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Xue M, Li S, Zhu Q. Improving diffuse optical tomography imaging quality using APU-Net: an attention-based physical U-Net model. JOURNAL OF BIOMEDICAL OPTICS 2024; 29:086001. [PMID: 39070721 PMCID: PMC11272096 DOI: 10.1117/1.jbo.29.8.086001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 05/28/2024] [Accepted: 07/01/2024] [Indexed: 07/30/2024]
Abstract
Significance Traditional diffuse optical tomography (DOT) reconstructions are hampered by image artifacts arising from factors such as DOT sources being closer to shallow lesions, poor optode-tissue coupling, tissue heterogeneity, and large high-contrast lesions lacking information in deeper regions (known as shadowing effect). Addressing these challenges is crucial for improving the quality of DOT images and obtaining robust lesion diagnosis. Aim We address the limitations of current DOT imaging reconstruction by introducing an attention-based U-Net (APU-Net) model to enhance the image quality of DOT reconstruction, ultimately improving lesion diagnostic accuracy. Approach We designed an APU-Net model incorporating a contextual transformer attention module to enhance DOT reconstruction. The model was trained on simulation and phantom data, focusing on challenges such as artifact-induced distortions and lesion-shadowing effects. The model was then evaluated by the clinical data. Results Transitioning from simulation and phantom data to clinical patients' data, our APU-Net model effectively reduced artifacts with an average artifact contrast decrease of 26.83% and improved image quality. In addition, statistical analyses revealed significant contrast improvements in depth profile with an average contrast increase of 20.28% and 45.31% for the second and third target layers, respectively. These results highlighted the efficacy of our approach in breast cancer diagnosis. Conclusions The APU-Net model improves the image quality of DOT reconstruction by reducing DOT image artifacts and improving the target depth profile.
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Affiliation(s)
- Minghao Xue
- Washington University in St. Louis, Biomedical Engineering Department, St. Louis, Missouri, United States
| | - Shuying Li
- Boston University, Electrical and Computer Engineering Department, Boston, Massachusetts, United States
| | - Quing Zhu
- Washington University in St. Louis, Biomedical Engineering Department, St. Louis, Missouri, United States
- Washington University in St. Louis, Radiology Department, St. Louis, Missouri, United States
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2
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Li S, Zhang M, Xue M, Zhu Q. Real-time breast lesion classification combining diffuse optical tomography frequency domain data and BI-RADS assessment. JOURNAL OF BIOPHOTONICS 2024; 17:e202300483. [PMID: 38430216 PMCID: PMC11065578 DOI: 10.1002/jbio.202300483] [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] [Received: 11/21/2023] [Revised: 02/07/2024] [Accepted: 02/08/2024] [Indexed: 03/03/2024]
Abstract
Ultrasound (US)-guided diffuse optical tomography (DOT) has demonstrated potential for breast cancer diagnosis, in which real-time or near real-time diagnosis with high accuracy is desired. However, DOT's relatively slow data processing and image reconstruction speeds have hindered real-time diagnosis. Here, we propose a real-time classification scheme that combines US breast imaging reporting and data system (BI-RADS) readings and DOT frequency domain measurements. A convolutional neural network is trained to generate malignancy probability scores from DOT measurements. Subsequently, these scores are integrated with BI-RADS assessments using a support vector machine classifier, which then provides the final diagnostic output. An area under the receiver operating characteristic curve of 0.978 is achieved in distinguishing between benign and malignant breast lesions in patient data without image reconstruction.
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Affiliation(s)
- Shuying Li
- Department of Biomedical Engineering, Washington University in St. Louis, 63130 St. Louis, USA
| | - Menghao Zhang
- Department of Electrical & Systems Engineering, Washington University in St. Louis, 63130 St. Louis, USA
| | - Minghao Xue
- Department of Biomedical Engineering, Washington University in St. Louis, 63130 St. Louis, USA
| | - Quing Zhu
- Department of Biomedical Engineering, Washington University in St. Louis, 63130 St. Louis, USA
- Department of Electrical & Systems Engineering, Washington University in St. Louis, 63130 St. Louis, USA
- Department of Radiology, Washington University School of Medicine, 63110 St. Louis, USA
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3
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Gao C, Xiu J, Huang C, Ma K, Li T. Reliability Evaluation for Continuous-Wave Functional Near-Infrared Spectroscopy Systems: Comprehensive Testing from Bench Characterization to Human Test. SENSORS (BASEL, SWITZERLAND) 2024; 24:2045. [PMID: 38610255 PMCID: PMC11014010 DOI: 10.3390/s24072045] [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] [Received: 02/23/2024] [Revised: 03/18/2024] [Accepted: 03/18/2024] [Indexed: 04/14/2024]
Abstract
In recent years, biomedical optics technology has developed rapidly. The current widespread use of biomedical optics was made possible by the invention of optical instruments. The advantages of being non-invasive, portable, effective, low cost, and less susceptible to system noise have led to the rapid development of functional near-infrared spectroscopy (fNIRS) technology for hemodynamics detection, especially in the field of functional brain imaging. At the same time, laboratories and companies have developed various fNIRS-based systems. The safety, stability, and efficacy of fNIRS systems are key performance indicators. However, there is still a lack of comprehensive and systematic evaluation methods for fNIRS instruments. This study uses the fNIRS system developed in our laboratory as the test object. The test method established in this study includes system validation and performance testing to comprehensively assess fNIRS systems' reliability. These methods feature low cost and high practicality. Based on this study, existing or newly developed systems can be comprehensively and easily evaluated in the laboratory or workspace.
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Affiliation(s)
- Chenyang Gao
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300192, China; (C.G.); (J.X.)
| | - Jia Xiu
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300192, China; (C.G.); (J.X.)
| | - Chong Huang
- Philips North America, Carlsbad, CA 92011, USA;
| | - Kaixue Ma
- School of Microelectronics, Tianjin University, Tianjin 300072, China;
| | - Ting Li
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300192, China; (C.G.); (J.X.)
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Xue M, Zhang M, Li S, Zou Y, Zhu Q. Automated pipeline for breast cancer diagnosis using US assisted diffuse optical tomography. BIOMEDICAL OPTICS EXPRESS 2023; 14:6072-6087. [PMID: 38021111 PMCID: PMC10659805 DOI: 10.1364/boe.502244] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 10/11/2023] [Accepted: 10/11/2023] [Indexed: 12/01/2023]
Abstract
Ultrasound (US)-guided diffuse optical tomography (DOT) is a portable and non-invasive imaging modality for breast cancer diagnosis and treatment response monitoring. However, DOT data pre-processing and imaging reconstruction often require labor intensive manual processing which hampers real-time diagnosis. In this study, we aim at providing an automated US-assisted DOT pre-processing, imaging and diagnosis pipeline to achieve near real-time diagnosis. We have developed an automated DOT pre-processing method including motion detection, mismatch classification using deep-learning approach, and outlier removal. US-lesion information needed for DOT reconstruction was extracted by a semi-automated lesion segmentation approach combined with a US reading algorithm. A deep learning model was used to evaluate the quality of the reconstructed DOT images and a two-step deep-learning model developed earlier is implemented to provide final diagnosis based on US imaging features and DOT measurements and imaging results. The presented US-assisted DOT pipeline accurately processed the DOT measurements and reconstruction and reduced the procedure time to 2 to 3 minutes while maintained a comparable classification result with manually processed dataset.
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Affiliation(s)
- Minghao Xue
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Menghao Zhang
- Department of Electrical & Systems Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Shuying Li
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Yun Zou
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Quing Zhu
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
- Department of Electrical & Systems Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
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5
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Poplack SP, Park EY, Ferrara KW. Optical Breast Imaging: A Review of Physical Principles, Technologies, and Clinical Applications. JOURNAL OF BREAST IMAGING 2023; 5:520-537. [PMID: 37981994 PMCID: PMC10655724 DOI: 10.1093/jbi/wbad057] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2023]
Abstract
Optical imaging involves the propagation of light through tissue. Current optical breast imaging technologies, including diffuse optical spectroscopy, diffuse optical tomography, and photoacoustic imaging, capitalize on the selective absorption of light in the near-infrared spectrum by deoxygenated and oxygenated hemoglobin. They provide information on the morphological and functional characteristics of different tissues based on their varied interactions with light, including physiologic information on lesion vascular content and anatomic information on tissue vascularity. Fluorescent contrast agents, such as indocyanine green, are used to visualize specific tissues, molecules, or proteins depending on how and where the agent accumulates. In this review, we describe the physical principles, spectrum of technologies, and clinical applications of the most common optical systems currently being used or developed for breast imaging. Most notably, US co-registered photoacoustic imaging and US-guided diffuse optical tomography have demonstrated efficacy in differentiating benign from malignant breast masses, thereby improving the specificity of diagnostic imaging. Diffuse optical tomography and diffuse optical spectroscopy have shown promise in assessing treatment response to preoperative systemic therapy, and photoacoustic imaging and diffuse optical tomography may help predict tumor phenotype. Lastly, fluorescent imaging using indocyanine green dye performs comparably to radioisotope mapping of sentinel lymph nodes and appears to improve the outcomes of autologous tissue flap breast reconstruction.
