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Schreiner OD, Socotar D, Ciobanu RC, Schreiner TG, Tamba BI. Statistical Analysis of Gastric Cancer Cells Response to Broadband Terahertz Radiation with and without Contrast Nanoparticles. Cancers (Basel) 2024; 16:2454. [PMID: 39001516 PMCID: PMC11240478 DOI: 10.3390/cancers16132454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2024] [Revised: 06/26/2024] [Accepted: 07/02/2024] [Indexed: 07/16/2024] Open
Abstract
The paper describes the statistical analysis of the response of gastric cancer cells and normal cells to broadband terahertz radiation up to 4 THz, both with and without the use of nanostructured contrast agents. The THz spectroscopy analysis was comparatively performed under the ATR procedure and transmission measurement procedure. The statistical analysis was conducted towards multiple pairwise comparisons, including a support medium (without cells) versus a support medium with nanoparticles, normal cells versus normal cells with nanoparticles, and, respectively, tumor cells versus tumor cells with nanoparticles. When generally comparing the ATR procedure and transmission measurement procedure for a broader frequency domain, the differentiation between normal and tumor cells in the presence of contrast agents is superior when using the ATR procedure. THz contrast enhancement by using contrast agents derived from MRI-related contrast agents leads to only limited benefits and only for narrow THz frequency ranges, a disadvantage for THz medical imaging.
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Affiliation(s)
- Oliver Daniel Schreiner
- Department of Electrical Measurements and Materials, Gheorghe Asachi Technical University, 700050 Iasi, Romania; (O.D.S.); (D.S.)
| | - Diana Socotar
- Department of Electrical Measurements and Materials, Gheorghe Asachi Technical University, 700050 Iasi, Romania; (O.D.S.); (D.S.)
| | - Romeo Cristian Ciobanu
- Department of Electrical Measurements and Materials, Gheorghe Asachi Technical University, 700050 Iasi, Romania; (O.D.S.); (D.S.)
| | - Thomas Gabriel Schreiner
- CEMEX-Center for Experimental Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700259 Iasi, Romania (B.I.T.)
| | - Bogdan Ionel Tamba
- CEMEX-Center for Experimental Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700259 Iasi, Romania (B.I.T.)
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2
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Cong M, Li W, Liu Y, Bi J, Wang X, Yang X, Zhang Z, Zhang X, Zhao YN, Zhao R, Qiu J. Biomedical application of terahertz imaging technology: a narrative review. Quant Imaging Med Surg 2023; 13:8768-8786. [PMID: 38106329 PMCID: PMC10722018 DOI: 10.21037/qims-23-526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 08/31/2023] [Indexed: 12/19/2023]
Abstract
Background and Objective Terahertz (THz) imaging has wide applications in biomedical research due to its properties, such as non-ionizing, non-invasive and distinctive spectral fingerprints. Over the past 6 years, the application of THz imaging in tumor tissue has made encouraging progress. However, due to the strong absorption of THz by water, the large size, high cost, and low sensitivity of THz devices, it is still difficult to be widely used in clinical practice. This paper provides ideas for researchers and promotes the development of THz imaging in clinical research. Methods The literature search was conducted in the Web of Science and PubMed databases using the keywords "Terahertz imaging", "Breast", "Brain", "Skin" and "Cancer". A total of 94 English language articles from 1 January, 2017 to 30 December, 2022 were reviewed. Key Content and Findings In this review, we briefly introduced the recent advances in THz near-field imaging, single-pixel imaging and real-time imaging, the applications of THz imaging for detecting breast, brain and skin tissues in the last 6 years were reviewed, and the advantages and existing challenges were identified. It is necessary to combine machine learning and metamaterials to develop real-time THz devices with small size, low cost and high sensitivity that can be widely used in clinical practice. More powerful THz detectors can be developed by combining graphene, designing structures and other methods to improve the sensitivity of the devices and obtain more accurate information. Establishing a THz database is one of the important methods to improve the repeatability and accuracy of imaging results. Conclusions THz technology is an effective method for tumor imaging. We believe that with the joint efforts of researchers and clinicians, accurate, real-time, and safe THz imaging will be widely applied in clinical practice in the future.
