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De Winne J, Strumane A, Babin D, Luthman S, Luong H, Philips W. Multispectral indices for real-time and non-invasive tissue ischemia monitoring using snapshot cameras. BIOMEDICAL OPTICS EXPRESS 2024; 15:641-655. [PMID: 38404312 PMCID: PMC10890856 DOI: 10.1364/boe.506084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 11/14/2023] [Accepted: 11/14/2023] [Indexed: 02/27/2024]
Abstract
An adequate supply of oxygen-rich blood is vital to maintain cell homeostasis, cellular metabolism, and overall tissue health. While classical methods of measuring tissue ischemia are often invasive, localized and require skin contact or contrast agents, spectral imaging shows promise as a non-invasive, wide field, and contrast-free approach. We evaluate three novel reflectance-based spectral indices from the 460 - 840 nm spectral range. With the aim of enabling real time visualization of tissue ischemia, information is extracted from only 2-3 spectral bands. Video-rate spectral data was acquired from arm occlusion experiments in 27 healthy volunteers. The performance of the indices was evaluated against binary Support Vector Machine (SVM) classification of healthy versus ischemic skin tissue, two other indices from literature, and tissue oxygenation estimated using spectral unmixing. Robustness was tested by evaluating these under various lighting conditions and on both the dorsal and palmar sides of the hand. A novel index with real-time capabilities using reflectance information only from 547 nm and 556 nm achieves an average classification accuracy of 88.48, compared to 92.65 using an SVM trained on all available wavelengths. Furthermore, the index has a higher accuracy compared to reference methods and its time dynamics compare well against the expected clinical responses. This holds promise for robust real-time detection of tissue ischemia, possibly contributing to improved patient care and clinical outcomes.
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Affiliation(s)
- Jens De Winne
- Department of Telecommunications and Information Processing (TELIN) - PI Research Group, Ghent University-imec, 9000 Ghent, Belgium
- Interuniversity Micro-Electronics Center (IMEC) vzw, 3000 Leuven, Belgium
| | - Anoek Strumane
- Department of Telecommunications and Information Processing (TELIN) - PI Research Group, Ghent University-imec, 9000 Ghent, Belgium
| | - Danilo Babin
- Department of Telecommunications and Information Processing (TELIN) - PI Research Group, Ghent University-imec, 9000 Ghent, Belgium
| | - Siri Luthman
- Interuniversity Micro-Electronics Center (IMEC) vzw, 3000 Leuven, Belgium
| | - Hiep Luong
- Department of Telecommunications and Information Processing (TELIN) - PI Research Group, Ghent University-imec, 9000 Ghent, Belgium
| | - Wilfried Philips
- Department of Telecommunications and Information Processing (TELIN) - PI Research Group, Ghent University-imec, 9000 Ghent, Belgium
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Pfahl A, Polat ST, Köhler H, Gockel I, Melzer A, Chalopin C. Switchable LED-based laparoscopic multispectral system for rapid high-resolution perfusion imaging. JOURNAL OF BIOMEDICAL OPTICS 2023; 28:126002. [PMID: 38094710 PMCID: PMC10718192 DOI: 10.1117/1.jbo.28.12.126002] [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: 05/09/2023] [Revised: 11/03/2023] [Accepted: 11/08/2023] [Indexed: 12/18/2023]
Abstract
Significance Multispectral imaging (MSI) is an approach for real-time, quantitative, and non-invasive tissue perfusion measurements. Current laparoscopic systems based on mosaic sensors or filter wheels lack high spatial resolution or acceptable frame rates. Aim To develop a laparoscopic system for MSI-based color video and tissue perfusion imaging during gastrointestinal surgery without compromising spatial or temporal resolution. Approach The system was built with 14 switchable light-emitting diodes in the visible and near-infrared spectral range, a 4K image sensor, and a 10 mm laparoscope. Illumination patterns were created for tissue oxygenation and hemoglobin content monitoring. The system was calibrated to a clinically approved laparoscopic hyperspectral system using linear regression models and evaluated in an occlusion study with 36 volunteers. Results The root mean squared errors between the MSI and reference system were 0.073 for hemoglobin content, 0.039 for oxygenation in deeper tissue layers, and 0.093 for superficial oxygenation. The spatial resolution at a working distance of 45 mm was 156 μ m . The effective frame rate was 20 fps. Conclusions High-resolution perfusion monitoring was successfully achieved. Hardware optimizations will increase the frame rate. Parameter optimizations through alternative illumination patterns, regression, or assumed tissue models are planned. Intraoperative measurements must confirm the suitability during surgery.