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Affiliation(s)
- Steven P. Poplack
- Stanford University School of Medicine, Department of Radiology, Palo Alto, CA, USA
| | - Eun-Yeong Park
- Stanford University School of Medicine, Department of Radiology, Palo Alto, CA, USA
| | - Katherine W. Ferrara
- Stanford University School of Medicine, Department of Radiology, Palo Alto, CA, USA
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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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Momtahen S, Momtahen M, Ramaseshan R, Golnaraghi F. An Optical Sensory System for Assessment of Residual Cancer Burden in Breast Cancer Patients Undergoing Neoadjuvant Chemotherapy. SENSORS (BASEL, SWITZERLAND) 2023; 23:5761. [PMID: 37420927 DOI: 10.3390/s23125761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 05/24/2023] [Accepted: 06/10/2023] [Indexed: 07/09/2023]
Abstract
Breast cancer patients undergoing neoadjuvant chemotherapy (NAC) require precise and accurate evaluation of treatment response. Residual cancer burden (RCB) is a prognostic tool widely used to estimate survival outcomes in breast cancer. In this study, we introduced a machine-learning-based optical biosensor called the Opti-scan probe to assess residual cancer burden in breast cancer patients undergoing NAC. The Opti-scan probe data were acquired from 15 patients (mean age: 61.8 years) before and after each cycle of NAC. Using regression analysis with k-fold cross-validation, we calculated the optical properties of healthy and unhealthy breast tissues. The ML predictive model was trained on the optical parameter values and breast cancer imaging features obtained from the Opti-scan probe data to calculate RCB values. The results show that the ML model achieved a high accuracy of 0.98 in predicting RCB number/class based on the changes in optical properties measured by the Opti-scan probe. These findings suggest that our ML-based Opti-scan probe has considerable potential as a valuable tool for the assessment of breast cancer response after NAC and to guide treatment decisions. Therefore, it could be a promising, non-invasive, and accurate method for monitoring breast cancer patient's response to NAC.
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Affiliation(s)
- Shadi Momtahen
- School of Mechatronic Systems Engineering, Simon Fraser University, Surrey, BC V3T 0A3, Canada
| | - Maryam Momtahen
- School of Mechatronic Systems Engineering, Simon Fraser University, Surrey, BC V3T 0A3, Canada
| | - Ramani Ramaseshan
- School of Mechatronic Systems Engineering, Simon Fraser University, Surrey, BC V3T 0A3, Canada
- Department of Medical Physics, BC Cancer, Abbotsford, BC V2S 0C2, Canada
| | - Farid Golnaraghi
- School of Mechatronic Systems Engineering, Simon Fraser University, Surrey, BC V3T 0A3, Canada
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8
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Zhang M, Xue M, Li S, Zou Y, Zhu Q. Fusion deep learning approach combining diffuse optical tomography and ultrasound for improving breast cancer classification. BIOMEDICAL OPTICS EXPRESS 2023; 14:1636-1646. [PMID: 37078047 PMCID: PMC10110311 DOI: 10.1364/boe.486292] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 02/25/2023] [Accepted: 03/04/2023] [Indexed: 05/03/2023]
Abstract
Diffuse optical tomography (DOT) is a promising technique that provides functional information related to tumor angiogenesis. However, reconstructing the DOT function map of a breast lesion is an ill-posed and underdetermined inverse process. A co-registered ultrasound (US) system that provides structural information about the breast lesion can improve the localization and accuracy of DOT reconstruction. Additionally, the well-known US characteristics of benign and malignant breast lesions can further improve cancer diagnosis based on DOT alone. Inspired by a fusion model deep learning approach, we combined US features extracted by a modified VGG-11 network with images reconstructed from a DOT deep learning auto-encoder-based model to form a new neural network for breast cancer diagnosis. The combined neural network model was trained with simulation data and fine-tuned with clinical data: it achieved an AUC of 0.931 (95% CI: 0.919-0.943), superior to those achieved using US images alone (0.860) or DOT images alone (0.842).
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Affiliation(s)
- Menghao Zhang
- Electrical and System Engineering Department, Washington University in St. Louis, 1 Brooking Dr, St. Louis, MO 63130, USA
| | - Minghao Xue
- Biomedical Engineering Department, Washington University in St. Louis, 1 Brooking Dr, St. Louis, MO 63130, USA
| | - Shuying Li
- Biomedical Engineering Department, Washington University in St. Louis, 1 Brooking Dr, St. Louis, MO 63130, USA
| | - Yun Zou
- Biomedical Engineering Department, Washington University in St. Louis, 1 Brooking Dr, St. Louis, MO 63130, USA
| | - Quing Zhu
- Electrical and System Engineering Department, Washington University in St. Louis, 1 Brooking Dr, St. Louis, MO 63130, USA
- Biomedical Engineering Department, Washington University in St. Louis, 1 Brooking Dr, St. Louis, MO 63130, USA
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9
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Pavlov MV, Bavrina AP, Plekhanov VI, Golubyatnikov GY, Orlova AG, Subochev PV, Davydova DA, Turchin IV, Maslennikova AV. Changes in the tumor oxygenation but not in the tumor volume and tumor vascularization reflect early response of breast cancer to neoadjuvant chemotherapy. Breast Cancer Res 2023; 25:12. [PMID: 36717842 PMCID: PMC9887770 DOI: 10.1186/s13058-023-01607-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 01/17/2023] [Indexed: 01/31/2023] Open
Abstract
BACKGROUND Breast cancer neoadjuvant chemotherapy (NACT) allows for assessing tumor sensitivity to systemic treatment, planning adjuvant treatment and follow-up. However, a sufficiently large number of patients fail to achieve the desired level of pathological tumor response while optimal early response assessment methods have not been established now. In our study, we simultaneously assessed the early chemotherapy-induced changes in the tumor volume by ultrasound (US), the tumor oxygenation by diffuse optical spectroscopy imaging (DOSI), and the state of the tumor vascular bed by Doppler US to elaborate the predictive criteria of breast tumor response to treatment. METHODS A total of 133 patients with a confirmed diagnosis of invasive breast cancer stage II to III admitted to NACT following definitive breast surgery were enrolled, of those 103 were included in the final analysis. Tumor oxygenation by DOSI, tumor volume by US, and tumor vascularization by Doppler US were determined before the first and second cycle of NACT. After NACT completion, patients underwent surgery followed by pathological examination and assessment of the pathological tumor response. On the basis of these, data regression predictive models were created. RESULTS We observed changes in all three parameters 3 weeks after the start of the treatment. However, a high predictive potential for early assessment of tumor sensitivity to NACT demonstrated only the level of oxygenation, ΔStO2, (ρ = 0.802, p ≤ 0.01). The regression model predicts the tumor response with a high probability of a correct conclusion (89.3%). The "Tumor volume" model and the "Vascularization index" model did not accurately predict the absence of a pathological tumor response to treatment (60.9% and 58.7%, respectively), while predicting a positive response to treatment was relatively better (78.9% and 75.4%, respectively). CONCLUSIONS Diffuse optical spectroscopy imaging appeared to be a robust tool for early predicting breast cancer response to chemotherapy. It may help identify patients who need additional molecular genetic study of the tumor in order to find the source of resistance to treatment, as well as to correct the treatment regimen.