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Affiliation(s)
- Mengyang Cong
- College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai’an, China
| | - Wen Li
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai’an, China
| | - Yang Liu
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai’an, China
| | - Jing Bi
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai’an, China
| | - Xiaokun Wang
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai’an, China
| | - Xueqiao Yang
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai’an, China
| | - Zihan Zhang
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai’an, China
| | - Xiaoxin Zhang
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai’an, China
| | - Ya-Nan Zhao
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai’an, China
| | - Rui Zhao
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai’an, China
- Department of Nuclear Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
- Science and Technology Innovation Center, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, China
| | - Jianfeng Qiu
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai’an, China
- Center for Medical Engineer Technology Research, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai’an, China
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3
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Zhan X, Liu Y, Chen Z, Luo J, Yang S, Yang X. Revolutionary approaches for cancer diagnosis by terahertz-based spectroscopy and imaging. Talanta 2023; 259:124483. [PMID: 37019007 DOI: 10.1016/j.talanta.2023.124483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 02/23/2023] [Accepted: 03/22/2023] [Indexed: 03/31/2023]
Abstract
Most tumors are easily missed and misdiagnosed due to the lack of specific clinical signs and symptoms in the early stage. Thus, an accurate, rapid and reliable early tumor detection method is highly desirable. The application of terahertz (THz) spectroscopy and imaging in biomedicine has made remarkable progress in the past two decades, which addresses the shortcomings of existing technologies and provides an alternative for early tumor diagnosis. Although issues such as size mismatch and strong absorption of THz waves by water have set hurdles for cancer diagnosis by THz technology, innovative materials and biosensors in recent years have led to possibilities for new THz biosensing and imaging methods. In this article, we reviewed the issues that need to be solved before THz technology is used for tumor-related biological sample detection and clinical auxiliary diagnosis. We focused on the recent research progress of THz technology, with an emphasis on biosensing and imaging. Finally, the application of THz spectroscopy and imaging for tumor diagnosis in clinical practice and the main challenges in this process were also mentioned. Collectively, THz-based spectroscopy and imaging reviewed here is envisioned as a cutting-edge approach for cancer diagnosis.
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Affiliation(s)
- Xinyu Zhan
- Department of Laboratory Medicine, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, 400038, China
| | - Yu Liu
- Department of Gastroenterology, Chongqing Hospital of Traditional Chinese Medicine, Chongqing, 400037, China
| | - Zhiguo Chen
- Gastroenterology Department, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, 400038, China
| | - Jie Luo
- Department of Laboratory Medicine, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, 400038, China
| | - Sha Yang
- Department of Laboratory Medicine, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, 400038, China.
| | - Xiang Yang
- Department of Laboratory Medicine, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, 400038, China.
