. Our results show that the overall quality of images reconstructed by FDU-Net is significantly improved compared to FEM-based methods and a previously proposed deep-learning network. Importantly, once trained, FDU-Net demonstrates substantially better capability to recover true inclusion contrast and location without using any inclusion information during reconstruction. The model was also generalizable to multi-focal and irregularly shaped inclusions unseen during training. Finally, FDU-Net, trained on simulated data, could successfully reconstruct a breast tumor from a real patient measurement. Overall, our deep learning-based approach demonstrates marked superiority over the conventional DOT image reconstruction methods while also offering over four orders of magnitude acceleration in computational time. Once adapted to the clinical breast imaging workflow, FDU-Net has the potential to provide real-time accurate lesion characterization by DOT to assist the clinical diagnosis and management of breast cancer.
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Zhang M, Li S, Xue M, Zhu Q. Two-stage classification strategy for breast cancer diagnosis using ultrasound-guided diffuse optical tomography and deep learning. JOURNAL OF BIOMEDICAL OPTICS 2023; 28:086002. [PMID: 37638108 PMCID: PMC10457211 DOI: 10.1117/1.jbo.28.8.086002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 07/29/2023] [Accepted: 08/02/2023] [Indexed: 08/29/2023]
Abstract
Significance Ultrasound (US)-guided diffuse optical tomography (DOT) has demonstrated great potential for breast cancer diagnosis in which real-time or near real-time diagnosis with high accuracy is desired. Aim We aim to use US-guided DOT to achieve an automated, fast, and accurate classification of breast lesions. Approach We propose a two-stage classification strategy with deep learning. In the first stage, US images and histograms created from DOT perturbation measurements are combined to predict benign lesions. Then the non-benign suspicious lesions are passed through to the second stage, which combine US image features, DOT histogram features, and 3D DOT reconstructed images for final diagnosis. Results The first stage alone identified 73.0% of benign cases without image reconstruction. In distinguishing between benign and malignant breast lesions in patient data, the two-stage classification approach achieved an area under the receiver operating characteristic curve of 0.946, outperforming the diagnoses of all single-modality models and of a single-stage classification model that combines all US images, DOT histogram, and imaging features. Conclusions The proposed two-stage classification strategy achieves better classification accuracy than single-modality-only models and a single-stage classification model that combines all features. It can potentially distinguish breast cancers from benign lesions in near real-time.
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Affiliation(s)
- Menghao Zhang
- Washington University in St. Louis, Department of Electrical and Systems Engineering, St. Louis, Missouri, United States
| | - Shuying Li
- Washington University in St. Louis, Department of Biomedical Engineering, St. Louis, Missouri, United States
| | - Minghao Xue
- Washington University in St. Louis, Department of Biomedical Engineering, St. Louis, Missouri, United States
| | - Quing Zhu
- Washington University in St. Louis, Department of Electrical and Systems Engineering, St. Louis, Missouri, United States
- Washington University in St. Louis, Department of Biomedical Engineering, St. Louis, Missouri, United States
- Washington University School of Medicine, Department of Radiology, St. Louis, Missouri, United States
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Rivera-Fernández JD, Roa-Tort K, Stolik S, Valor A, Fabila-Bustos DA, de la Rosa G, Hernández-Chávez M, de la Rosa-Vázquez JM. Design of a Low-Cost Diffuse Optical Mammography System for Biomedical Image Processing in Breast Cancer Diagnosis. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23094390. [PMID: 37177594 PMCID: PMC10181699 DOI: 10.3390/s23094390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 04/15/2023] [Accepted: 04/26/2023] [Indexed: 05/15/2023]
Abstract
Worldwide, breast cancer is the most common type of cancer that mainly affects women. Several diagnosis techniques based on optical instrumentation and image analysis have been developed, and these are commonly used in conjunction with conventional diagnostic devices such as mammographs, ultrasound, and magnetic resonance imaging of the breast. The cost of using these instruments is increasing, and developing countries, whose deaths indices due to breast cancer are high, cannot access conventional diagnostic methods and have even less access to newer techniques. Other studies, based on the analysis of images acquired by traditional methods, require high resolutions and knowledge of the origin of the captures in order to avoid errors. For this reason, the design of a low-cost diffuse optical mammography system for biomedical image processing in breast cancer diagnosis is presented. The system combines the acquisition of breast tissue photographs, diffuse optical reflectance (as a biophotonics technique), and the processing of digital images for the study and diagnosis of breast cancer. The system was developed in the form of a medical examination table with a 638 nm red-light source, using light-emitted diode technology (LED) and a low-cost web camera for the acquisition of breast tissue images. The system is automatic, and its control, through a graphical user interface (GUI), saves costs and allows for the subsequent analysis of images using a digital image-processing algorithm. The results obtained allow for the possibility of planning in vivo measurements. In addition, the acquisition of images every 30° around the breast tissue could be used in future research in order to perform a three-dimensional (3D) reconstruction and an analysis of the captures through deep learning techniques. These could be combined with virtual, augmented, or mixed reality environments to predict the position of tumors, increase the likelihood of a correct medical diagnosis, and develop a training system for specialists. Furthermore, the system allows for the possibility to develop analysis of optical characterization for new phantom studies in breast cancer diagnosis through bioimaging techniques.