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Affiliation(s)
- Mikhail V. Pavlov
- Nizhny Novgorod Regional Clinical Oncology Dispensary, Delovaya St., 11/1, Nizhny Novgorod, Russia 603126
| | - Anna P. Bavrina
- grid.416347.30000 0004 0386 1631Privolzhsky Research Medical University, Minina Square, 10/1, Nizhny Novgorod, Russia 603950
| | - Vladimir I. Plekhanov
- grid.410472.40000 0004 0638 0147Institute of Applied Physics RAS, Ul’yanov Street, 46, Nizhny Novgorod, Russia 603950
| | - German Yu. Golubyatnikov
- grid.410472.40000 0004 0638 0147Institute of Applied Physics RAS, Ul’yanov Street, 46, Nizhny Novgorod, Russia 603950
| | - Anna G. Orlova
- grid.410472.40000 0004 0638 0147Institute of Applied Physics RAS, Ul’yanov Street, 46, Nizhny Novgorod, Russia 603950
| | - Pavel V. Subochev
- grid.410472.40000 0004 0638 0147Institute of Applied Physics RAS, Ul’yanov Street, 46, Nizhny Novgorod, Russia 603950
| | - Diana A. Davydova
- Nizhny Novgorod Regional Clinical Oncology Dispensary, Delovaya St., 11/1, Nizhny Novgorod, Russia 603126
| | - Ilya V. Turchin
- grid.410472.40000 0004 0638 0147Institute of Applied Physics RAS, Ul’yanov Street, 46, Nizhny Novgorod, Russia 603950
| | - Anna V. Maslennikova
- grid.416347.30000 0004 0386 1631Privolzhsky Research Medical University, Minina Square, 10/1, Nizhny Novgorod, Russia 603950 ,grid.28171.3d0000 0001 0344 908XNational Research Lobachevsky State University of Nizhny Novgorod, Gagarin Ave., 23, Nizhny Novgorod, Russia 603022
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Nouizi F, Brooks J, Zuro DM, Hui SK, Gulsen G. Development of a theranostic preclinical fluorescence molecular tomography/cone beam CT-guided irradiator platform. BIOMEDICAL OPTICS EXPRESS 2022; 13:6100-6112. [PMID: 36733750 PMCID: PMC9872876 DOI: 10.1364/boe.469559] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 09/21/2022] [Accepted: 09/22/2022] [Indexed: 05/11/2023]
Abstract
Image-guided small animal radiation research platforms allow more precise radiation treatment. Commercially available small animal X-ray irradiators are often equipped with a CT/cone-beam CT (CBCT) component for target guidance. Besides having poor soft-tissue contrast, CBCT unfortunately cannot provide molecular information due to its low sensitivity. Hence, there are extensive efforts to incorporate a molecular imaging component besides CBCT on these radiation therapy platforms. As an extension of these efforts, here we present a theranostic fluorescence tomography/CBCT-guided irradiator platform that provides both anatomical and molecular guidance, which can overcome the limitations of stand-alone CBCT. The performance of our hybrid system is validated using both tissue-like phantoms and mice ex vivo. Both studies show that fluorescence tomography can provide much more accurate quantitative results when CBCT-derived structural information is used to constrain the inverse problem. The error in the recovered fluorescence absorbance reduces nearly 10-fold for all cases, from approximately 60% down to 6%. This is very significant since high quantitative accuracy in molecular information is crucial to the correct assessment of the changes in tumor microenvironment related to radiation therapy.
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Affiliation(s)
- Farouk Nouizi
- Tu and Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California Irvine, CA 92697, USA
- Chao Family Comprehensive Cancer Center, University of California Irvine, CA 92697, USA
| | - Jamison Brooks
- Department of Radiation Oncology, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Darren M. Zuro
- Department of Radiation Oncology, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Susanta K. Hui
- Department of Radiation Oncology, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Gultekin Gulsen
- Tu and Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California Irvine, CA 92697, USA
- Chao Family Comprehensive Cancer Center, University of California Irvine, CA 92697, USA
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11
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Althobaiti M. In Silico Investigation of SNR and Dermis Sensitivity for Optimum Dual-Channel Near-Infrared Glucose Sensor Designs for Different Skin Colors. BIOSENSORS 2022; 12:805. [PMID: 36290941 PMCID: PMC9599199 DOI: 10.3390/bios12100805] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Revised: 09/25/2022] [Accepted: 09/26/2022] [Indexed: 06/16/2023]
Abstract
Diabetes is a serious health condition that requires patients to regularly monitor their blood glucose level, making the development of practical, compact, and non-invasive techniques essential. Optical glucose sensors-and, specifically, NIR sensors-have the advantages of being non-invasive, compact, inexpensive, and user-friendly devices. However, these sensors have low accuracy and are yet to be adopted by healthcare providers. In our previous work, we introduced a non-invasive dual-channel technique for NIR sensors, in which a long channel is utilized to measure the glucose level in the inner skin (dermis) layer, while a short channel is used to measure the noise signal of the superficial skin (epidermis) layer. In this work, we investigated the use of dual-NIR channels for patients with different skin colors (i.e., having different melanin concentrations). We also adopted a Monte Carlo simulation model that takes into consideration the differences between different skin layers, in terms of blood content, water content, melanin concentration in the epidermis layer, and skin optical proprieties. On the basis of the signal-to-noise ratio, as well as the sensitivities of both the epidermis and dermis layers, we suggest the selection of wavelengths and source-to-detector separation for optimal NIR channels under different skin melanin concentrations. This work facilitates the improved design of a compact and non-invasive NIR glucose sensor that can be utilized by patients with different skin colors.
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Affiliation(s)
- Murad Althobaiti
- Biomedical Engineering Department, College of Engineering, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
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12
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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:JBO-220081GRR. [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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Di Sciacca G, Maffeis G, Farina A, Dalla Mora A, Pifferi A, Taroni P, Arridge S. Evaluation of a pipeline for simulation, reconstruction, and classification in ultrasound-aided diffuse optical tomography of breast tumors. JOURNAL OF BIOMEDICAL OPTICS 2022; 27:JBO-210385GRR. [PMID: 35332743 PMCID: PMC8943242 DOI: 10.1117/1.jbo.27.3.036003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 02/28/2022] [Indexed: 06/01/2023]
Abstract
SIGNIFICANCE Diffuse optical tomography is an ill-posed problem. Combination with ultrasound can improve the results of diffuse optical tomography applied to the diagnosis of breast cancer and allow for classification of lesions. AIM To provide a simulation pipeline for the assessment of reconstruction and classification methods for diffuse optical tomography with concurrent ultrasound information. APPROACH A set of breast digital phantoms with benign and malignant lesions was simulated building on the software VICTRE. Acoustic and optical properties were assigned to the phantoms for the generation of B-mode images and optical data. A reconstruction algorithm based on a two-region nonlinear fitting and incorporating the ultrasound information was tested. Machine learning classification methods were applied to the reconstructed values to discriminate lesions into benign and malignant after reconstruction. RESULTS The approach allowed us to generate realistic US and optical data and to test a two-region reconstruction method for a large number of realistic simulations. When information is extracted from ultrasound images, at least 75% of lesions are correctly classified. With ideal two-region separation, the accuracy is higher than 80%. CONCLUSIONS A pipeline for the generation of realistic ultrasound and diffuse optics data was implemented. Machine learning methods applied to a optical reconstruction with a nonlinear optical model and morphological information permit to discriminate malignant lesions from benign ones.
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Affiliation(s)
- Giuseppe Di Sciacca
- University College London, Department of Computer Science, London, United Kingdom
- Politecnico di Milano, Dipartimento di Fisica, Milano, Italy
| | - Giulia Maffeis
- Politecnico di Milano, Dipartimento di Fisica, Milano, Italy
| | - Andrea Farina
- Istituto di Fotonica e Nanotecnologie, Consiglio Nazionale delle Ricerche, Milano, Italy
| | | | - Antonio Pifferi
- Politecnico di Milano, Dipartimento di Fisica, Milano, Italy
- Istituto di Fotonica e Nanotecnologie, Consiglio Nazionale delle Ricerche, Milano, Italy
| | - Paola Taroni
- Politecnico di Milano, Dipartimento di Fisica, Milano, Italy
- Istituto di Fotonica e Nanotecnologie, Consiglio Nazionale delle Ricerche, Milano, Italy
| | - Simon Arridge
- University College London, Department of Computer Science, London, United Kingdom
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14
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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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15
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Smith JT, Ochoa M, Faulkner D, Haskins G, Intes X. Deep learning in macroscopic diffuse optical imaging. JOURNAL OF BIOMEDICAL OPTICS 2022; 27:JBO-210288VRR. [PMID: 35218169 PMCID: PMC8881080 DOI: 10.1117/1.jbo.27.2.020901] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 02/09/2022] [Indexed: 05/02/2023]
Abstract
SIGNIFICANCE Biomedical optics system design, image formation, and image analysis have primarily been guided by classical physical modeling and signal processing methodologies. Recently, however, deep learning (DL) has become a major paradigm in computational modeling and has demonstrated utility in numerous scientific domains and various forms of data analysis. AIM We aim to comprehensively review the use of DL applied to macroscopic diffuse optical imaging (DOI). APPROACH First, we provide a layman introduction to DL. Then, the review summarizes current DL work in some of the most active areas of this field, including optical properties retrieval, fluorescence lifetime imaging, and diffuse optical tomography. RESULTS The advantages of using DL for DOI versus conventional inverse solvers cited in the literature reviewed herein are numerous. These include, among others, a decrease in analysis time (often by many orders of magnitude), increased quantitative reconstruction quality, robustness to noise, and the unique capability to learn complex end-to-end relationships. CONCLUSIONS The heavily validated capability of DL's use across a wide range of complex inverse solving methodologies has enormous potential to bring novel DOI modalities, otherwise deemed impractical for clinical translation, to the patient's bedside.