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4
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Choi WJ, Lee SH, Park BC, Kotov NA. Terahertz Circular Dichroism Spectroscopy of Molecular Assemblies and Nanostructures. J Am Chem Soc 2022; 144:22789-22804. [DOI: 10.1021/jacs.2c04817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Won Jin Choi
- Biointerfaces Institute, University of Michigan, Ann Arbor, Michigan 48109, United States
- Department of Chemical Engineering, University of Michigan, Ann Arbor, Michigan 48109, United States
- Physical and Life Sciences, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Sang Hyun Lee
- Biointerfaces Institute, University of Michigan, Ann Arbor, Michigan 48109, United States
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Bum Chul Park
- Biointerfaces Institute, University of Michigan, Ann Arbor, Michigan 48109, United States
- Department of Chemical Engineering, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Nicholas A. Kotov
- Biointerfaces Institute, University of Michigan, Ann Arbor, Michigan 48109, United States
- Department of Chemical Engineering, University of Michigan, Ann Arbor, Michigan 48109, United States
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan 48109, United States
- Program in Macromolecular Science and Engineering, University of Michigan, Ann Arbor, Michigan 48109, United States
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5
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Identifying plastics with photoluminescence spectroscopy and machine learning. Sci Rep 2022; 12:18840. [PMID: 36336705 PMCID: PMC9637756 DOI: 10.1038/s41598-022-23414-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 10/31/2022] [Indexed: 11/07/2022] Open
Abstract
A quantitative understanding of the worldwide plastics distribution is required not only to assess the extent and possible impact of plastic litter on the environment but also to identify possible counter measures. A systematic collection of data characterizing amount and composition of plastics has to be based on two crucial components: (i) An experimental approach that is simple enough to be accessible worldwide and sensible enough to capture the diversity of plastics; (ii) An analysis pipeline that is able to extract the relevant parameters from the vast amount of experimental data. In this study, we demonstrate that such an approach could be realized by a combination of photoluminescence spectroscopy and a machine learning-based theoretical analysis. We show that appropriate combinations of classifiers with dimensional reduction algorithms are able to identify specific material properties from the spectroscopic data. The best combination is based on an unsupervised learning technique making our approach robust to alternations of the input data.
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6
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Khani ME, Osman OB, Harris ZB, Chen A, Zhou JW, Singer AJ, Arbab MH. Accurate and early prediction of the wound healing outcome of burn injuries using the wavelet Shannon entropy of terahertz time-domain waveforms. JOURNAL OF BIOMEDICAL OPTICS 2022; 27:JBO-220119GR. [PMID: 36348509 PMCID: PMC9641274 DOI: 10.1117/1.jbo.27.11.116001] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 10/14/2022] [Indexed: 05/06/2023]
Abstract
Significance Severe burn injuries cause significant hypermetabolic alterations that are highly dynamic, hard to predict, and require acute and critical care. The clinical assessments of the severity of burn injuries are highly subjective and have consistently been reported to be inaccurate. Therefore, the utilization of other imaging modalities is crucial to reaching an objective and accurate burn assessment modality. Aim We describe a non-invasive technique using terahertz time-domain spectroscopy (THz-TDS) and the wavelet packet Shannon entropy to automatically estimate the burn depth and predict the wound healing outcome of thermal burn injuries. Approach We created 40 burn injuries of different severity grades in two porcine models using scald and contact methods of infliction. We used our THz portable handheld spectral reflection (PHASR) scanner to obtain the in vivo THz-TDS images. We used the energy to Shannon entropy ratio of the wavelet packet coefficients of the THz-TDS waveforms on day 0 to create supervised support vector machine (SVM) classification models. Histological assessments of the burn biopsies serve as the ground truth. Results We achieved an accuracy rate of 94.7% in predicting the wound healing outcome, as determined by histological measurement of the re-epithelialization rate on day 28 post-burn induction, using the THz-TDS measurements obtained on day 0. Furthermore, we report the accuracy rates of 89%, 87.1%, and 87.6% in automatic diagnosis of the superficial partial-thickness, deep partial-thickness, and full-thickness burns, respectively, using a multiclass SVM model. Conclusions The THz PHASR scanner promises a robust, high-speed, and accurate diagnostic modality to improve the clinical triage of burns and their management.
