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Guezzi N, Lee C, Le TD, Seong H, Choi KH, Min JJ, Yu J. Multistage adaptive noise reduction technique for optical resolution photoacoustic microscopy. JOURNAL OF BIOPHOTONICS 2022; 15:e202200164. [PMID: 36053943 DOI: 10.1002/jbio.202200164] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 07/22/2022] [Accepted: 07/25/2022] [Indexed: 06/15/2023]
Abstract
Photoacoustic microscopy has received great attention due to the benefits of the optical resolution contrast as well as its superior spatial resolution and relatively deep depth. Like other imaging modalities, photoacoustic images suffer from noise, and filtering techniques are required to remove them. To overcome the noise, we proposed a combination of filters, including an adaptive median filter, an effective filter for impulsive noise, and a nonlocal means filter, an effective filter for background noise, for noise removal and image quality enhancement. Our proposed method enhanced the signal-to-noise ratio by 16 dB in an in vivo study compared to the traditional image reconstruction approach and preserved the image detail with minimal blurring, which usually occurs when filtering. These experimental results verified that the proposed adaptive multistage denoising techniques could effectively improve image quality under noisy data acquisition conditions, providing a strong foundation for photoacoustic microscopy with limited laser power.
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Affiliation(s)
- Nizar Guezzi
- Department of Robotics and Mechatronics Engineering, DGIST, Daegu, South Korea
- DGIST Robotics Research Center, DGIST, Daegu, South Korea
| | - Changho Lee
- Department of Nuclear Medicine, Chonnam National University Medical School & Hwasun Hospital, Hwasun, Jeollanamado, South Korea
- Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju, South Korea
| | - Thanh Dat Le
- Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju, South Korea
| | - Hyojin Seong
- Department of Robotics and Mechatronics Engineering, DGIST, Daegu, South Korea
- DGIST Robotics Research Center, DGIST, Daegu, South Korea
| | - Kang-Ho Choi
- Department of Neurology, Chonnam National University Hospital, Gwangju, South Korea
| | - Jung-Joon Min
- Department of Nuclear Medicine, Chonnam National University Medical School & Hwasun Hospital, Hwasun, Jeollanamado, South Korea
| | - Jaesok Yu
- Department of Robotics and Mechatronics Engineering, DGIST, Daegu, South Korea
- DGIST Robotics Research Center, DGIST, Daegu, South Korea
- The Interdisciplinary Studies of Artificial Intelligence, DGIST, Daegu, South Korea
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Segmentation and Quantitative Analysis of Photoacoustic Imaging: A Review. PHOTONICS 2022. [DOI: 10.3390/photonics9030176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
Photoacoustic imaging is an emerging biomedical imaging technique that combines optical contrast and ultrasound resolution to create unprecedented light absorption contrast in deep tissue. Thanks to its fusional imaging advantages, photoacoustic imaging can provide multiple structural and functional insights into biological tissues such as blood vasculatures and tumors and monitor the kinetic movements of hemoglobin and lipids. To better visualize and analyze the regions of interest, segmentation and quantitative analyses were used to extract several biological factors, such as the intensity level changes, diameter, and tortuosity of the tissues. Over the past 10 years, classical segmentation methods and advances in deep learning approaches have been utilized in research investigations. In this review, we provide a comprehensive review of segmentation and quantitative methods that have been developed to process photoacoustic imaging in preclinical and clinical experiments. We focus on the parametric reliability of quantitative analysis for semantic and instance-level segmentation. We also introduce the similarities and alternatives of deep learning models in qualitative measurements using classical segmentation methods for photoacoustic imaging.
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Yan Y, John S, Shaik T, Patel B, Lam MT, Kabbani L, Mehrmohammadi M. Photoacoustic-guided endovenous laser ablation: Characterization and in vivo canine study. PHOTOACOUSTICS 2021; 24:100298. [PMID: 34504765 PMCID: PMC8416949 DOI: 10.1016/j.pacs.2021.100298] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 08/26/2021] [Accepted: 08/28/2021] [Indexed: 05/28/2023]
Abstract
Endovenous laser ablation (EVLA) is a minimally invasive surgical procedure, often guided by ultrasound (US) imaging, for treating venous insufficiencies. US imaging limitations in accurately visualizing the catheter and the lack of a temperature monitoring system can lead to sub-optimal outcomes. An integrated photoacoustic (PA)-guided EVLA system has been previously developed and reported to overcome the shortcomings of US-guided procedure. In this study, we further characterized the system and tested the in vivo utility. In addition, PA thermometry was further explored by compensating the variation of PA signal with temperature with respect to the temperature-dependent absorption of blood and water. In vivo imaging results indicated that the PA-guided EVLA system can provide high contrast and accurate images of the ablation catheter tip overlaid on US images of the background tissue. Additionally, absorption-compensated PA signal amplitudes over a relevant range of temperature were measured and demonstrated.