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Affiliation(s)
- Jason T. Smith
- Rensselaer Polytechnic Institute, Department of Biomedical Engineering, Troy, New York, United States
| | - Marien Ochoa
- Rensselaer Polytechnic Institute, Department of Biomedical Engineering, Troy, New York, United States
| | - Denzel Faulkner
- Rensselaer Polytechnic Institute, Department of Biomedical Engineering, Troy, New York, United States
| | - Grant Haskins
- Rensselaer Polytechnic Institute, Department of Biomedical Engineering, Troy, New York, United States
| | - Xavier Intes
- Rensselaer Polytechnic Institute, Center for Modeling, Simulation and Imaging for Medicine, Troy, New York, United States
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16
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Prospective assessment of adjunctive ultrasound-guided diffuse optical tomography in women undergoing breast biopsy: Impact on BI-RADS assessments. Eur J Radiol 2021; 145:110029. [PMID: 34801874 DOI: 10.1016/j.ejrad.2021.110029] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 10/22/2021] [Accepted: 11/09/2021] [Indexed: 11/23/2022]
Abstract
PURPOSE To assess the impact of adjunctive ultrasound guided diffuse optical tomography (US-guided DOT) on BI-RADS assessment in women undergoing US-guided breast biopsy. METHOD This prospective study enrolled women referred for US-guided breast biopsy between 3/5/2019 and 3/19/2020. Participants underwent US-guided DOT immediately before biopsy. The US-guided DOT acquisition generated average maximum total hemoglobin (HbT) spatial maps and quantitative HbT values. Four radiologists blinded to histopathology assessed conventional imaging (CI) to assign a CI BI-RADS assessment and then integrated DOT information in assigning a CI&DOT BI-RADS assessment. HbT was compared between benign and malignant lesions using an ANOVA test and Tukey's test. Benign biopsies were tabulated, deeming BI-RADS ≥ 4A as positive. Reader agreement was assessed. RESULTS Among 61 included women (mean age 48 years), biopsy demonstrated 15 (24.6%) malignant and 46 (75.4%) benign lesions. Mean HbT was 55.3 ± 22.6 µM in benign lesions versus 85.4 ± 15.6 µM in cancers (p < .001). HbT threshold of 78.5 µM achieved sensitivity 80% (12/15) and specificity 89% (41/46) for malignancy. Across readers and patients, 197 pairs of CI BI-RADS and CI&DOT BI-RADS assessments were assigned. Adjunctive US-guided DOT achieved a net decrease in 23.5% (31/132) of suspicious (CI BI-RADS ≥ 4A) assessments of benign lesions (34 correct downgrades and 3 incorrect upgrades). 38.3% (31/81) of 4A assessments were appropriately downgraded. No cancer was downgraded to a non-actionable assessment. Interreader agreement analysis demonstrated kappa = 0.48-0.53 for CI BI-RADS and kappa = 0.28-0.44 for CI&DOT BI-RADS. CONCLUSIONS Integration of US-guided DOT information achieved a 23.5% reduction in suspicious BI-RADS assessments for benign lesions. Larger studies are warranted, with attention to improved reader agreement.
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17
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Zhang M, Li S, Zou Y, Zhu Q. Deep learning-based method to accurately estimate breast tissue optical properties in the presence of the chest wall. JOURNAL OF BIOMEDICAL OPTICS 2021; 26:JBO-210118RR. [PMID: 34672146 PMCID: PMC8527162 DOI: 10.1117/1.jbo.26.10.106004] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Accepted: 08/30/2021] [Indexed: 05/02/2023]
Abstract
SIGNIFICANCE In general, image reconstruction methods used in diffuse optical tomography (DOT) are based on diffusion approximation, and they consider the breast tissue as a homogenous, semi-infinite medium. However, the semi-infinite medium assumption used in DOT reconstruction is not valid when the chest wall is underneath the breast tissue. AIM We aim to reduce the chest wall's effect on the estimated average optical properties of breast tissue and obtain accurate forward model for DOT reconstruction. APPROACH We propose a deep learning-based neural network approach where a convolution neural network (CNN) is trained to simultaneously obtain accurate optical property values for both the breast tissue and the chest wall. RESULTS The CNN model shows great promise in reducing errors in estimating the optical properties of the breast tissue in the presence of a shallow chest wall. For patient data, the CNN model predicted the breast tissue optical absorption coefficient, which was independent of chest wall depth. CONCLUSIONS Our proposed method can be readily used in DOT and diffuse spectroscopy measurements to improve the accuracy of estimated tissue optical properties.
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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
| | - Yun Zou
- 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
- Address all correspondence to Quing Zhu,
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18
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Di Sciacca G, Di Sieno L, Farina A, Lanka P, Venturini E, Panizza P, Dalla Mora A, Pifferi A, Taroni P, Arridge SR. Enhanced diffuse optical tomographic reconstruction using concurrent ultrasound information. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2021; 379:20200195. [PMID: 34218668 PMCID: PMC8255947 DOI: 10.1098/rsta.2020.0195] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 02/25/2021] [Indexed: 05/26/2023]
Abstract
Multimodal imaging is an active branch of research as it has the potential to improve common medical imaging techniques. Diffuse optical tomography (DOT) is an example of a low resolution, functional imaging modality that typically has very low resolution due to the ill-posedness of its underlying inverse problem. Combining the functional information of DOT with a high resolution structural imaging modality has been studied widely. In particular, the combination of DOT with ultrasound (US) could serve as a useful tool for clinicians for the formulation of accurate diagnosis of breast lesions. In this paper, we propose a novel method for US-guided DOT reconstruction using a portable time-domain measurement system. B-mode US imaging is used to retrieve morphological information on the probed tissues by means of a semi-automatical segmentation procedure based on active contour fitting. A two-dimensional to three-dimensional extrapolation procedure, based on the concept of distance transform, is then applied to generate a three-dimensional edge-weighting prior for the regularization of DOT. The reconstruction procedure has been tested on experimental data obtained on specifically designed dual-modality silicon phantoms. Results show a substantial quantification improvement upon the application of the implemented technique. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'.
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Affiliation(s)
- G. Di Sciacca
- Department of Computer Science, University College London, London WC1E 6BT, UK
| | - L. Di Sieno
- Dipartimento di Fisica, Politecnico di Milano, Piazza Leonardo da Vinci, 32 20133 Milano, Italy
| | - A. Farina
- Consiglio Nazionale delle Ricerche, Istituto di Fotonica e Nanotecnologie, Piazza Leonardo da Vinci, 32 20133 Milano, Italy
| | - P. Lanka
- Dipartimento di Fisica, Politecnico di Milano, Piazza Leonardo da Vinci, 32 20133 Milano, Italy
| | - E. Venturini
- Breast Imaging Unit, San Raffaele Scientific Hospital, Milano, Italy
| | - P. Panizza
- Breast Imaging Unit, San Raffaele Scientific Hospital, Milano, Italy
| | - A. Dalla Mora
- Dipartimento di Fisica, Politecnico di Milano, Piazza Leonardo da Vinci, 32 20133 Milano, Italy
| | - A. Pifferi
- Dipartimento di Fisica, Politecnico di Milano, Piazza Leonardo da Vinci, 32 20133 Milano, Italy
| | - P. Taroni
- Dipartimento di Fisica, Politecnico di Milano, Piazza Leonardo da Vinci, 32 20133 Milano, Italy
| | - S. R. Arridge
- Department of Computer Science, University College London, London WC1E 6BT, UK
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19
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Zhu Q, Ademuyiwa FO, Young C, Appleton C, Covington MF, Ma C, Sanati S, Hagemann IS, Mostafa A, Uddin KMS, Grigsby I, Frith AE, Hernandez-Aya LF, Poplack SS. Early Assessment Window for Predicting Breast Cancer Neoadjuvant Therapy using Biomarkers, Ultrasound, and Diffuse Optical Tomography. Breast Cancer Res Treat 2021; 188:615-630. [PMID: 33970392 DOI: 10.1007/s10549-021-06239-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2021] [Accepted: 04/20/2021] [Indexed: 12/28/2022]
Abstract
PURPOSE The purpose of the study was to assess the utility of tumor biomarkers, ultrasound (US) and US-guided diffuse optical tomography (DOT) in early prediction of breast cancer response to neoadjuvant therapy (NAT). METHODS This prospective HIPAA compliant study was approved by the institutional review board. Forty one patients were imaged with US and US-guided DOT prior to NAT, at completion of the first three treatment cycles, and prior to definitive surgery from February 2017 to January 2020. Miller-Payne grading was used to assess pathologic response. Receiver operating characteristic curves (ROCs) were derived from logistic regression using independent variables, including: tumor biomarkers, US maximum diameter, percentage reduction of the diameter (%US), pretreatment maximum total hemoglobin concentration (HbT) and percentage reduction in HbT (%HbT) at different treatment time points. Resulting ROCs were compared using area under the curve (AUC). Statistical significance was tested using two-sided two-sample student t-test with P < 0.05 considered statistically significant. Logistic regression was used for ROC analysis. RESULTS Thirty-eight patients (mean age = 47, range 24-71 years) successfully completed the study, including 15 HER2 + of which 11 were ER + ; 12 ER + or PR + /HER2-, and 11 triple negative. The combination of HER2 and ER biomarkers, %HbT at the end of cycle 1 (EOC1) and %US (EOC1) provided the best early prediction, AUC = 0.941 (95% CI 0.869-1.0). Similarly an AUC of 0.910 (95% CI 0.810-1.0) with %US (EOC1) and %HbT (EOC1) can be achieved independent of HER2 and ER status. The most accurate prediction, AUC = 0.974 (95% CI 0.933-1.0), was achieved with %US at EOC1 and %HbT (EOC3) independent of biomarker status. CONCLUSION The combined use of tumor HER2 and ER status, US, and US-guided DOT may provide accurate prediction of NAT response as early as the completion of the first treatment cycle. CLINICAL TRIAL REGISTRATION NUMBER NCT02891681. https://clinicaltrials.gov/ct2/show/NCT02891681 , Registration time: September 7, 2016.