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Affiliation(s)
- Mahmoud E. Khani
- Stony Brook University, Department of Biomedical Engineering, Stony Brook, New York, United States
| | - Omar B. Osman
- Stony Brook University, Department of Biomedical Engineering, Stony Brook, New York, United States
| | - Zachery B. Harris
- Stony Brook University, Department of Biomedical Engineering, Stony Brook, New York, United States
| | - Andrew Chen
- Stony Brook University, Department of Biomedical Engineering, Stony Brook, New York, United States
| | - Juin W. Zhou
- Stony Brook University, Department of Biomedical Engineering, Stony Brook, New York, United States
| | - Adam J. Singer
- Renaissance School of Medicine at Stony Brook University, Department of Emergency Medicine, Stony Brook, New York, United States
| | - Mohammad Hassan Arbab
- Stony Brook University, Department of Biomedical Engineering, Stony Brook, New York, United States
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7
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Zhou Z, Jia S, Cao L. A General Neural Network Model for Complex Refractive Index Extraction of Low-Loss Materials in the Transmission-Mode THz-TDS. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22207877. [PMID: 36298228 PMCID: PMC9611207 DOI: 10.3390/s22207877] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 10/10/2022] [Accepted: 10/14/2022] [Indexed: 05/25/2023]
Abstract
The complex refractive index for low-loss materials is conventionally extracted by either approximate analytical formula or numerical iterative algorithm (such as Nelder-Mead and Newton-Raphson) based on the transmission-mode terahertz time domain spectroscopy (THz-TDS). A novel 4-layer neural network model is proposed to obtain optical parameters of low-loss materials with high accuracy in a wide range of parameters (frequency and thickness). Three materials (TPX, z-cut crystal quartz and 6H SiC) with different dispersions and thicknesses are used to validate the robustness of the general model. Without problems of proper initial values and non-convergence, the neural network method shows even smaller errors than the iterative algorithm. Once trained and tested, the proposed method owns both high accuracy and wide generality, which will find application in the multi-class object detection and high-precision characterization of THz materials.
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8
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Agbley BLY, Li J, Hossin MA, Nneji GU, Jackson J, Monday HN, James EC. Federated Learning-Based Detection of Invasive Carcinoma of No Special Type with Histopathological Images. Diagnostics (Basel) 2022; 12:diagnostics12071669. [PMID: 35885573 PMCID: PMC9323034 DOI: 10.3390/diagnostics12071669] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 07/03/2022] [Accepted: 07/05/2022] [Indexed: 11/16/2022] Open
Abstract
Invasive carcinoma of no special type (IC-NST) is known to be one of the most prevalent kinds of breast cancer, hence the growing research interest in studying automated systems that can detect the presence of breast tumors and appropriately classify them into subtypes. Machine learning (ML) and, more specifically, deep learning (DL) techniques have been used to approach this problem. However, such techniques usually require massive amounts of data to obtain competitive results. This requirement makes their application in specific areas such as health problematic as privacy concerns regarding the release of patients’ data publicly result in a limited number of publicly available datasets for the research community. This paper proposes an approach that leverages federated learning (FL) to securely train mathematical models over multiple clients with local IC-NST images partitioned from the breast histopathology image (BHI) dataset to obtain a global model. First, we used residual neural networks for automatic feature extraction. Then, we proposed a second network consisting of Gabor kernels to extract another set of features from the IC-NST dataset. After that, we performed a late fusion of the two sets of features and passed the output through a custom classifier. Experiments were conducted for the federated learning (FL) and centralized learning (CL) scenarios, and the results were compared. Competitive results were obtained, indicating the positive prospects of adopting FL for IC-NST detection. Additionally, fusing the Gabor features with the residual neural network features resulted in the best performance in terms of accuracy, F1 score, and area under the receiver operation curve (AUC-ROC). The models show good generalization by performing well on another domain dataset, the breast cancer histopathological (BreakHis) image dataset. Our method also outperformed other methods from the literature.
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Affiliation(s)
- Bless Lord Y. Agbley
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; (B.L.Y.A.); (H.N.M.)
| | - Jianping Li
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; (B.L.Y.A.); (H.N.M.)