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Affiliation(s)
- Yan Yan
- Biomedical Engineering, Wayne State University, Detroit, MI, United States
| | - Samuel John
- Biomedical Engineering, Wayne State University, Detroit, MI, United States
| | - Tanyeem Shaik
- Biomedical Engineering, Wayne State University, Detroit, MI, United States
| | - Bijal Patel
- Biomedical Engineering, Wayne State University, Detroit, MI, United States
| | - Mai T. Lam
- Biomedical Engineering, Wayne State University, Detroit, MI, United States
| | - Loay Kabbani
- Vascular Surgery, Henry Ford Health System, MI, United States
| | - Mohammad Mehrmohammadi
- Biomedical Engineering, Wayne State University, Detroit, MI, United States
- Barbara Ann Karmanos Cancer Institute, MI, United States
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Jeong S, Yoo SW, Kim HJ, Park J, Kim JW, Lee C, Kim H. Recent Progress on Molecular Photoacoustic Imaging with Carbon-Based Nanocomposites. MATERIALS (BASEL, SWITZERLAND) 2021; 14:5643. [PMID: 34640053 PMCID: PMC8510032 DOI: 10.3390/ma14195643] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 09/25/2021] [Accepted: 09/27/2021] [Indexed: 12/20/2022]
Abstract
For biomedical imaging, the interest in noninvasive imaging methods is ever increasing. Among many modalities, photoacoustic imaging (PAI), which is a combination of optical and ultrasound imaging techniques, has received attention because of its unique advantages such as high spatial resolution, deep penetration, and safety. Incorporation of exogenous imaging agents further amplifies the effective value of PAI, since they can deliver other specified functions in addition to imaging. For these agents, carbon-based materials can show a large specific surface area and interesting optoelectronic properties, which increase their effectiveness and have proved their potential in providing a theragnostic platform (diagnosis + therapy) that is essential for clinical use. In this review, we introduce the current state of the PAI modality, address recent progress on PAI imaging that takes advantage of carbon-based agents, and offer a future perspective on advanced PAI systems using carbon-based agents.
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Affiliation(s)
- Songah Jeong
- School of Polymer Science and Engineering, Chonnam National University, 77 Yongbong-ro, Buk-gu, Gwangju 61186, Korea; (S.J.); (H.J.K.); (J.P.); (J.W.K.)
| | - Su Woong Yoo
- Department of Nuclear Medicine, Chonnam National University Hwasun Hospital, 264, Seoyang-ro, Hwasun-eup, Hwasun-gun 58128, Jeollanam-do, Korea;
| | - Hea Ji Kim
- School of Polymer Science and Engineering, Chonnam National University, 77 Yongbong-ro, Buk-gu, Gwangju 61186, Korea; (S.J.); (H.J.K.); (J.P.); (J.W.K.)
| | - Jieun Park
- School of Polymer Science and Engineering, Chonnam National University, 77 Yongbong-ro, Buk-gu, Gwangju 61186, Korea; (S.J.); (H.J.K.); (J.P.); (J.W.K.)
| | - Ji Woo Kim
- School of Polymer Science and Engineering, Chonnam National University, 77 Yongbong-ro, Buk-gu, Gwangju 61186, Korea; (S.J.); (H.J.K.); (J.P.); (J.W.K.)
| | - Changho Lee
- Department of Nuclear Medicine, Chonnam National University Hwasun Hospital, 264, Seoyang-ro, Hwasun-eup, Hwasun-gun 58128, Jeollanam-do, Korea;
- Department of Nuclear Medicine, Chonnam National University Medical School, 160, Baekseo-ro, Dong-gu, Gwangju 61469, Korea
- Department of Artificial Intelligence Convergence, Chonnam National University, 77 Yongbong-ro, Buk-gu, Gwangju 61186, Korea
| | - Hyungwoo Kim
- School of Polymer Science and Engineering, Chonnam National University, 77 Yongbong-ro, Buk-gu, Gwangju 61186, Korea; (S.J.); (H.J.K.); (J.P.); (J.W.K.)