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Affiliation(s)
- Quing Zhu
- Biomedical Engineering and Radiology, Washington University in St Louis, One Brookings Drive, Mail Box 1097, Whitaker Hall 200F, St. Louis, MO, 63130, USA. .,Washington University School of Medicine in St Louis, St. Louis, USA.
| | - Foluso O Ademuyiwa
- Medical Oncology, Washington University School of Medicine in St Louis, St. Louis, USA
| | - Catherine Young
- Washington Baylor Scott & White Health, Medical Center, Texas, Dallas, USA
| | - Catherine Appleton
- Diagnostic Imaging Associates, Ltd. St. Luke's Hospital, Chesterfield, USA
| | - Matthew F Covington
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, USA
| | - Cynthia Ma
- Medical Oncology, Washington University School of Medicine in St Louis, St. Louis, USA
| | - Souzan Sanati
- Pathology, Department of Pathology, Cedars-Sinai Medical Center, Los Angeles, USA
| | - Ian S Hagemann
- Washington University School of Medicine in St Louis, St. Louis, USA
| | - Atahar Mostafa
- Biomedical Engineering and Radiology, Washington University in St Louis, One Brookings Drive, Mail Box 1097, Whitaker Hall 200F, St. Louis, MO, 63130, USA
| | - K M Shihab Uddin
- Biomedical Engineering and Radiology, Washington University in St Louis, One Brookings Drive, Mail Box 1097, Whitaker Hall 200F, St. Louis, MO, 63130, USA
| | - Isabella Grigsby
- Medical Oncology, Washington University School of Medicine in St Louis, St. Louis, USA
| | - Ashley E Frith
- Medical Oncology, Washington University School of Medicine in St Louis, St. Louis, USA
| | | | - Steven S Poplack
- Washington University School of Medicine in St Louis, St. Louis, USA.,Radiology, Stanford University, Stanford, USA
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20
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Pal UM, Nayak A, Medisetti T, Gogoi G, Shekhar H, Prasad MSN, Vaidya JS, Pandya HJ. Hybrid Spectral-IRDx: Near-IR and Ultrasound Attenuation System for Differentiating Breast Cancer From Adjacent Normal Tissue. IEEE Trans Biomed Eng 2021; 68:3554-3563. [PMID: 33945469 DOI: 10.1109/tbme.2021.3077582] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVE While performing surgical excision for breast cancer (lumpectomy), it is important to ensure a clear margin of normal tissue around the cancer to achieve complete resection. The current standard is histopathology; however, it is time-consuming and labour-intensive requiring skilled personnel. METHOD We describe a Hybrid Spectral-IRDx - a combination of the previously reported Spectral-IRDx tool with multimodal ultrasound and NIR spectroscopy techniques. We show how this portable, cost-effective, minimal-contact tool could provide rapid diagnosis of cancer using formalin-fixed (FF) and deparaffinized (DP) breast biopsy tissues. RESULTS Using this new tool, measurements were performed on cancerous/fibroadenoma and its adjacent normal tissues from the same patients (N = 14). The acoustic attenuation coefficient (α) and reduced scattering coefficient (µ's) (at 850, 940, and 1060 nm) for the cancerous/fibroadenoma tissues were reported to be higher compared to adjacent normal tissues, a basis of delineation. Comparing FF cancerous and adjacent normal tissue, the difference in µ's at 850 nm and 940 nm were statistically significant (p = 3.17e-2 and 7.94e-3 respectively). The difference in α between the cancerous and adjacent normal tissues for DP and FF tissues were also statistically significant (p = 2.85e-2 and 7.94e-3 respectively). Combining multimodal parameters α and µ's (at 940 nm) show highest statistical significance (p = 6.72e-4) between FF cancerous/fibroadenoma and adjacent normal tissues. CONCLUSION We show that Hybrid Spectral-IRDx can accurately delineate between cancerous and adjacent normal breast biopsy tissue. SIGNIFICANCE The results obtained establish the proof-of-principle and large-scale testing of this multimodal breast cancer diagnostic platform for core biopsy diagnosis.
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21
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Li J, Yang LZ, Ding WJ, Zhan MX, Fan LL, Wang JF, Shang HF, Ti G. Image reconstruction with the chaotic fiber laser in scattering media. APPLIED OPTICS 2021; 60:4004-4012. [PMID: 33983340 DOI: 10.1364/ao.420441] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 04/13/2021] [Indexed: 05/26/2023]
Abstract
The reconstruction of the size, position, optical properties, and structure of the object in scattering media was realized with a chaotic fiber laser. The light from the chaotic fiber laser was split into two parts. One part was used as the detection signal to detect the object, and the other was used as the reference signal; then, the two signals were cross correlated. The attenuation of light in scattering media was attributed to scattering and absorption. The theoretical model of the peak value of cross correlation of the chaotic signals as projection data were established by the attenuation law, and the filtered back-projection algorithms were used to realize the image reconstruction. The mean squared error, the normalized mean squared error, the peak signal-to-noise ratio, and the structural similarity index of the reconstructed image were analyzed. The results show that the high resolution of the reconstructed image benefits from the high signal-to-noise ratio with the chaotic fiber laser based on a delta-like cross-correlation function.
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22
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Janjic A, Cayoren M, Akduman I, Yilmaz T, Onemli E, Bugdayci O, Aribal ME. SAFE: A Novel Microwave Imaging System Design for Breast Cancer Screening and Early Detection-Clinical Evaluation. Diagnostics (Basel) 2021; 11:diagnostics11030533. [PMID: 33809770 PMCID: PMC8002280 DOI: 10.3390/diagnostics11030533] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 03/11/2021] [Accepted: 03/11/2021] [Indexed: 12/29/2022] Open
Abstract
SAFE (Scan and Find Early) is a novel microwave imaging device intended for breast cancer screening and early detection. SAFE is based on the use of harmless electromagnetic waves and can provide relevant initial diagnostic information without resorting to X-rays. Because of SAFE’s harmless effect on organic tissue, imaging can be performed repeatedly. In addition, the scanning process itself is not painful since breast compression is not required. Because of the absence of physical compression, SAFE can also detect tumors that are close to the thoracic wall. A total number of 115 patients underwent the SAFE scanning procedure, and the resultant images were compared with available magnetic resonance (MR), ultrasound, and mammography images in order to determine the correct detection rate. A sensitivity of 63% was achieved. Breast size influenced overall sensitivity, as sensitivity was lower in smaller breasts (51%) compared to larger ones (74%). Even though this is only a preliminary study, the results show promising concordance with clinical reports, thus encouraging further SAFE clinical studies.
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Affiliation(s)
- Aleksandar Janjic
- Mitos Medical Technologies, ITU Ayazaga Ari Teknokent 2-B Block 2-2-E, Maslak, 34469 Istanbul, Turkey; (M.C.); (I.A.); (T.Y.); (E.O.); (M.E.A.)
- Electrical and Electronics Engineering Faculty, Istanbul Technical University, Maslak, 34469 Istanbul, Turkey
- Correspondence:
| | - Mehmet Cayoren
- Mitos Medical Technologies, ITU Ayazaga Ari Teknokent 2-B Block 2-2-E, Maslak, 34469 Istanbul, Turkey; (M.C.); (I.A.); (T.Y.); (E.O.); (M.E.A.)
- Electrical and Electronics Engineering Faculty, Istanbul Technical University, Maslak, 34469 Istanbul, Turkey
| | - Ibrahim Akduman
- Mitos Medical Technologies, ITU Ayazaga Ari Teknokent 2-B Block 2-2-E, Maslak, 34469 Istanbul, Turkey; (M.C.); (I.A.); (T.Y.); (E.O.); (M.E.A.)
- Electrical and Electronics Engineering Faculty, Istanbul Technical University, Maslak, 34469 Istanbul, Turkey
| | - Tuba Yilmaz
- Mitos Medical Technologies, ITU Ayazaga Ari Teknokent 2-B Block 2-2-E, Maslak, 34469 Istanbul, Turkey; (M.C.); (I.A.); (T.Y.); (E.O.); (M.E.A.)