- Correspondence:
| | - Md Altab Hossin
- School of Innovation and Entrepreneurship, Chengdu University, Chengdu 610106, China;
| | - Grace Ugochi Nneji
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; (G.U.N.); (J.J.); (E.C.J.)
| | - Jehoiada Jackson
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; (G.U.N.); (J.J.); (E.C.J.)
| | - Happy Nkanta Monday
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; (B.L.Y.A.); (H.N.M.)
| | - Edidiong Christopher James
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; (G.U.N.); (J.J.); (E.C.J.)
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9
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Liu H, Liu X, Zhang Z, Liang M, Zhang C. A novel strategy regarding geometric product for liquids discrimination based on THz reflection spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 274:121104. [PMID: 35276474 DOI: 10.1016/j.saa.2022.121104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 02/24/2022] [Accepted: 02/28/2022] [Indexed: 06/14/2023]
Abstract
A novel expression of geometric product that associated with the geometric relationship from geometric algebra constructed by the vectorized refractive index and absorption coefficient in THz region is proposed, which could provide a new insight into the THz properties of materials. From the novel expression, the candidate characteristic parameters are extracted for liquids discrimination and present the abundant second order correlation information of optical parameters with the consideration of dimension rising. Three groups of liquids, containing C-reactive protein calibrators and alpha fetoprotein calibrators, were selected as examples to validate the feasibility of the proposed strategy. Comparing with the traditional THz parameters, including refractive index, absorption coefficient, and complex permittivity, the novel approach exhibits notable superiority for differentiation with the evaluation of statistical differences and effect sizes. The proposed geometric product expression could have a large potential on promoting the substance identification in some applications of THz technology.
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Affiliation(s)
- Haishun Liu
- Key Laboratory of Terahertz Optoelectronics, Ministry of Education, Department of Physics, Capital Normal University, Beijing 100048, China; Department of Medical Laboratory, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China
| | - Xiangyi Liu
- Department of Medical Laboratory, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China.
| | - Zhenwei Zhang
- Key Laboratory of Terahertz Optoelectronics, Ministry of Education, Department of Physics, Capital Normal University, Beijing 100048, China.
| | - Meiyan Liang
- Department of Electronics and Information Engineering, Shanxi University, Taiyuan 030006, China
| | - Cunlin Zhang
- Key Laboratory of Terahertz Optoelectronics, Ministry of Education, Department of Physics, Capital Normal University, Beijing 100048, China
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Klokkou N, Gorecki J, Wilkinson JS, Apostolopoulos V. Artificial neural networks for material parameter extraction in terahertz time-domain spectroscopy. OPTICS EXPRESS 2022; 30:15583-15595. [PMID: 35473275 DOI: 10.1364/oe.454756] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Accepted: 02/22/2022] [Indexed: 06/14/2023]
Abstract
Terahertz time-domain spectroscopy (THz-TDS) is a proven technique whereby the complex refractive indices of materials can be obtained without requiring the use of the Kramers-Kronig relations, as phase and amplitude information can be extracted from the measurement. However, manual pre-processing of the data is still required and the material parameters require iterative fitting, resulting in complexity, loss of accuracy and inconsistencies between measurements. Alternatively approximations can be used to enable analytical extraction but with a considerable sacrifice of accuracy. We investigate the use of machine learning techniques for interpreting spectroscopic THz-TDS data by training with large data sets of simulated light-matter interactions, resulting in a computationally efficient artificial neural network for material parameter extraction. The trained model improves on the accuracy of analytical methods that need approximations while being easier to implement and faster to run than iterative root-finding methods. We envisage neural networks can alleviate many of the common hurdles involved in analyzing THz-TDS data such as phase unwrapping, time domain windowing, slow computation times, and extraction accuracy at the low frequency range.