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Tanabe S, Perkins EJ, Ono R, Sasaki H. Artificial intelligence in gastrointestinal diseases. Artif Intell Gastroenterol 2021; 2:69-76. [DOI: 10.35712/aig.v2.i3.69] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 04/09/2021] [Accepted: 06/04/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) applications are growing in medicine. It is important to understand the current state of the AI applications prior to utilizing in disease research and treatment. In this review, AI application in the diagnosis and treatment of gastrointestinal diseases are studied and summarized. In most cases, AI studies had large amounts of data, including images, to learn to distinguish disease characteristics according to a human’s perspectives. The detailed pros and cons of utilizing AI approaches should be investigated in advance to ensure the safe application of AI in medicine. Evidence suggests that the collaborative usage of AI in both diagnosis and treatment of diseases will increase the precision and effectiveness of medicine. Recent progress in genome technology such as genome editing provides a specific example where AI has revealed the diagnostic and therapeutic possibilities of RNA detection and targeting.
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Affiliation(s)
- Shihori Tanabe
- Division of Risk Assessment, Center for Biological Safety and Research, National Institute of Health Sciences, Kawasaki 210-9501, Japan
| | - Edward J Perkins
- Environmental Laboratory, US Army Engineer Research and Development Center, Vicksburg, MS 3180, United States
| | - Ryuichi Ono
- Division of Cellular and Molecular Toxicology, Center for Biological Safety and Research, National Institute of Health Sciences, Kawasaki 210-9501, Japan
| | - Hiroki Sasaki
- Department of Clinical Genomics, Fundamental Innovative Oncology Core, National Cancer Center Research Institute, Tokyo 104-0045, Japan
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Yoon C, Lee C. Recent Advances in Imaging Sensors and Applications. SENSORS 2021; 21:s21123970. [PMID: 34207534 PMCID: PMC8229651 DOI: 10.3390/s21123970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Accepted: 06/07/2021] [Indexed: 11/25/2022]
Affiliation(s)
- Changhan Yoon
- Department of Biomedical Engineering, Inje University, Gimhae 50834, Korea;
- Department of Nanoscience and Engineering, Inje University, Gimhae 50834, Korea
| | - Changho Lee
- Department of Artificial Intelligence Convergence, Chonnam National University, 77 Yongbong-ro, Buk-gu, Gwangju 61186, Korea
- Department of Nuclear Medicine, Chonnam National University Medical School & Hwasun Hospital, Hwasun 58128, Korea
- Correspondence:
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Mai TT, Yoo SW, Park S, Kim JY, Choi KH, Kim C, Kwon SY, Min JJ, Lee C. In Vivo Quantitative Vasculature Segmentation and Assessment for Photodynamic Therapy Process Monitoring Using Photoacoustic Microscopy. SENSORS 2021; 21:s21051776. [PMID: 33806466 PMCID: PMC7961824 DOI: 10.3390/s21051776] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 02/23/2021] [Accepted: 03/02/2021] [Indexed: 11/16/2022]
Abstract
Vascular damage is one of the therapeutic mechanisms of photodynamic therapy (PDT). In particular, short-term PDT treatments can effectively destroy malignant lesions while minimizing damage to nonmalignant tissue. In this study, we investigate the feasibility of label-free quantitative photoacoustic microscopy (PAM) for monitoring the vasculature changes under the effect of PDT in mouse ear melanoma tumors. In particular, quantitative vasculature evaluation was conducted based on Hessian filter segmentation. Three-dimensional morphological PAM and depth-resolved images before and after PDT treatment were acquired. In addition, five quantitative vasculature parameters, including the PA signal, vessel diameter, vessel density, perfused vessel density, and vessel complexity, were analyzed to evaluate the influence of PDT on four different areas: Two melanoma tumors, and control and normal vessel areas. The quantitative and qualitative results successfully demonstrated the potential of the proposed PAM-based quantitative approach to evaluate the effectiveness of the PDT method.