- Electrical and Electronics Engineering Faculty, Istanbul Technical University, Maslak, 34469 Istanbul, Turkey
| | - Emre Onemli
- Mitos Medical Technologies, ITU Ayazaga Ari Teknokent 2-B Block 2-2-E, Maslak, 34469 Istanbul, Turkey; (M.C.); (I.A.); (T.Y.); (E.O.); (M.E.A.)
- Electrical and Electronics Engineering Faculty, Istanbul Technical University, Maslak, 34469 Istanbul, Turkey
| | - Onur Bugdayci
- Department of Radiology, School of Medicine, Marmara University, Pendik, 34899 Istanbul, Turkey;
| | - Mustafa Erkin Aribal
- Mitos Medical Technologies, ITU Ayazaga Ari Teknokent 2-B Block 2-2-E, Maslak, 34469 Istanbul, Turkey; (M.C.); (I.A.); (T.Y.); (E.O.); (M.E.A.)
- Radiology Department, Breast Health Center, Altunizade Hospital, Acibadem M.A.A. University, Atasehir, 34684 Istanbul, Turkey
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23
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Li S, Huang K, Zhang M, Uddin KMS, Zhu Q. Effect and correction of optode coupling errors in breast imaging using diffuse optical tomography. BIOMEDICAL OPTICS EXPRESS 2021; 12:689-704. [PMID: 33680536 PMCID: PMC7901340 DOI: 10.1364/boe.411595] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 11/27/2020] [Accepted: 12/08/2020] [Indexed: 05/18/2023]
Abstract
In diffuse optical tomography (DOT) and spectroscopy (DOS) using handheld probes, tissue curvature can cause bad fiber-to-tissue contact. Understanding and minimizing image artifacts caused by these coupling errors would significantly improve DOT and DOS image quality. In this work, we utilized Monte Carlo simulations and experiments with gelatin-Intralipid phantoms to systematically study the influence of source or detector (optode) coupling errors. Optode coupling errors can increase the amplitude and decrease the phase of the measured diffuse reflectance, creating artifacts in the reconstructed absorption maps, such as hot spots on the edges. We propose an outlier removal algorithm that can correct these image artifacts, and we demonstrate its performance using simulations, phantom experiments, and breast patient data acquired with bad probe contact due to a dense or small breast. Further, we designed and implemented a new resistance-type thin-film force sensor array that provides real-time optode coupling feedback and guides the outlier removal to minimize optode coupling errors. Our approaches and study results have significant implications for reducing image artifacts arising from handheld probes, which are commonly used with mobile and wearable DOT and DOS devices.
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Affiliation(s)
- Shuying Li
- Department of Biomedical Engineering,
Washington University in St. Louis, 1 Brookings Dr, St. Louis 63130,
USA
| | - Kexin Huang
- Department of Biomedical Engineering,
Washington University in St. Louis, 1 Brookings Dr, St. Louis 63130,
USA
| | - Menghao Zhang
- Department of Electrical and Systems
Engineering, Washington University in St. Louis, 1 Brookings Dr, St.
Louis 63130, USA
| | - K. M. Shihab Uddin
- Department of Biomedical 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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24
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Carbone NA, Vera DA, Iriarte DI, Pomarico JA, Macdonald R, Grosenick D. Camera-based CW Diffuse Optical Tomography for obtaining 3D absorption maps by means of digital tomosynthesis. Biomed Phys Eng Express 2020; 6. [PMID: 35039466 DOI: 10.1088/2057-1976/abc633] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Accepted: 10/30/2020] [Indexed: 11/11/2022]
Abstract
We present a novel method for obtaining a 3D absorption map of a tissue-like turbid slab in the near-infrared spectral range by tomosynthesis. Transmittance data are obtained for a large number of oblique projection directions by scanning a cw laser source across the surface of the slab and by using a CCD camera for spatially resolved light detection. A perturbation model of light transport is used to convert the intensity maps for the different projections into absorption maps. By applying the tomosynthesis approach to these new maps, 3D absorption information on embedded inclusions has been obtained for the first time. The number and the positions of the lateral offset detectors have been optimized by employing a structural similarity index for comparison of the reconstructed with the true absorption data. We present 3D reconstruction of absorption maps using both Monte Carlo simulations and experiments on phantoms with breast-like optical properties. A comparison with conventional 3D reconstruction by a finite element approach shows the superior location performance of tomosynthesis.
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Affiliation(s)
- N A Carbone
- CIFICEN (UNCPBA-CONICET-CICPBA), Pinto 399, B7000GHG Tandil, Argentina
| | - D A Vera
- CIFICEN (UNCPBA-CONICET-CICPBA), Pinto 399, B7000GHG Tandil, Argentina
| | - D I Iriarte
- CIFICEN (UNCPBA-CONICET-CICPBA), Pinto 399, B7000GHG Tandil, Argentina
| | - J A Pomarico
- CIFICEN (UNCPBA-CONICET-CICPBA), Pinto 399, B7000GHG Tandil, Argentina
| | - R Macdonald
- Physikalisch-Technische Bundesanstalt (PTB), Abbestraße 2-12, 10587 Berlin, Germany
| | - D Grosenick
- Physikalisch-Technische Bundesanstalt (PTB), Abbestraße 2-12, 10587 Berlin, Germany
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25
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Recent Developments in Instrumentation of Functional Near-Infrared Spectroscopy Systems. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10186522] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
In the last three decades, the development and steady improvement of various optical technologies at the near-infrared region of the electromagnetic spectrum has inspired a large number of scientists around the world to design and develop functional near-infrared spectroscopy (fNIRS) systems for various medical applications. This has been driven further by the availability of new sources and detectors that support very compact and wearable system designs. In this article, we review fNIRS systems from the instrumentation point of view, discussing the associated challenges and state-of-the-art approaches. In the beginning, the fundamentals of fNIRS systems as well as light-tissue interaction at NIR are briefly introduced. After that, we present the basics of NIR systems instrumentation. Next, the recent development of continuous-wave, frequency-domain, and time-domain fNIRS systems are discussed. Finally, we provide a summary of these three modalities and an outlook into the future of fNIRS technology.
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26
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Algarawi M, Erkol H, Luk A, Ha S, Ünlü MB, Gulsen G, Nouizi F. Resolving tissue chromophore concentration at MRI resolution using multi-wavelength photo-magnetic imaging. BIOMEDICAL OPTICS EXPRESS 2020; 11:4244-4254. [PMID: 32923039 PMCID: PMC7449711 DOI: 10.1364/boe.397538] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Revised: 06/24/2020] [Accepted: 06/30/2020] [Indexed: 06/11/2023]
Abstract
Photo-magnetic imaging (PMI) is an emerging optical imaging modality that showed great performance on providing absorption maps with high resolution and quantitative accuracy. As a multi-modality technology, PMI warms up the imaged object using a near infrared laser while temperature variation is measured using magnetic resonance imaging. By probing tissue at multiple wavelengths, concentration of the main tissue chromophores such as oxy- and deoxy-hemoglobin, lipid, and water are obtained then used to derive functional parameters such as total hemoglobin concentration and relative oxygen saturation. In this paper, we present a multi-wavelength PMI system that was custom-built to host five different laser wavelengths. After recovering the high-resolution absorption maps, a least-squared minimization process was used to resolve the different chromophore concentration. The performance of the system was experimentally tested on a phantom with two different dyes. Their concentrations were successfully assessed with high spatial resolution and average accuracy of nearly 80%.
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Affiliation(s)
- Maha Algarawi
- Center for Functional Onco-Imaging, University of California Irvine, CA 92697, USA
- Department of Physics and Astronomy, University of California Irvine, CA 92697, USA
| | - Hakan Erkol
- Department of Physics, Bogazici University, Istanbul, Turkey
| | - Alex Luk
- Center for Functional Onco-Imaging, University of California Irvine, CA 92697, USA
| | | | - Mehmet B. Ünlü
- Department of Physics, Bogazici University, Istanbul, Turkey
| | - Gultekin Gulsen
- Center for Functional Onco-Imaging, University of California Irvine, CA 92697, USA
- Department of Physics and Astronomy, University of California Irvine, CA 92697, USA
- Department of Radiological Sciences, University of California Irvine, CA 92697, USA
| | - Farouk Nouizi
- Center for Functional Onco-Imaging, University of California Irvine, CA 92697, USA
- Department of Radiological Sciences, University of California Irvine, CA 92697, USA
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27
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Almulla L, Al-Naib I, Althobaiti M. Hemodynamic responses during standing and sitting activities: a study toward fNIRS-BCI. Biomed Phys Eng Express 2020; 6:055005. [PMID: 33444236 DOI: 10.1088/2057-1976/aba102] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
In this paper, we utilized functional near-infrared spectroscopy (fNIRS) technology to examine the hemodynamic responses in the motor cortex for two conditions, namely standing and sitting tasks. Nine subjects performed five trials of standing and sitting (SAS) tasks with both real movements and imagery thinking of SAS. A group level of statistical parametric mapping (SPM) analysis during these tasks showed bilateral activation of oxy-hemoglobin for both real movements and imagery experiments. Interestingly, the SPM analysis clearly revealed that the sitting tasks induced a higher oxy-hemoglobin level activation compared to the standing task. Remarkably, this finding is persistent across the 22 measured channels at the individual and group levels for both experiments. Furthermore, six features were extracted from pre-processed HbO signals and the performance of four different classifiers was examined in order to test the viability of using SAS tasks in future fNIRS-brain-computer interface (fNIRS-BCI) systems. In particular, two features-combination tests revealed that the signal slope with signal variance represents one of the three best two-combined features for its consistency in providing high accuracy results for both real and imagery experiments. This study shows the potential of implementing such tasks into the fNIRS-BCI system. In the future, the results of this work could pave the way towards the application of fNIRS-BCI in lower limb rehabilitation.