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Affiliation(s)
- Josué D Rivera-Fernández
- Laboratorio de Optomecatrónica, UPIIH, Instituto Politécnico Nacional, Distrito de Educación, Salud, Ciencia, Tecnología e Innovación, San Agustín Tlaxiaca 42162, Mexico
| | - Karen Roa-Tort
- Laboratorio de Optomecatrónica, UPIIH, Instituto Politécnico Nacional, Distrito de Educación, Salud, Ciencia, Tecnología e Innovación, San Agustín Tlaxiaca 42162, Mexico
| | - Suren Stolik
- Laboratorio de Biofotónica, ESIME ZAC, Instituto Politécnico Nacional, Ciudad de Mexico 07320, Mexico
| | - Alma Valor
- Laboratorio de Biofotónica, ESIME ZAC, Instituto Politécnico Nacional, Ciudad de Mexico 07320, Mexico
| | - Diego A Fabila-Bustos
- Laboratorio de Optomecatrónica, UPIIH, Instituto Politécnico Nacional, Distrito de Educación, Salud, Ciencia, Tecnología e Innovación, San Agustín Tlaxiaca 42162, Mexico
| | - Gabriela de la Rosa
- Hospital de Especialidades del niño y la Mujer Dr. Felipe Nuñez Lara, Santiago de Querétaro 76090, Mexico
| | - Macaria Hernández-Chávez
- Laboratorio de Optomecatrónica, UPIIH, Instituto Politécnico Nacional, Distrito de Educación, Salud, Ciencia, Tecnología e Innovación, San Agustín Tlaxiaca 42162, Mexico
| | - José M de la Rosa-Vázquez
- Laboratorio de Biofotónica, ESIME ZAC, Instituto Politécnico Nacional, Ciudad de Mexico 07320, Mexico
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Vanegas M, Mireles M, Xu E, Yan S, Fang Q. Compact breast shape acquisition system for improving diffuse optical tomography image reconstructions. BIOMEDICAL OPTICS EXPRESS 2023; 14:1579-1593. [PMID: 37078036 PMCID: PMC10110328 DOI: 10.1364/boe.481092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 12/23/2022] [Accepted: 01/04/2023] [Indexed: 05/03/2023]
Abstract
Diffuse optical tomography (DOT) has been investigated for diagnosing malignant breast lesions, but its accuracy relies on model-based image reconstructions, which in turn depends on the accuracy of breast shape acquisition. In this work, we have developed a dual-camera structured light imaging (SLI) breast shape acquisition system tailored for a mammography-like compression setting. Illumination pattern intensity is dynamically adjusted to account for skin tone differences, while thickness-informed pattern masking reduces artifacts due to specular reflections. This compact system is affixed to a rigid mount that can be installed into existing mammography or parallel-plate DOT systems without the need for camera-projector re-calibration. Our SLI system produces sub-millimeter resolution with a mean surface error of 0.26 mm. This breast shape acquisition system results in more accurate surface recovery, with an average 1.6-fold reduction in surface estimation errors over a reference method via contour extrusion. Such improvement translates to 25% to 50% reduction in mean squared error in the recovered absorption coefficient for a series of simulated tumors 1-2 cm below the skin.