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11
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Osman OB, Harris ZB, Khani ME, Zhou JW, Chen A, Singer AJ, Hassan Arbab M. Deep neural network classification of in vivo burn injuries with different etiologies using terahertz time-domain spectral imaging. BIOMEDICAL OPTICS EXPRESS 2022; 13:1855-1868. [PMID: 35519269 PMCID: PMC9045889 DOI: 10.1364/boe.452257] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 01/28/2022] [Accepted: 01/28/2022] [Indexed: 05/22/2023]
Abstract
Thermal injuries can occur due to direct exposure to hot objects or liquids, flames, electricity, solar energy and several other sources. If the resulting injury is a deep partial thickness burn, the accuracy of a physician's clinical assessment is as low as 50-76% in determining the healing outcome. In this study, we show that the Terahertz Portable Handheld Spectral Reflection (THz-PHASR) Scanner combined with a deep neural network classification algorithm can accurately differentiate between partial-, deep partial-, and full-thickness burns 1-hour post injury, regardless of the etiology, scanner geometry, or THz spectroscopy sampling method (ROC-AUC = 91%, 88%, and 86%, respectively). The neural network diagnostic method simplifies the classification process by directly using the pre-processed THz spectra and removing the need for any hyperspectral feature extraction. Our results show that deep learning methods based on THz time-domain spectroscopy (THz-TDS) measurements can be used to guide clinical treatment plans based on objective and accurate classification of burn injuries.
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Affiliation(s)
- Omar B. Osman
- State University of New York at Stony Brook, THz Biophotonics Laboratory, Department of Biomedical Engineering, 101 Nicolls Rd., Stony Brook, NY 11794, USA
| | - Zachery B. Harris
- State University of New York at Stony Brook, THz Biophotonics Laboratory, Department of Biomedical Engineering, 101 Nicolls Rd., Stony Brook, NY 11794, USA
| | - Mahmoud E. Khani
- State University of New York at Stony Brook, THz Biophotonics Laboratory, Department of Biomedical Engineering, 101 Nicolls Rd., Stony Brook, NY 11794, USA
| | - Juin W. Zhou
- State University of New York at Stony Brook, THz Biophotonics Laboratory, Department of Biomedical Engineering, 101 Nicolls Rd., Stony Brook, NY 11794, USA
| | - Andrew Chen
- State University of New York at Stony Brook, THz Biophotonics Laboratory, Department of Biomedical Engineering, 101 Nicolls Rd., Stony Brook, NY 11794, USA
| | - Adam J. Singer
- Renaissance School of Medicine at Stony Brook University, Department of Emergency Medicine, 101 Nicolls Rd., Stony Brook, NY 11794, USA
| | - M. Hassan Arbab
- State University of New York at Stony Brook, THz Biophotonics Laboratory, Department of Biomedical Engineering, 101 Nicolls Rd., Stony Brook, NY 11794, USA
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12
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Khani ME, Harris ZB, Osman OB, Zhou JW, Chen A, Singer AJ, Arbab MH. Supervised machine learning for automatic classification of in vivo scald and contact burn injuries using the terahertz Portable Handheld Spectral Reflection (PHASR) Scanner. Sci Rep 2022; 12:5096. [PMID: 35332207 PMCID: PMC8948290 DOI: 10.1038/s41598-022-08940-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 03/04/2022] [Indexed: 12/21/2022] Open
Abstract
We present an automatic classification strategy for early and accurate assessment of burn injuries using terahertz (THz) time-domain spectroscopic imaging. Burn injuries of different severity grades, representing superficial partial-thickness (SPT), deep partial-thickness (DPT), and full-thickness (FT) wounds, were created by a standardized porcine scald model. THz spectroscopic imaging was performed using our new fiber-coupled Portable HAndheld Spectral Reflection Scanner, incorporating a telecentric beam steering configuration and an f-[Formula: see text] scanning lens. ASynchronous Optical Sampling in a dual-fiber-laser THz spectrometer with 100 MHz repetition rate enabled high-speed spectroscopic measurements. Given twenty-four different samples composed of ten scald and ten contact burns and four healthy samples, supervised machine learning algorithms using THz-TDS spectra achieved areas under the receiver operating characteristic curves of 0.88, 0.93, and 0.93 when differentiating between SPT, DPT, and FT burns, respectively, as determined by independent histological assessments. These results show the potential utility of our new broadband THz PHASR Scanner for early and accurate triage of burn injuries.