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Affiliation(s)
- Latifah Almulla
- Biomedical Engineering Department, College of Engineering, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
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28
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A review of optical breast imaging: Multi-modality systems for breast cancer diagnosis. Eur J Radiol 2020; 129:109067. [PMID: 32497943 DOI: 10.1016/j.ejrad.2020.109067] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Revised: 05/04/2020] [Accepted: 05/09/2020] [Indexed: 11/24/2022]
Abstract
This review of optical breast imaging describes basic physical and system principles and summarizes technological evolution with a focus on multi-modality platforms and recent clinical trial results. Ultrasound-guided diffuse optical tomography and co-registered ultrasound and photoacoustic imaging systems are emphasized as models of state of the art optical technology that are most conducive to clinical translation.
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Zhang M, Uddin KMS, Li S, Zhu Q. Target depth-regularized reconstruction in diffuse optical tomography using ultrasound segmentation as prior information. BIOMEDICAL OPTICS EXPRESS 2020; 11:3331-3345. [PMID: 32637258 PMCID: PMC7316021 DOI: 10.1364/boe.388816] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Revised: 05/03/2020] [Accepted: 05/04/2020] [Indexed: 05/13/2023]
Abstract
Ultrasound (US)-guided diffuse optical tomography (DOT) is a promising non-invasive functional imaging technique for diagnosing breast cancer and monitoring breast cancer treatment response. However, because larger lesions are highly absorbing, reconstructions of these lesions using reflection geometry may exhibit light shadowing, which leads to inaccurate quantification of their deeper portions. Here we propose a depth-regularized reconstruction algorithm combined with a semi-automated interactive neural network (CNN) for depth-dependent reconstruction of absorption distribution. CNN segments co-registered US to extract both spatial and depth priors, and the depth-regularized algorithm incorporates these parameters into the reconstruction. Through simulation and phantom data, the proposed algorithm is shown to significantly improve the depth distribution of reconstructed absorption maps of large targets. Evaluated with 26 patients with larger breast lesions, the algorithm shows 2.4 to 3 times improvement in the top-to-bottom reconstructed homogeneity of the absorption maps for these lesions.
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Affiliation(s)
- Menghao Zhang
- Electrical and System Engineering
Department, Washington University in St. Louis, 1 Brooking Dr, St.
Louis, MO 63130, USA
| | - K. M. Shihab Uddin
- Biomedical Engineering Department,
Washington University in St. Louis, 1 Brooking Dr, St. Louis, MO
63130, USA
| | - Shuying Li
- Biomedical Engineering Department,
Washington University in St. Louis, 1 Brooking Dr, St. Louis, MO
63130, USA
| | - Quing Zhu
- Electrical and System Engineering
Department, Washington University in St. Louis, 1 Brooking Dr, St.
Louis, MO 63130, USA
- Biomedical Engineering Department,
Washington University in St. Louis, 1 Brooking Dr, St. Louis, MO
63130, USA
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30
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Uddin KMS, Zhang M, Anastasio M, Zhu Q. Optimal breast cancer diagnostic strategy using combined ultrasound and diffuse optical tomography. BIOMEDICAL OPTICS EXPRESS 2020; 11:2722-2737. [PMID: 32499955 PMCID: PMC7249842 DOI: 10.1364/boe.389275] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Revised: 03/19/2020] [Accepted: 03/31/2020] [Indexed: 05/02/2023]
Abstract
Ultrasound (US)-guided near-infrared diffuse optical tomography (DOT) has demonstrated great potential as an adjunct breast cancer diagnosis tool to US imaging alone, especially in reducing unnecessary benign biopsies. However, DOT data processing and image reconstruction speeds remain slow compared to the real-time speed of US. Real-time or near real-time diagnosis with DOT is an important step toward the clinical translation of US-guided DOT. Here, to address this important need, we present a two-stage diagnostic strategy that is both computationally efficient and accurate. In the first stage, benign lesions are identified in near real-time by use of a random forest classifier acting on the DOT measurements and the radiologists' US diagnostic scores. Any lesions that cannot be reliably classified by the random forest classifier will be passed on to the second stage which begins with image reconstruction. Functional information from the reconstructed hemoglobin concentrations is employed by a Support Vector Machine (SVM) classifier for diagnosis at the end of the second stage. This two-step classification approach which combines both perturbation data and functional features, results in improved classification, as denoted by the receiver operating characteristic (ROC) curve. Using this two-step approach, the area under the ROC curve (AUC) is 0.937 ± 0.009, with a sensitivity of 91.4% and specificity of 85.7%. In comparison, using functional features and US score yields an AUC of 0.892 ± 0.027, with a sensitivity of 90.2% and specificity of 74.5%. Most notably, the specificity is increased by more than 10% due to the implementation of the random forest classifier.
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Affiliation(s)
- K. M. Shihab Uddin
- Biomedical Engineering Department, Washington University in St. Louis, 1 Brooking Dr, St. Louis, MO 63130, USA
| | - Menghao Zhang
- Electrical and System Engineering Department, Washington University in St. Louis, 1 Brooking Dr, St. Louis, MO 63130, USA
| | - Mark Anastasio
- Biomedical Engineering Department, Washington University in St. Louis, 1 Brooking Dr, St. Louis, MO 63130, USA
- Department of Bioengineering, University of Illinois at Urbana-Champaign, 1406 W Green St, Urbana, IL 61801, USA
| | - Quing Zhu
- Biomedical Engineering Department, Washington University in St. Louis, 1 Brooking Dr, St. Louis, MO 63130, USA
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31
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Time-Gated Single-Photon Detection in Time-Domain Diffuse Optics: A Review. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10031101] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
This work reviews physical concepts, technologies and applications of time-domain diffuse optics based on time-gated single-photon detection. This particular photon detection strategy is of the utmost importance in the diffuse optics field as it unleashes the full power of the time-domain approach by maximizing performances in terms of contrast produced by a localized perturbation inside the scattering medium, signal-to-noise ratio, measurement time and dynamic range, penetration depth and spatial resolution. The review covers 15 years of theoretical studies, technological progresses, proof of concepts and design of laboratory systems based on time-gated single-photon detection with also few hints on other fields where the time-gated detection strategy produced and will produce further impact.
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Abstract
Optical imaging offers a high potential for noninvasive detection and therapy of cancer in humans. Recent advances in instrumentation for diffuse optical imaging have led to new capabilities for the detection of cancer in highly scattering tissue such as the female breast. In particular, fluorescence imaging was made applicable as a sensitive technique to image molecular probes in vivo. We review recent developments in the detection of breast cancer and fluorescence-guided surgery of the breast by contrast agents available for application on humans. Detection of cancer has been investigated with the unspecific contrast agents "indocyanine green" and "omocianine" so far. Hereby, indocyanine green was found to offer high potential for the differentiation of malignant and benign lesions by exploiting vessel permeability for macromolecules as a cancer-specific feature. Tumor-specific molecular targeting and activatable probes have been investigated in clinical trials for fluorescence-guided tumor margin detection. In this application, high spatial resolution can be achieved, since tumor regions are visualized mainly at the tissue surface. As another example of superficial tumor tissue, imaging of lesions in the gastrointestinal tract is discussed. Promising results have been obtained on high-risk patients with Barrett´s esophagus and with ulcerative colitis by administering 5-aminolevulinic acid which induces accumulation of protoporphyrin IX serving as a tumor-specific fluorescent marker. Time-gated fluorescence imaging and spectroscopy are effective ways to suppress underlying background from tissue autofluorescence. Furthermore, recently developed tumor-specific molecular probes have been demonstrated to be superior to white-light endoscopy offering new ways for early detection of malignancies in the gastrointestinal tract.