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Affiliation(s)
- Morris Vanegas
- Department of Bioengineering, Northeastern University, Boston, MA 02115, USA
| | - Miguel Mireles
- Department of Bioengineering, Northeastern University, Boston, MA 02115, USA
| | - Edward Xu
- Department of Bioengineering, Northeastern University, Boston, MA 02115, USA
| | - Shijie Yan
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA 02115, USA
| | - Qianqian Fang
- Department of Bioengineering, Northeastern University, Boston, MA 02115, USA
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA 02115, USA
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8
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Nizam NI, Ochoa M, Smith JT, Intes X. Deep learning-based fusion of widefield diffuse optical tomography and micro-CT structural priors for accurate 3D reconstructions. BIOMEDICAL OPTICS EXPRESS 2023; 14:1041-1053. [PMID: 36950248 PMCID: PMC10026582 DOI: 10.1364/boe.480091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 01/10/2023] [Accepted: 01/24/2023] [Indexed: 06/17/2023]
Abstract
Widefield illumination and detection strategies leveraging structured light have enabled fast and robust probing of tissue properties over large surface areas and volumes. However, when applied to diffuse optical tomography (DOT) applications, they still require a time-consuming and expert-centric solving of an ill-posed inverse problem. Deep learning (DL) models have been recently proposed to facilitate this challenging step. Herein, we expand on a previously reported deep neural network (DNN) -based architecture (modified AUTOMAP - ModAM) for accurate and fast reconstructions of the absorption coefficient in 3D DOT based on a structured light illumination and detection scheme. Furthermore, we evaluate the improved performances when incorporating a micro-CT structural prior in the DNN-based workflow, named Z-AUTOMAP. This Z-AUTOMAP significantly improves the widefield imaging process's spatial resolution, especially in the transverse direction. The reported DL-based strategies are validated both in silico and in experimental phantom studies using spectral micro-CT priors. Overall, this is the first successful demonstration of micro-CT and DOT fusion using deep learning, greatly enhancing the prospect of rapid data-integration strategies, often demanded in challenging pre-clinical scenarios.
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Muldoon A, Kabeer A, Cormier J, Saksena MA, Fang Q, Carp SA, Deng B. 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 2022; 13:5295-5310. [PMID: 36425617 PMCID: PMC9664870 DOI: 10.1364/boe.470373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 09/05/2022] [Accepted: 09/05/2022] [Indexed: 05/11/2023]
Abstract
Near-infrared diffuse optical tomography (DOT) has the potential to improve the accuracy of breast cancer diagnosis and aid in monitoring the response of breast tumors to chemotherapy by providing hemoglobin-based functional imaging. The use of structural lesion priors derived from clinical breast imaging methods, such as mammography, can improve recovery of tumor optical contrast; however, accurate lesion prior placement is essential to take full advantage of prior-guided DOT image reconstruction. Simultaneous optical and anatomical imaging may not always be possible or desired, which can make the accurate registration of the lesion prior challenging. In this paper, we present a three-step lesion prior scanning approach to facilitate improved accuracy in lesion localization based on the optical contrast quantified by the total hemoglobin concentration (HbT) for non-simultaneous multimodal DOT and digital breast tomosynthesis (DBT) imaging. In three challenging breast cancer patient cases, where no clear optical contrast was present initially, we have demonstrated consistent improvement in the recovered HbT lesion contrast by utilizing this method.