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Affiliation(s)
- Mahmoud E Khani
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Zachery B Harris
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Omar B Osman
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Juin W Zhou
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Andrew Chen
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Adam J Singer
- Department of Emergency Medicine, Stony Brook University, Stony Brook, NY, 11794, USA
| | - M Hassan Arbab
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, 11794, USA.
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13
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Khani ME, Arbab MH. Translation-Invariant Zero-Phase Wavelet Methods for Feature Extraction in Terahertz Time-Domain Spectroscopy. SENSORS (BASEL, SWITZERLAND) 2022; 22:2305. [PMID: 35336476 PMCID: PMC8952727 DOI: 10.3390/s22062305] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 03/11/2022] [Accepted: 03/14/2022] [Indexed: 11/24/2022]
Abstract
Wavelet transform is an important tool in the computational signal processing of terahertz time-domain spectroscopy (THz-TDS) measurements. Despite its prevalence, the effects of using different forms of wavelet transforms in THz-TDS studies have not been investigated. In this paper, we explore the implications of using the maximal overlap discrete wavelet transform (MODWT) versus the well-known discrete wavelet transform (DWT). We demonstrate that the spectroscopic features extracted using DWT can vary over different overlapping frequency ranges. On the contrary, MODWT is translation-invariant and results in identical features, regardless of the spectral range used for its implementation.We also demonstrate that the details coefficients obtained by the multiresolution analysis (MRA) using MODWT are associated with zero-phase filters. In contrast, DWT details coefficients suffer from misalignments originated from the down- and upsampling operations in DWT pyramid algorithm. Such misalignments have adverse effects when it is critical to retain the exact location of the absorption lines. We study the differences of DWT and MODWT both analytically and experimentally, using reflection THz-TDS measurements of α-lactose monohydrate. This manuscript can guide the researchers to select the right wavelet analysis tool for their specific application of the THz spectroscopy.
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Affiliation(s)
| | - Mohammad Hassan Arbab
- Biomedical Engineering Department, Stony Brook University, Stony Brook, NY 11790, USA;
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14
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Wang L. Terahertz Imaging for Breast Cancer Detection. SENSORS (BASEL, SWITZERLAND) 2021; 21:6465. [PMID: 34640784 PMCID: PMC8512288 DOI: 10.3390/s21196465] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 09/19/2021] [Accepted: 09/26/2021] [Indexed: 12/02/2022]
Abstract
Terahertz (THz) imaging has the potential to detect breast tumors during breast-conserving surgery accurately. Over the past decade, many research groups have extensively studied THz imaging and spectroscopy techniques for identifying breast tumors. This manuscript presents the recent development of THz imaging techniques for breast cancer detection. The dielectric properties of breast tissues in the THz range, THz imaging and spectroscopy systems, THz radiation sources, and THz breast imaging studies are discussed. In addition, numerous chemometrics methods applied to improve THz image resolution and data collection processing are summarized. Finally, challenges and future research directions of THz breast imaging are presented.