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Affiliation(s)
- Annekatrin Pfahl
- Leipzig University, Faculty of Medicine, Innovation Center Computer Assisted Surgery, Leipzig, Germany
| | - Süleyman T. Polat
- Leipzig University, Faculty of Medicine, Innovation Center Computer Assisted Surgery, Leipzig, Germany
| | - Hannes Köhler
- Leipzig University, Faculty of Medicine, Innovation Center Computer Assisted Surgery, Leipzig, Germany
| | - Ines Gockel
- University Hospital of Leipzig, Department of Visceral, Transplant, Thoracic, and Vascular Surgery, Leipzig, Germany
| | - Andreas Melzer
- Leipzig University, Faculty of Medicine, Innovation Center Computer Assisted Surgery, Leipzig, Germany
- University of Dundee, School of Medicine, Institute for Medical Science and Technology, Dundee, United Kingdom
| | - Claire Chalopin
- Leipzig University, Faculty of Medicine, Innovation Center Computer Assisted Surgery, Leipzig, Germany
- University of Applied Sciences and Arts, Faculty of Engineering and Health, Göttingen, Germany
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He Q, Li W, Shi Y, Yu Y, Geng W, Sun Z, Wang RK. SpeCamX: mobile app that turns unmodified smartphones into multispectral imagers. BIOMEDICAL OPTICS EXPRESS 2023; 14:4929-4946. [PMID: 37791269 PMCID: PMC10545193 DOI: 10.1364/boe.497602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 08/13/2023] [Accepted: 08/14/2023] [Indexed: 10/05/2023]
Abstract
We present the development of SpeCamX, a mobile application that enables an unmodified smartphone into a multispectral imager. Multispectral imaging provides detailed spectral information about objects or scenes, but its accessibility has been limited due to its specialized requirements for the device. SpeCamX overcomes this limitation by utilizing the RGB photographs captured by smartphones and converting them into multispectral images spanning a range of 420 to 680 nm without a need for internal modifications or external attachments. The app also includes plugin functions for extracting medical information from the resulting multispectral data cube. In a clinical study, SpeCamX was used to implement an augmented smartphone bilirubinometer, predicting blood bilirubin levels (BBL) with superior performance in accuracy, efficiency and stability compared to default smartphone cameras. This innovative technology democratizes multispectral imaging, making it accessible to a wider audience and opening new possibilities for both medical and non-medical applications.