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Affiliation(s)
- Ailis Muldoon
- Athinoula A. Martinos Center for Biomedical Imaging,
Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Aiza Kabeer
- Athinoula A. Martinos Center for Biomedical Imaging,
Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Jayne Cormier
- Breast Imaging Division, Department of Radiology,
Massachusetts General Hospital, Boston, MA 02114, USA
| | - Mansi A. Saksena
- Breast Imaging Division, Department of Radiology,
Massachusetts General Hospital, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Qianqian Fang
- Department of Bioengineering, Northeastern University, Boston, MA 02115, USA
| | - Stefan A. Carp
- Athinoula A. Martinos Center for Biomedical Imaging,
Massachusetts General Hospital, Charlestown, MA 02129, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Bin Deng
- Athinoula A. Martinos Center for Biomedical Imaging,
Massachusetts General Hospital, Charlestown, MA 02129, USA
- Harvard Medical School, Boston, MA 02115, USA
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Li S, Zhang M, Xue M, Zhu Q. Difference imaging from single measurements in diffuse optical tomography: a deep learning approach. JOURNAL OF BIOMEDICAL OPTICS 2022; 27:086003. [PMID: 36008881 PMCID: PMC9403167 DOI: 10.1117/1.jbo.27.8.086003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Accepted: 08/03/2022] [Indexed: 06/15/2023]
Abstract
SIGNIFICANCE "Difference imaging," which reconstructs target optical properties using measurements with and without target information, is often used in diffuse optical tomography (DOT) in vivo imaging. However, taking additional reference measurements is time consuming, and mismatches between the target medium and the reference medium can cause inaccurate reconstruction. AIM We aim to streamline the data acquisition and mitigate the mismatch problems in DOT difference imaging using a deep learning-based approach to generate data from target measurements only. APPROACH We train an artificial neural network to output data for difference imaging from target measurements only. The model is trained and validated on simulation data and tested with simulations, phantom experiments, and clinical data from 56 patients with breast lesions. RESULTS The proposed method has comparable performance to the traditional approach using measurements without mismatch between the target side and the reference side, and it outperforms the traditional approach using measurements when there is a mismatch. It also improves the target-to-artifact ratio and lesion localization in patient data. CONCLUSIONS The proposed method can simplify the data acquisition procedure, mitigate mismatch problems, and improve reconstructed image quality in DOT difference imaging.
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Affiliation(s)
- Shuying Li
- Washington University in St. Louis, Optical and Ultrasound Imaging Lab, Department of Biomedical Engineering, St. Louis, Missouri, United States
| | - Menghao Zhang
- Washington University in St. Louis, Optical and Ultrasound Imaging Lab, Department of Electrical and Systems Engineering, St. Louis, Missouri, United States
| | - Minghao Xue
- Washington University in St. Louis, Optical and Ultrasound Imaging Lab, Department of Biomedical Engineering, St. Louis, Missouri, United States
| | - Quing Zhu
- Washington University in St. Louis, Optical and Ultrasound Imaging Lab, Department of Biomedical Engineering, St. Louis, Missouri, United States
- Washington University School of Medicine, Department of Radiology, St. Louis, Missouri, United States
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New directions for optical breast imaging and sensing: multimodal cancer imaging and lactation research. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2022. [DOI: 10.1016/j.cobme.2022.100380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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12
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Wang Y, Li S, Wang Y, Yan Q, Wang X, Shen Y, Li Z, Kang F, Cao X, Zhu S. Compact fiber-free parallel-plane multi-wavelength diffuse optical tomography system for breast imaging. OPTICS EXPRESS 2022; 30:6469-6486. [PMID: 35299431 DOI: 10.1364/oe.448874] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 02/05/2022] [Indexed: 06/14/2023]
Abstract
To facilitate the clinical applicability of the diffuse optical inspection device, a compact multi-wavelength diffuse optical tomography system for breast imaging (compact-DOTB) with a fiber-free parallel-plane structure was designed and fabricated for acquiring three-dimensional optical properties of the breast in continuous-wave mode. The source array consists of 56 surface-mounted micro light-emitting diodes (LEDs), each integrating three wavelengths (660, 750, and 840 nm). The detector array is arranged with 56 miniaturized surface-mounted optical sensors, each encapsulating a high-sensitivity photodiode (PD) and a low-noise current amplifier with a gain of 24×. The system provides 3,136 pairs of source-detector measurements at each wavelength, and the fiber-free design largely ensures consistency between source/detection channels while effectively reducing the complexity of system operation and maintenance. We have evaluated the compact-DOTB system's characteristics and demonstrated its performance in terms of reconstruction positioning accuracy and recovery contrast with breast-sized phantom experiments. Furthermore, the breast cancer patient studies have been carried out, and the quantitative results indicate that the compact-DOTB system is able to observe the changes in the functional tissue components of the breast after receiving the neoadjuvant chemotherapy (NAC), demonstrating the great potential of the proposed compact system for clinical applications, while its cost and ease of operation are competitive with the existing breast-DOT devices.