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Affiliation(s)
- Thi Thao Mai
- Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju 61186, Korea;
| | - Su Woong Yoo
- Department of Nuclear Medicine, Chonnam National University Hwasun Hospital, Hwasun, Jeollanamdo 58128, Korea; (S.W.Y.); (S.Y.K.); (J.-J.M.)
| | - Suhyun Park
- Interdisciplinary Program of Molecular Medicine, Chonnam National University, Gwangju 61186, Korea;
| | - Jin Young Kim
- Department of Creative IT Engineering and Electrical Engineering, Pohang University of Science and Technology (POSTECH), 77 Cheongam-ro, Nam-gu, Pohang, Gyeongbuk-do 37673, Korea; (J.Y.K.); (C.K.)
| | - Kang-Ho Choi
- Department of Neurology, Chonnam National University Hospital, 8 Hak-dong, Dong-gu, Gwangju 501-757, Korea;
| | - Chulhong Kim
- Department of Creative IT Engineering and Electrical Engineering, Pohang University of Science and Technology (POSTECH), 77 Cheongam-ro, Nam-gu, Pohang, Gyeongbuk-do 37673, Korea; (J.Y.K.); (C.K.)
| | - Seong Young Kwon
- Department of Nuclear Medicine, Chonnam National University Hwasun Hospital, Hwasun, Jeollanamdo 58128, Korea; (S.W.Y.); (S.Y.K.); (J.-J.M.)
- Interdisciplinary Program of Molecular Medicine, Chonnam National University, Gwangju 61186, Korea;
- Department of Nuclear Medicine, Chonnam National University Medical School, Jeollanamdo 58128, Korea
| | - Jung-Joon Min
- Department of Nuclear Medicine, Chonnam National University Hwasun Hospital, Hwasun, Jeollanamdo 58128, Korea; (S.W.Y.); (S.Y.K.); (J.-J.M.)
- Interdisciplinary Program of Molecular Medicine, Chonnam National University, Gwangju 61186, Korea;
- Department of Nuclear Medicine, Chonnam National University Medical School, Jeollanamdo 58128, Korea
| | - Changho Lee
- Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju 61186, Korea;
- Department of Nuclear Medicine, Chonnam National University Hwasun Hospital, Hwasun, Jeollanamdo 58128, Korea; (S.W.Y.); (S.Y.K.); (J.-J.M.)
- Interdisciplinary Program of Molecular Medicine, Chonnam National University, Gwangju 61186, Korea;
- Department of Nuclear Medicine, Chonnam National University Medical School, Jeollanamdo 58128, Korea
- Correspondence: ; Tel.: +82-61-379-2885
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Pilot Study: Quantitative Photoacoustic Evaluation of Peripheral Vascular Dynamics Induced by Carfilzomib In Vivo. SENSORS 2021; 21:s21030836. [PMID: 33513784 PMCID: PMC7865712 DOI: 10.3390/s21030836] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/26/2020] [Revised: 01/21/2021] [Accepted: 01/23/2021] [Indexed: 02/07/2023]
Abstract
Carfilzomib is mainly used to treat multiple myeloma. Several side effects have been reported in patients treated with carfilzomib, especially those associated with cardiovascular events, such as hypertension, congestive heart failure, and coronary artery disease. However, the side effects, especially the manifestation of cardiovascular events through capillaries, have not been fully investigated. Here, we performed a pilot experiment to monitor peripheral vascular dynamics in a mouse ear under the effects of carfilzomib using a quantitative photoacoustic vascular evaluation method. Before and after injecting the carfilzomib, bortezomib, and PBS solutions, we acquired high-resolution three-dimensional PAM data of the peripheral vasculature of the mouse ear during each experiment for 10 h. Then, the PAM maximum amplitude projection (MAP) images and five quantitative vascular parameters, i.e., photoacoustic (PA) signal, diameter, density, length fraction, and fractal dimension, were estimated. Quantitative results showed that carfilzomib induces a strong effect on the peripheral vascular system through a significant increase in all vascular parameters up to 50%, especially during the first 30 min after injection. Meanwhile, bortezomib and PBS do not have much impact on the peripheral vascular system. This pilot study verified PAM as a comprehensive method to investigate peripheral vasculature, along with the effects of carfilzomib. Therefore, we expect that PAM may be useful to predict cardiovascular events caused by carfilzomib.
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