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Affiliation(s)
- Qinghua He
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Science, Changchun, Jilin 130033, China
- Department of Bioengineering, University of Washington, Seattle, Washington 98105, USA
| | - Wanyu Li
- Department of Hepatobiliary and pancreatic Medicine, The first Hospital of Jilin University NO.71 Xinmin Street, Changchun, Jilin 130021, China
| | - Yaping Shi
- Department of Bioengineering, University of Washington, Seattle, Washington 98105, USA
| | - Yi Yu
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Science, Changchun, Jilin 130033, China
| | - Wenqian Geng
- Department of Hepatobiliary and pancreatic Medicine, The first Hospital of Jilin University NO.71 Xinmin Street, Changchun, Jilin 130021, China
| | - Zhiyuan Sun
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Science, Changchun, Jilin 130033, China
| | - Ruikang K Wang
- Department of Bioengineering, University of Washington, Seattle, Washington 98105, USA
- Department of Ophthalmology, University of Washington, Seattle, Washington 98109, USA
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Hudock MR, Pinezich MR, Mir SM, Chen J, Bacchetta M, Vunjak-Novakovic G, Kim J. Emerging Imaging Modalities for Functional Assessment of Donor Lungs Ex Vivo. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2023; 25:100432. [PMID: 36778755 PMCID: PMC9913406 DOI: 10.1016/j.cobme.2022.100432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The severe shortage of functional donor lungs that can be offered to recipients has been a major challenge in lung transplantation. Innovative ex vivo lung perfusion (EVLP) and tissue engineering methodologies are now being developed to repair damaged donor lungs that are deemed unsuitable for transplantation. To assess the efficacy of donor lung reconditioning methods intended to rehabilitate rejected donor lungs, monitoring of lung function with improved spatiotemporal resolution is needed. Recent developments in live imaging are enabling non-destructive, direct, and longitudinal modalities for assessing local tissue and whole lung functions. In this review, we describe how emerging live imaging modalities can be coupled with lung tissue engineering approaches to promote functional recovery of ex vivo donor lungs.
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Affiliation(s)
- Maria R. Hudock
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Meghan R. Pinezich
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Seyed Mohammad Mir
- Department of Biomedical Engineering, Stevens Institute of Technology, Hoboken, NJ, USA
| | - Jiawen Chen
- Department of Biomedical Engineering, Stevens Institute of Technology, Hoboken, NJ, USA
| | - Matthew Bacchetta
- Department of Cardiac Surgery, Vanderbilt University, Nashville, TN, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Gordana Vunjak-Novakovic
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
- Department of Medicine, Columbia University, New York, NY, USA
| | - Jinho Kim
- Department of Biomedical Engineering, Stevens Institute of Technology, Hoboken, NJ, USA
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Tan H, Cadusch JJ, Meng J, Crozier KB. Genetic optimization of mid-infrared filters for a machine learning chemical classifier. OPTICS EXPRESS 2022; 30:18330-18347. [PMID: 36221637 DOI: 10.1364/oe.459067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 04/30/2022] [Indexed: 06/16/2023]
Abstract
Miniaturized mid-infrared spectrometers present opportunities for applications that range from health monitoring to agriculture. One approach combines arrays of spectral filters with infrared photodetectors, called filter-array detector-array (FADA) microspectrometers. A paper recently reported a FADA microspectrometer in tandem with machine learning for chemical identification. In that work, a FADA microspectrometer with 20 filters was assembled and tested. The filters were band-pass, or band-stop designs that evenly spanned the microspectrometer's operating wavelength range. However, given that a machine learning classifier can be trained on an arbitrary filter basis, it is not apparent that evenly spaced filters are optimal. Here, through simulations with noise, we use a genetic algorithm to optimize six bandpass filters to best identify liquid and gaseous chemicals. We report that the classifiers trained with the optimized filter sets outperform those trained with evenly spaced filter sets and those handpicked to target the absorption bands of the chemicals investigated.