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Affiliation(s)
- Lulu Wang
- Biomedical Device Innovation Center, Shenzhen Technology University, Shenzhen 518118, China;
- Institute of Biomedical Technologies, Auckland University of Technology, Auckland 1010, New Zealand
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15
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Park H, Son JH. Machine Learning Techniques for THz Imaging and Time-Domain Spectroscopy. SENSORS (BASEL, SWITZERLAND) 2021; 21:1186. [PMID: 33567605 PMCID: PMC7914669 DOI: 10.3390/s21041186] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 01/28/2021] [Accepted: 02/03/2021] [Indexed: 01/04/2023]
Abstract
Terahertz imaging and time-domain spectroscopy have been widely used to characterize the properties of test samples in various biomedical and engineering fields. Many of these tasks require the analysis of acquired terahertz signals to extract embedded information, which can be achieved using machine learning. Recently, machine learning techniques have developed rapidly, and many new learning models and learning algorithms have been investigated. Therefore, combined with state-of-the-art machine learning techniques, terahertz applications can be performed with high performance that cannot be achieved using modeling techniques that precede the machine learning era. In this review, we introduce the concept of machine learning and basic machine learning techniques and examine the methods for performance evaluation. We then summarize representative examples of terahertz imaging and time-domain spectroscopy that are conducted using machine learning.
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Affiliation(s)
- Hochong Park
- Department of Electronics Engineering, Kwangwoon University, Seoul 01897, Korea;
| | - Joo-Hiuk Son
- Department of Physics, University of Seoul, Seoul 02504, Korea
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Abstract
This review considers glioma molecular markers in brain tissues and body fluids, shows the pathways of their formation, and describes traditional methods of analysis. The most important optical properties of glioma markers in the terahertz (THz) frequency range are also presented. New metamaterial-based technologies for molecular marker detection at THz frequencies are discussed. A variety of machine learning methods, which allow the marker detection sensitivity and differentiation of healthy and tumor tissues to be improved with the aid of THz tools, are considered. The actual results on the application of THz techniques in the intraoperative diagnosis of brain gliomas are shown. THz technologies’ potential in molecular marker detection and defining the boundaries of the glioma’s tissue is discussed.
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Peng Y, Shi C, Wu X, Zhu Y, Zhuang S. Terahertz Imaging and Spectroscopy in Cancer Diagnostics: A Technical Review. BME FRONTIERS 2020; 2020:2547609. [PMID: 37849968 PMCID: PMC10521734 DOI: 10.34133/2020/2547609] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 08/31/2020] [Indexed: 10/19/2023] Open
Abstract
Terahertz (THz) waves are electromagnetic waves with frequency in the range from 0.1 to 10 THz. THz waves have great potential in the biomedical field, especially in cancer diagnosis, because they exhibit low ionization energy and can be used to discern most biomolecules based on their spectral fingerprints. In this paper, we review the recent progress in two applications of THz waves in cancer diagnosis: imaging and spectroscopy. THz imaging is expected to help researchers and doctors attain a direct intuitive understanding of a cancerous area. THz spectroscopy is an efficient tool for component analysis of tissue samples to identify cancer biomarkers. Additionally, the advantages and disadvantages of the developed technologies for cancer diagnosis are discussed. Furthermore, auxiliary techniques that have been used to enhance the spectral signal-to-noise ratio (SNR) are also reviewed.
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Affiliation(s)
- Yan Peng
- Terahertz Technology Innovation Research Institute, Shanghai Key Lab of Modern Optical System, Terahertz Science Cooperative Innovation Center, University of Shanghai for Science and Technology, Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, China
| | - Chenjun Shi
- Terahertz Technology Innovation Research Institute, Shanghai Key Lab of Modern Optical System, Terahertz Science Cooperative Innovation Center, University of Shanghai for Science and Technology, Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, China
| | - Xu Wu
- Terahertz Technology Innovation Research Institute, Shanghai Key Lab of Modern Optical System, Terahertz Science Cooperative Innovation Center, University of Shanghai for Science and Technology, Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, China
| | - Yiming Zhu
- Terahertz Technology Innovation Research Institute, Shanghai Key Lab of Modern Optical System, Terahertz Science Cooperative Innovation Center, University of Shanghai for Science and Technology, Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, China
| | - Songlin Zhuang
- Terahertz Technology Innovation Research Institute, Shanghai Key Lab of Modern Optical System, Terahertz Science Cooperative Innovation Center, University of Shanghai for Science and Technology, Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, China
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