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Vonk J, Knieling F, Kruijff S. Collection on clinical photoacoustic imaging. Eur J Nucl Med Mol Imaging 2024:10.1007/s00259-024-06780-0. [PMID: 38832946 DOI: 10.1007/s00259-024-06780-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2024]
Affiliation(s)
- J Vonk
- Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, Groningen, The Netherlands.
- Medical Imaging Center, University Medical Center Groningen, PO Box 30.001, Groningen, 9700 RB, The Netherlands.
| | - F Knieling
- Department of Paediatrics and Adolescent Medicine and German Center Immunotherapy (DZI), University Hospital Erlangen, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany
- Paediatric Experimental and Translational Imaging Laboratory (PETI-Lab), Department of Paediatrics and Adolescent Medicine, University Hospital Erlangen, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany
| | - S Kruijff
- Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, Groningen, The Netherlands
- Department of Surgery, University Medical Center Groningen, Groningen, The Netherlands
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
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2
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He H, Paetzold JC, Borner N, Riedel E, Gerl S, Schneider S, Fisher C, Ezhov I, Shit S, Li H, Ruckert D, Aguirre J, Biedermann T, Darsow U, Menze B, Ntziachristos V. Machine Learning Analysis of Human Skin by Optoacoustic Mesoscopy for Automated Extraction of Psoriasis and Aging Biomarkers. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:2074-2085. [PMID: 38241120 DOI: 10.1109/tmi.2024.3356180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/21/2024]
Abstract
Ultra-wideband raster-scan optoacoustic mesoscopy (RSOM) is a novel modality that has demonstrated unprecedented ability to visualize epidermal and dermal structures in-vivo. However, an automatic and quantitative analysis of three-dimensional RSOM datasets remains unexplored. In this work we present our framework: Deep Learning RSOM Analysis Pipeline (DeepRAP), to analyze and quantify morphological skin features recorded by RSOM and extract imaging biomarkers for disease characterization. DeepRAP uses a multi-network segmentation strategy based on convolutional neural networks with transfer learning. This strategy enabled the automatic recognition of skin layers and subsequent segmentation of dermal microvasculature with an accuracy equivalent to human assessment. DeepRAP was validated against manual segmentation on 25 psoriasis patients under treatment and our biomarker extraction was shown to characterize disease severity and progression well with a strong correlation to physician evaluation and histology. In a unique validation experiment, we applied DeepRAP in a time series sequence of occlusion-induced hyperemia from 10 healthy volunteers. We observe how the biomarkers decrease and recover during the occlusion and release process, demonstrating accurate performance and reproducibility of DeepRAP. Furthermore, we analyzed a cohort of 75 volunteers and defined a relationship between aging and microvascular features in-vivo. More precisely, this study revealed that fine microvascular features in the dermal layer have the strongest correlation to age. The ability of our newly developed framework to enable the rapid study of human skin morphology and microvasculature in-vivo promises to replace biopsy studies, increasing the translational potential of RSOM.
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Feng K, Tang J, Qiu R, Wang B, Wang J, Hu W. Fabrication of a core-shell nanofibrous wound dressing with an antioxidant effect on skin injury. J Mater Chem B 2024; 12:2384-2393. [PMID: 38349135 DOI: 10.1039/d3tb02911e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/29/2024]
Abstract
Oxidative stress is one of the obstacles preventing wound regeneration, especially for chronic wounds. Herein, designing a wound dressing with an anti-oxidant function holds great appeal for enhancing wound regeneration. In this study, a biocompatible and degradable nanofiber with a core-shell structure was fabricated via coaxial electrospinning, in which polycaprolactone (PCL) was applied as the core structure, while the shell was composed of a mixture of silk fibroin (SF) and tocopherol acetate (TA). The electrospun PST nanofibers were proven to have a network structure with significantly enhanced mechanical properties. The PSTs exhibited a diameter distribution with an average of 321 ± 134 nm, and the water contact angle of their surface is 124 ± 2°. The PSTs also exhibited good tissue compatibility, which can promote the adhesion and proliferation of L929 cells. Besides, the dissolution of silk fibroin encourages the release of TA, which could play a synergistic effect and regulate the oxidative stress effect in the damaged area, for it promotes the adhesion and proliferation of skin fibroblasts (L929), reduces the cytotoxicity of hydrogen peroxide to cells, and lowers the level of reactive oxygen species. The animal experiment indicated that the PSTs would promote the reconstruction of skin. These nanofibers are expected to repair skin ulcers related to diabetes.