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Seidlitz S, Sellner J, Odenthal J, Özdemir B, Studier-Fischer A, Knödler S, Ayala L, Adler TJ, Kenngott HG, Tizabi M, Wagner M, Nickel F, Müller-Stich BP, Maier-Hein L. Robust deep learning-based semantic organ segmentation in hyperspectral images. Med Image Anal 2022; 80:102488. [DOI: 10.1016/j.media.2022.102488] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Revised: 03/28/2022] [Accepted: 05/20/2022] [Indexed: 12/15/2022]
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Gröhl J, Dreher KK, Schellenberg M, Rix T, Holzwarth N, Vieten P, Ayala L, Bohndiek SE, Seitel A, Maier-Hein L. SIMPA: an open-source toolkit for simulation and image processing for photonics and acoustics. JOURNAL OF BIOMEDICAL OPTICS 2022; 27:JBO-210395SSR. [PMID: 35380031 PMCID: PMC8978263 DOI: 10.1117/1.jbo.27.8.083010] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 02/28/2022] [Indexed: 05/09/2023]
Abstract
SIGNIFICANCE Optical and acoustic imaging techniques enable noninvasive visualisation of structural and functional properties of tissue. The quantification of measurements, however, remains challenging due to the inverse problems that must be solved. Emerging data-driven approaches are promising, but they rely heavily on the presence of high-quality simulations across a range of wavelengths due to the lack of ground truth knowledge of tissue acoustical and optical properties in realistic settings. AIM To facilitate this process, we present the open-source simulation and image processing for photonics and acoustics (SIMPA) Python toolkit. SIMPA is being developed according to modern software design standards. APPROACH SIMPA enables the use of computational forward models, data processing algorithms, and digital device twins to simulate realistic images within a single pipeline. SIMPA's module implementations can be seamlessly exchanged as SIMPA abstracts from the concrete implementation of each forward model and builds the simulation pipeline in a modular fashion. Furthermore, SIMPA provides comprehensive libraries of biological structures, such as vessels, as well as optical and acoustic properties and other functionalities for the generation of realistic tissue models. RESULTS To showcase the capabilities of SIMPA, we show examples in the context of photoacoustic imaging: the diversity of creatable tissue models, the customisability of a simulation pipeline, and the degree of realism of the simulations. CONCLUSIONS SIMPA is an open-source toolkit that can be used to simulate optical and acoustic imaging modalities. The code is available at: https://github.com/IMSY-DKFZ/simpa, and all of the examples and experiments in this paper can be reproduced using the code available at: https://github.com/IMSY-DKFZ/simpa_paper_experiments.
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Affiliation(s)
- Janek Gröhl
- German Cancer Research Center (DKFZ), Division of Intelligent Medical Systems, Heidelberg, Germany
| | - Kris K. Dreher
- German Cancer Research Center (DKFZ), Division of Intelligent Medical Systems, Heidelberg, Germany
- Heidelberg University, Faculty of Physics and Astronomy, Heidelberg, Germany
| | - Melanie Schellenberg
- German Cancer Research Center (DKFZ), Division of Intelligent Medical Systems, Heidelberg, Germany
- Heidelberg University, Faculty of Mathematics and Computer Science, Heidelberg, Germany
- HIDSS4Health - Helmholtz Information and Data Science School for Health, Heidelberg, Germany
| | - Tom Rix
- German Cancer Research Center (DKFZ), Division of Intelligent Medical Systems, Heidelberg, Germany
- Heidelberg University, Faculty of Mathematics and Computer Science, Heidelberg, Germany
| | - Niklas Holzwarth
- German Cancer Research Center (DKFZ), Division of Intelligent Medical Systems, Heidelberg, Germany
| | - Patricia Vieten
- German Cancer Research Center (DKFZ), Division of Intelligent Medical Systems, Heidelberg, Germany
- Heidelberg University, Faculty of Physics and Astronomy, Heidelberg, Germany
| | - Leonardo Ayala
- German Cancer Research Center (DKFZ), Division of Intelligent Medical Systems, Heidelberg, Germany
- Heidelberg University, Medical Faculty, Heidelberg, Germany
| | - Sarah E. Bohndiek
- University of Cambridge, Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, United Kingdom
- University of Cambridge, Department of Physics, Cambridge, United Kingdom
| | - Alexander Seitel
- German Cancer Research Center (DKFZ), Division of Intelligent Medical Systems, Heidelberg, Germany
| | - Lena Maier-Hein
- German Cancer Research Center (DKFZ), Division of Intelligent Medical Systems, Heidelberg, Germany
- Heidelberg University, Faculty of Mathematics and Computer Science, Heidelberg, Germany
- Heidelberg University, Medical Faculty, Heidelberg, Germany
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