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Cheng Z, Du Y, Yu L, Yuan Z, Tian J. Application of Noninvasive Imaging to Combined Immune Checkpoint Inhibitors for Breast Cancer: Facts and Future. Mol Imaging Biol 2022; 24:264-279. [PMID: 35102468 DOI: 10.1007/s11307-021-01688-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 11/13/2021] [Accepted: 11/24/2021] [Indexed: 12/19/2022]
Abstract
With the application of mono-immunotherapy in cancer, particularly immune checkpoint inhibitors, improved outcomes have been achieved. However, there are several limitations to immunotherapy, such as a poor response to the drugs, immune resistance, and immune-related adverse events. In recent years, studies of preclinical animal models and clinical trials have demonstrated that immune checkpoint inhibitors for breast cancer can significantly prolong the overall survival and quality of patients' lives. Meanwhile, combined immune checkpoint inhibitor treatment has attracted researchers' attention and showed great potential in the comprehensive treatment of breast cancer patients. Additionally, noninvasive imaging enables physicians to predict response to combined immunotherapeutic drugs, achieve treatment efficacy, and lead to better clinical management. Herein, we review the background of combined immune checkpoint inhibitor therapy and summarize its targeted imaging as well as progress in noninvasive imaging aimed at evaluating therapeutic outcomes. Finally, we describe several factors that may influence the outcome of this combined immunotherapy, the future direction of medical imaging, and the potential application of artificial intelligence in breast cancer. With further development of noninvasive imaging for the guidance of combined immune checkpoint inhibitors, cures for this disease may be achieved.
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Affiliation(s)
- Zhongquan Cheng
- Department of General Surgery, Capital Medical University, Beijing Friendship Hospital, Beijing, 100050, China.,CAS Key Laboratory of Molecular Imaging, Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex System, Institute of Automation, Chinese Academy of Sciences, BeijingBeijing, 100190, China
| | - Yang Du
- CAS Key Laboratory of Molecular Imaging, Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex System, Institute of Automation, Chinese Academy of Sciences, BeijingBeijing, 100190, China. .,University of Chinese Academy of Sciences, Beijing, 100080, China.
| | - Leyi Yu
- Department of General Surgery, Capital Medical University, Beijing Friendship Hospital, Beijing, 100050, China
| | - Zhu Yuan
- Department of General Surgery, Capital Medical University, Beijing Friendship Hospital, Beijing, 100050, China.
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex System, Institute of Automation, Chinese Academy of Sciences, BeijingBeijing, 100190, China. .,University of Chinese Academy of Sciences, Beijing, 100080, China. .,Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine Science and Engineering, Beihang University, Beijing, 100191, China. .,School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710071, China.
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Wang X, Hu R, Wang Y, Yan Q, Wang Y, Kang F, Zhu S. A Data Self-Calibration Method Based on High-Density Parallel Plate Diffuse Optical Tomography for Breast Cancer Imaging. Front Oncol 2021; 11:786289. [PMID: 34993144 PMCID: PMC8724432 DOI: 10.3389/fonc.2021.786289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 12/03/2021] [Indexed: 11/13/2022] Open
Abstract
When performing the diffuse optical tomography (DOT) of the breast, the mismatch between the forward model and the experimental conditions will significantly hinder the reconstruction accuracy. Therefore, the reference measurement is commonly used to calibrate the measured data before the reconstruction. However, it is complicated to customize corresponding reference phantoms based on the breast shape and background optical parameters of different subjects in clinical trials. Furthermore, although high-density (HD) DOT configuration has been proven to improve imaging quality, a large number of source-detector (SD) pairs also increase the difficulty of multi-channel correction. To enhance the applicability of the breast DOT, a data self-calibration method based on an HD parallel-plate DOT system is proposed in this paper to replace the conventional relative measurement on a reference phantom. The reference predicted data can be constructed directly from the measurement data with the support of the HD-DOT system, which has nearly a hundred sets of measurements at each SD distance. The proposed scheme has been validated by Monte Carlo (MC) simulation, breast-size phantom experiments, and clinical trials, exhibiting the feasibility in ensuring the quality of the DOT reconstruction while effectively reducing the complexity associated with relative measurements on reference phantoms.