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Affiliation(s)
- Kexin Feng
- School of Materials Science and Engineering, Hubei University, Wuhan 430062, China.
- Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Jinlan Tang
- School of Materials Science and Engineering, Hubei University, Wuhan 430062, China.
| | - Ruiyang Qiu
- School of Materials Science and Engineering, Hubei University, Wuhan 430062, China.
| | - Bin Wang
- Department of General Surgery, Shenzhen Children's Hospital, Shenzhen, 518038, China.
| | - Jianglin Wang
- Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Weikang Hu
- School of Materials Science and Engineering, Hubei University, Wuhan 430062, China.
- Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
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4
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Noversa de Sousa R, Tascilar K, Corte G, Atzinger A, Minopoulou I, Ohrndorf S, Waldner M, Schmidkonz C, Kuwert T, Knieling F, Kleyer A, Ramming A, Schett G, Simon D, Fagni F. Metabolic and molecular imaging in inflammatory arthritis. RMD Open 2024; 10:e003880. [PMID: 38341194 PMCID: PMC10862311 DOI: 10.1136/rmdopen-2023-003880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 01/25/2024] [Indexed: 02/12/2024] Open
Abstract
It is known that metabolic shifts and tissue remodelling precede the development of visible inflammation and structural organ damage in inflammatory rheumatic diseases such as the inflammatory arthritides. As such, visualising and measuring metabolic tissue activity could be useful to identify biomarkers of disease activity already in a very early phase. Recent advances in imaging have led to the development of so-called 'metabolic imaging' tools that can detect these changes in metabolism in an increasingly accurate manner and non-invasively.Nuclear imaging techniques such as 18F-D-glucose and fibroblast activation protein inhibitor-labelled positron emission tomography are increasingly used and have yielded impressing results in the visualisation (including whole-body staging) of inflammatory changes in both early and established arthritis. Furthermore, optical imaging-based bedside techniques such as multispectral optoacoustic tomography and fluorescence optical imaging are advancing our understanding of arthritis by identifying intra-articular metabolic changes that correlate with the onset of inflammation with high precision and without the need of ionising radiation.Metabolic imaging holds great potential for improving the management of patients with inflammatory arthritis by contributing to early disease interception and improving diagnostic accuracy, thereby paving the way for a more personalised approach to therapy strategies including preventive strategies. In this narrative review, we discuss state-of-the-art metabolic imaging methods used in the assessment of arthritis and inflammation, and we advocate for more extensive research endeavours to elucidate their full field of application in rheumatology.
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Affiliation(s)
- Rita Noversa de Sousa
- Department of Internal Medicine 3, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
- Serviço de Medicina Interna, Hospital Pedro Hispano, Matosinhos, Portugal
- Deutsches Zentrum fuer Immuntherapie (DZI), Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Koray Tascilar
- Department of Internal Medicine 3, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
- Deutsches Zentrum fuer Immuntherapie (DZI), Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Giulia Corte
- Department of Internal Medicine 3, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
- Deutsches Zentrum fuer Immuntherapie (DZI), Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Armin Atzinger
- Department of Nuclear Medicine, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Ioanna Minopoulou
- Department of Internal Medicine 3, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
- Deutsches Zentrum fuer Immuntherapie (DZI), Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Sarah Ohrndorf
- Department of Rheumatology and Clinical Immunology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Maximilian Waldner
- Department of Internal Medicine 3, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Christian Schmidkonz
- Department of Nuclear Medicine, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
- Institute for Medical Engineering, Ostbayerische Technische Hochschule Amberg-Weiden, Amberg, Germany
| | - Torsten Kuwert
- Department of Nuclear Medicine, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Ferdinand Knieling
- Department of Pediatrics and Adolescent Medicine, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Arnd Kleyer
- Department of Internal Medicine 3, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
- Department of Rheumatology and Clinical Immunology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Andreas Ramming
- Department of Internal Medicine 3, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
- Deutsches Zentrum fuer Immuntherapie (DZI), Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Georg Schett