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33
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Broadband Time Domain Diffuse Optical Reflectance Spectroscopy: A Review of Systems, Methods, and Applications. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9245465] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
This review presents recent developments and a wide overview of broadband time domain diffuse optical spectroscopy (TD-DOS). Various topics including physics of photon migration, advanced instrumentation, methods of analysis, applications covering multiple domains (tissue chromophore, in vivo studies, food, wood, pharmaceutical industry) are elaborated. The key role of standardization and recent studies in that direction are discussed. Towards the end, a brief outlook is presented on the current status and future trends in broadband TD-DOS.
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34
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Di Sieno L, Contini D, Lo Presti G, Cortese L, Mateo T, Rosinski B, Venturini E, Panizza P, Mora M, Aranda G, Squarcia M, Farina A, Durduran T, Taroni P, Pifferi A, Mora AD. Systematic study of the effect of ultrasound gel on the performances of time-domain diffuse optics and diffuse correlation spectroscopy. BIOMEDICAL OPTICS EXPRESS 2019; 10:3899-3915. [PMID: 31452983 PMCID: PMC6701515 DOI: 10.1364/boe.10.003899] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Revised: 07/01/2019] [Accepted: 07/01/2019] [Indexed: 05/06/2023]
Abstract
Recently, multimodal imaging has gained an increasing interest in medical applications thanks to the inherent combination of strengths of the different techniques. For example, diffuse optics is used to probe both the composition and the microstructure of highly diffusive media down to a depth of few centimeters, but its spatial resolution is intrinsically low. On the other hand, ultrasound imaging exhibits the higher spatial resolution of morphological imaging, but without providing solid constitutional information. Thus, the combination of diffuse optical imaging and ultrasound may improve the effectiveness of medical examinations, e.g. for screening or diagnosis of tumors. However, the presence of an ultrasound coupling gel between probe and tissue can impair diffuse optical measurements like diffuse optical spectroscopy and diffuse correlation spectroscopy, since it may provide a direct path for photons between source and detector. A systematic study on the effect of different ultrasound coupling fluids was performed on tissue-mimicking phantoms, confirming that a water-clear gel can produce detrimental effects on optical measurements when recovering absorption/reduced scattering coefficients from time-domain spectroscopy acquisitions as well as particle Brownian diffusion coefficient from diffuse correlation spectroscopy ones. On the other hand, we show the suitability for optical measurements of other types of diffusive fluids, also compatible with ultrasound imaging.
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Affiliation(s)
- Laura Di Sieno
- Politecnico di Milano - Dipartimento di Fisica, Milano, Italy
| | - Davide Contini
- Politecnico di Milano - Dipartimento di Fisica, Milano, Italy
| | - Giuseppe Lo Presti
- ICFO - Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology, Castelldefels (Barcelona), Spain
| | - Lorenzo Cortese
- ICFO - Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology, Castelldefels (Barcelona), Spain
| | | | | | - Elena Venturini
- Scientific Institute (IRCCS) Ospedale San Raffaele - Breast Imaging Unit, Milano, Italy
| | - Pietro Panizza
- Scientific Institute (IRCCS) Ospedale San Raffaele - Breast Imaging Unit, Milano, Italy
| | - Mireia Mora
- IDIBAPS, Fundació Clínic per la Recerca Biomèdica, Barcelona, Spain
| | - Gloria Aranda
- IDIBAPS, Fundació Clínic per la Recerca Biomèdica, Barcelona, Spain
| | - Mattia Squarcia
- IDIBAPS, Fundació Clínic per la Recerca Biomèdica, Barcelona, Spain
| | - Andrea Farina
- Consiglio Nazionale delle Ricerche - Istituto di Fotonica e Nanotecnologie, Milano, Italy
| | - Turgut Durduran
- ICFO - Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology, Castelldefels (Barcelona), Spain
- Instituciò Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| | - Paola Taroni
- Politecnico di Milano - Dipartimento di Fisica, Milano, Italy
- Consiglio Nazionale delle Ricerche - Istituto di Fotonica e Nanotecnologie, Milano, Italy
| | - Antonio Pifferi
- Politecnico di Milano - Dipartimento di Fisica, Milano, Italy
- Consiglio Nazionale delle Ricerche - Istituto di Fotonica e Nanotecnologie, Milano, Italy
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Uddin KMS, Zhu Q. Reducing image artifact in diffuse optical tomography by iterative perturbation correction based on multiwavelength measurements. JOURNAL OF BIOMEDICAL OPTICS 2019; 24:1-8. [PMID: 31119903 PMCID: PMC6529735 DOI: 10.1117/1.jbo.24.5.056005] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Accepted: 04/19/2019] [Indexed: 05/18/2023]
Abstract
Ultrasound (US) guided diffuse optical tomography has demonstrated great potential for breast cancer diagnosis, treatment monitoring, and chemotherapy response prediction. Optical measurements of four different wavelengths are used to reconstruct unknown optical absorption maps, which are then used to calculate the hemoglobin concentration distribution of the US visible lesion. Reconstructed absorption maps are prone to image artifacts from outliers in measurement data from tissue heterogeneity, bad coupling between tissue and light guides, and motion by patient or operator. We propose an automated iterative perturbation correction algorithm to reduce image artifacts based on the structural similarity index (SSIM) of absorption maps of four optical wavelengths. The initial image is estimated from the truncated pseudoinverse solution. The SSIM was calculated for each wavelength to assess its similarity with other wavelengths. An absorption map is repeatedly reconstructed and projected back into measurement space to quantify projection error. Outlier measurements with highest projection errors are iteratively removed until all wavelength images are structurally similar with SSIM values greater than a threshold. Clinical data demonstrate statistically significant improvement in image artifact reduction.
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Affiliation(s)
- K. M. Shihab Uddin
- Washington University in St Louis, Biomedical Engineering Department, St. Louis, Missouri, United States
| | - Quing Zhu
- Washington University in St Louis, Biomedical Engineering Department, St. Louis, Missouri, United States
- Address all correspondence to Quing Zhu, E-mail:
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Wang T, Shuai JJ, Li X, Wen Z. Impact of full field digital mammography diagnosis for female patients with breast cancer. Medicine (Baltimore) 2019; 98:e15175. [PMID: 31008938 PMCID: PMC6494235 DOI: 10.1097/md.0000000000015175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Previous clinical studies have reported that full field digital mammography (FFDM) can be used for diagnosis on breast cancer (BC) with promising outcome results. However, no study systematically investigates its diagnostic impact on female patients with BC. Thus, this systematic review will assess the accurate of FFDM diagnosis on BC. METHODS In this study, we will perform a comprehensive search strategy in the databases as follows: Cochrane Library, EMBASE, MEDILINE, PSYCINFO, Web of Science, Cumulative Index to Nursing and Allied Health Literature, Allied and Complementary Medicine Database, Chinese Biomedical Literature Database, China National Knowledge Infrastructure, VIP Information, and Wanfang Data from inception to February 28, 2019. All case-controlled studies exploring the impacts of FFDM diagnosis for patients BC will be fully considered for inclusion in this study. Two authors will independently scan the title and abstracts for relevance, and assess full texts for inclusion. They will also independently extract data and will assess methodological qualify for each included study by using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. RevMan V.5.3 software (London, UK) and Stata V.12.0 software (Texas, USA) will be used to pool the data and to conduct the meta-analysis. RESULTS The sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio of FFDM will be used to determine the diagnostic accuracy of FFDM for the diagnosis of patients with BC. CONCLUSION Its findings will provide latest evidence for the diagnostic accuracy of FFDM in female patients with BC. SYSTEMATIC REVIEW REGISTRATION PROSPERO CRD42019125338.
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Affiliation(s)
- Tuan Wang
- Department of Radiology, Affiliated Tumor Hospital of Xinjiang Medical University
| | - Jian-jun Shuai
- Department of Imaging Center, Traditional Chinese Medicine Hospital of Xinjiang Uyghur Autonomous Region
| | - Xing Li
- Department of Nuclear Magnetic
| | - Zhi Wen
- Department of Computed Tomography, Affiliated Tumor Hospital of Xinjiang Medical University, Urumqi, China
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37
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Apiou-Sbirlea G, Choe R, Kleemann M, Tromberg BJ. Special Section Guest Editorial: Translational Biophotonics. JOURNAL OF BIOMEDICAL OPTICS 2019; 24:1-2. [PMID: 30770679 PMCID: PMC6988178 DOI: 10.1117/1.jbo.24.2.021200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
This guest editorial introduces the special section on Translational Biophotonics.
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Affiliation(s)
- Gabriela Apiou-Sbirlea
- Massachusetts General Hospital Research Institute, Wellman Center for Photomedicine and Harvard Medi
| | - Regine Choe
- University of Rochester, Department of Biomedical Engineering, 204 Robert B. Goergen Hall, Rochester
| | - Markus Kleemann
- University Vascular Center Lübeck, University of Lübeck, Department of Surgery, University Medical C
| | - Bruce J Tromberg
- Beckman Laser Institute and Medical Clinic, 1002 Health Sciences Road East, University of California
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