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Affiliation(s)
- Xin Wang
- School of Life Science and Technology, Xidian University, Xi’an, China
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, Xi’an, China
| | - Rui Hu
- School of Life Science and Technology, Xidian University, Xi’an, China
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, Xi’an, China
| | - Yirong Wang
- Department of Nuclear Medicine, Xijing Hospital, Fourth Military Medical University, Xi’an, China
| | - Qiang Yan
- School of Life Science and Technology, Xidian University, Xi’an, China
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, Xi’an, China
| | - Yihan Wang
- School of Life Science and Technology, Xidian University, Xi’an, China
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, Xi’an, China
- *Correspondence: Yihan Wang, ; Shouping Zhu,
| | - Fei Kang
- Department of Nuclear Medicine, Xijing Hospital, Fourth Military Medical University, Xi’an, China
| | - Shouping Zhu
- School of Life Science and Technology, Xidian University, Xi’an, China
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, Xi’an, China
- *Correspondence: Yihan Wang, ; Shouping Zhu,
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Korn L, Dual S, Rixen J, Meboldt M, Leonhardt S, Schmid Daners M, Walter M. Dual-modality Volume Measurement integrated on a Ventricular Assist Device. IEEE Trans Biomed Eng 2021; 69:1151-1161. [PMID: 34559630 DOI: 10.1109/tbme.2021.3115019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
OBJECTIVE Ventricular assist devices (VADs) are implanted in patients suffering from end-stage heart failure to sustain the blood circulation. Real-time volume measurement could be a valuable tool to monitor patients and enable physiological control strategies to provide individualized therapy. However, volume measurement using one sensor modality requires re-calibration in the critical time post VAD implantation. METHODS To overcome this limitation, we have integrated ultrasound and impedance volume measurement techniques into a cannula of an apical VAD. We tested both modalities across a volume range from 140-420 mL using two differently sized and shaped biventricular silicon heart phantoms, which were subjected to physiological pressures in an in-vitro test bench. We compared results from standard calibrated measurements with calculations found by a quadratic optimization for the single modality and their combination (dual-modality) and validated the results using twofold cross-validation. RESULTS The dual-modality approach resulted in most favorable limits of agreement (LOA) of -0.83 ± 1.54% compared to -13.88 ± 5.90% for ultrasound and -43.45 ± 10.28% for electric impedance, separately. CONCLUSION The results of the dual-modality approach were as accurate as the standard calibrated measurement and valid over a large range of volumes (140-420 mL). In this in-vitro study, we show how a dual-modality ventricular volume measurement of ultrasound and electric impedance increases the robustness and renders calibration obsolete. SIGNIFICANCE Ventricular volumes could be measured accurately in the critical period post VAD implantation despite ventricular remodeling.
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Li S, Zhang M, Zhu Q. Ultrasound segmentation-guided edge artifact reduction in diffuse optical tomography using connected component analysis. BIOMEDICAL OPTICS EXPRESS 2021; 12:5320-5336. [PMID: 34513259 PMCID: PMC8407838 DOI: 10.1364/boe.428107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 07/09/2021] [Accepted: 07/12/2021] [Indexed: 05/02/2023]
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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Affiliation(s)
- Shuying Li
- Department of Biomedical Engineering, Washington University in St. Louis, 1 Brookings Dr, St. Louis 63130, USA
| | - Menghao Zhang
- Department of Electrical & Systems Engineering, Washington University in St. Louis, 1 Brookings Dr, St. Louis 63130, USA
| | - Quing Zhu
- Department of Biomedical Engineering, Washington University in St. Louis, 1 Brookings Dr, St. Louis 63130, USA
- Department of Radiology, Washington University School of Medicine, 660 S Euclid Ave, St. Louis 63110, USA
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Jiang Y, Machida M, Todoroki N. Diffuse optical tomography by simulated annealing via a spin Hamiltonian. JOURNAL OF THE OPTICAL SOCIETY OF AMERICA. A, OPTICS, IMAGE SCIENCE, AND VISION 2021; 38:1032-1040. [PMID: 34263759 DOI: 10.1364/josaa.421219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 05/31/2021] [Indexed: 06/13/2023]
Abstract
Diffuse optical tomography (DOT) is an imaging modality that uses near-infrared light. Although iterative numerical schemes are commonly used for its inverse problem, correct solutions are not obtained unless good initial guesses are chosen. We propose a numerical scheme of DOT, which works even when good initial guesses of optical parameters are not available. We use simulated annealing (SA), which is a method of the Markov-chain Monte Carlo. To implement SA for DOT, a spin Hamiltonian is introduced in the cost function, and the Metropolis algorithm or single-component Metropolis-Hastings algorithm is used. By numerical experiments, it is shown that an initial random spin configuration is brought to a converged configuration by SA, and targets in the medium are reconstructed. The proposed numerical method solves the inverse problem for DOT by finding the ground state of a spin Hamiltonian with SA.
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