- Department of Internal Medicine 3, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
- Deutsches Zentrum fuer Immuntherapie (DZI), Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - David Simon
- Department of Internal Medicine 3, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
- Department of Rheumatology and Clinical Immunology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Filippo Fagni
- Department of Internal Medicine 3, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
- Deutsches Zentrum fuer Immuntherapie (DZI), Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
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He H, Fischer C, Darsow U, Aguirre J, Ntziachristos V. Quality control in clinical raster-scan optoacoustic mesoscopy. PHOTOACOUSTICS 2024; 35:100582. [PMID: 38312808 PMCID: PMC10835451 DOI: 10.1016/j.pacs.2023.100582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 12/15/2023] [Accepted: 12/21/2023] [Indexed: 02/06/2024]
Abstract
Optoacoustic (photoacoustic) mesoscopy bridges the gap between optoacoustic microscopy and macroscopy and enables high-resolution visualization deeper than optical microscopy. Nevertheless, as images may be affected by motion and noise, it is critical to develop methodologies that offer standardization and quality control to ensure that high-quality datasets are reproducibly obtained from patient scans. Such development is particularly important for ensuring reliability in applying machine learning methods or for reliably measuring disease biomarkers. We propose herein a quality control scheme to assess the quality of data collected. A reference scan of a suture phantom is performed to characterize the system noise level before each raster-scan optoacoustic mesoscopy (RSOM) measurement. Using the recorded RSOM data, we develop a method that estimates the amount of motion in the raw data. These motion metrics are employed to classify the quality of raw data collected and derive a quality assessment index (QASIN) for each raw measurement. Using simulations, we propose a selection criterion of images with sufficient QASIN, leading to the compilation of RSOM datasets with consistent quality. Using 160 RSOM measurements from healthy volunteers, we show that RSOM images that were selected using QASIN were of higher quality and fidelity compared to non-selected images. We discuss how this quality control scheme can enable the standardization of RSOM images for clinical and biomedical applications.
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Affiliation(s)
- Hailong He
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Neuherberg, Germany
- Chair of Biological Imaging at the Central Institute for Translational Cancer Research (TranslaTUM), School of Medicine, Technical University of Munich, Munich, Germany
| | - Chiara Fischer
- Department of Dermatology and Allergy, Technical University of Munich, Munich, Germany
| | - Ulf Darsow
- Department of Dermatology and Allergy, Technical University of Munich, Munich, Germany
| | - Juan Aguirre
- Departamento de Tecnología Electrónica y de las Comunicaciones, Universidad Autónoma de Madrid, Madrid, Spain
- Instituto de Investigación Sanitaria de la Fundación Jimenez Diaz, Madrid, Spain
| | - Vasilis Ntziachristos
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Neuherberg, Germany
- Chair of Biological Imaging at the Central Institute for Translational Cancer Research (TranslaTUM), School of Medicine, Technical University of Munich, Munich, Germany
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Karlas A, Katsouli N, Fasoula NA, Bariotakis M, Chlis NK, Omar M, He H, Iakovakis D, Schäffer C, Kallmayer M, Füchtenbusch M, Ziegler A, Eckstein HH, Hadjileontiadis L, Ntziachristos V. Dermal features derived from optoacoustic tomograms via machine learning correlate microangiopathy phenotypes with diabetes stage. Nat Biomed Eng 2023; 7:1667-1682. [PMID: 38049470 PMCID: PMC10727986 DOI: 10.1038/s41551-023-01151-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 10/24/2023] [Indexed: 12/06/2023]
Abstract
Skin microangiopathy has been associated with diabetes. Here we show that skin-microangiopathy phenotypes in humans can be correlated with diabetes stage via morphophysiological cutaneous features extracted from raster-scan optoacoustic mesoscopy (RSOM) images of skin on the leg. We obtained 199 RSOM images from 115 participants (40 healthy and 75 with diabetes), and used machine learning to segment skin layers and microvasculature to identify clinically explainable features pertaining to different depths and scales of detail that provided the highest predictive power. Features in the dermal layer at the scale of detail of 0.1-1 mm (such as the number of junction-to-junction branches) were highly sensitive to diabetes stage. A 'microangiopathy score' compiling the 32 most-relevant features predicted the presence of diabetes with an area under the receiver operating characteristic curve of 0.84. The analysis of morphophysiological cutaneous features via RSOM may allow for the discovery of diabetes biomarkers in the skin and for the monitoring of diabetes status.
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Affiliation(s)
- Angelos Karlas
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Neuherberg, Germany
- Chair of Biological Imaging at the Central Institute for Translational Cancer Research (TranslaTUM), School of Medicine, Technical University of Munich, Munich, Germany
- Department for Vascular and Endovascular Surgery, Klinikum rechts der Isar, Technical University of Munich (TUM), Munich, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany
| | - Nikoletta Katsouli
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Neuherberg, Germany
- Chair of Biological Imaging at the Central Institute for Translational Cancer Research (TranslaTUM), School of Medicine, Technical University of Munich, Munich, Germany
| | - Nikolina-Alexia Fasoula
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Neuherberg, Germany
- Chair of Biological Imaging at the Central Institute for Translational Cancer Research (TranslaTUM), School of Medicine, Technical University of Munich, Munich, Germany
| | - Michail Bariotakis
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Neuherberg, Germany
- Chair of Biological Imaging at the Central Institute for Translational Cancer Research (TranslaTUM), School of Medicine, Technical University of Munich, Munich, Germany
| | - Nikolaos-Kosmas Chlis
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Neuherberg, Germany
- Chair of Biological Imaging at the Central Institute for Translational Cancer Research (TranslaTUM), School of Medicine, Technical University of Munich, Munich, Germany
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Murad Omar
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Neuherberg, Germany
- Chair of Biological Imaging at the Central Institute for Translational Cancer Research (TranslaTUM), School of Medicine, Technical University of Munich, Munich, Germany
| | - Hailong He
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Neuherberg, Germany
- Chair of Biological Imaging at the Central Institute for Translational Cancer Research (TranslaTUM), School of Medicine, Technical University of Munich, Munich, Germany
| | - Dimitrios Iakovakis
- Department of Biomedical Engineering, Healthcare Engineering Innovation Center (HEIC), Khalifa University, Abu Dhabi, United Arab Emirates
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Christoph Schäffer
- Department for Vascular and Endovascular Surgery, Klinikum rechts der Isar, Technical University of Munich (TUM), Munich, Germany
| | - Michael Kallmayer
- Department for Vascular and Endovascular Surgery, Klinikum rechts der Isar, Technical University of Munich (TUM), Munich, Germany
| | | | - Annette Ziegler
- Forschergruppe Diabetes e.V., Helmholtz Zentrum München, Neuherberg, Germany
- Institute of Diabetes Research, Helmholtz Zentrum München, Neuherberg, Germany
- Forschergruppe Diabetes, Klinikum rechts der Isar, Technical University of Munich (TUM), Munich, Germany
| | - Hans-Henning Eckstein
- Department for Vascular and Endovascular Surgery, Klinikum rechts der Isar, Technical University of Munich (TUM), Munich, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany
| | - Leontios Hadjileontiadis
- Department of Biomedical Engineering, Healthcare Engineering Innovation Center (HEIC), Khalifa University, Abu Dhabi, United Arab Emirates
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Vasilis Ntziachristos
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Neuherberg, Germany.
- Chair of Biological Imaging at the Central Institute for Translational Cancer Research (TranslaTUM), School of Medicine, Technical University of Munich, Munich, Germany.
- DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany.
- Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich, Munich, Germany.
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Siegel AP, Avanaki K. The power of light and sound: optoacoustic skin imaging for diabetes progression monitoring. LIGHT, SCIENCE & APPLICATIONS 2023; 12:283. [PMID: 37996426 PMCID: PMC10667326 DOI: 10.1038/s41377-023-01322-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2023]
Abstract
Diabetes progression is marked by damage to vascular and neural networks. Raster-scan optoacoustic mesoscopy holds the potential to measure extent of diabetes progression by analyzing changes in skin vasculature.
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Affiliation(s)
- Amanda P Siegel
- Richard and Loan Hill Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, USA
| | - Kamran Avanaki
- Richard and Loan Hill Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, USA.
- Section of Neonatology, Department of Pediatrics, UI Health Children's Hospital of the University of Illinois at Chicago, Chicago, IL, USA.
- Department of Dermatology, University of Illinois at Chicago, Chicago, IL, USA.
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