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Tao R, Gröhl J, Hacker L, Pifferi A, Roblyer D, Bohndiek SE. Tutorial on methods for estimation of optical absorption and scattering properties of tissue. JOURNAL OF BIOMEDICAL OPTICS 2024; 29:060801. [PMID: 38864093 PMCID: PMC11166171 DOI: 10.1117/1.jbo.29.6.060801] [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: 02/26/2024] [Revised: 05/09/2024] [Accepted: 05/10/2024] [Indexed: 06/13/2024]
Abstract
Significance The estimation of tissue optical properties using diffuse optics has found a range of applications in disease detection, therapy monitoring, and general health care. Biomarkers derived from the estimated optical absorption and scattering coefficients can reflect the underlying progression of many biological processes in tissues. Aim Complex light-tissue interactions make it challenging to disentangle the absorption and scattering coefficients, so dedicated measurement systems are required. We aim to help readers understand the measurement principles and practical considerations needed when choosing between different estimation methods based on diffuse optics. Approach The estimation methods can be categorized as: steady state, time domain, time frequency domain (FD), spatial domain, and spatial FD. The experimental measurements are coupled with models of light-tissue interactions, which enable inverse solutions for the absorption and scattering coefficients from the measured tissue reflectance and/or transmittance. Results The estimation of tissue optical properties has been applied to characterize a variety of ex vivo and in vivo tissues, as well as tissue-mimicking phantoms. Choosing a specific estimation method for a certain application has to trade-off its advantages and limitations. Conclusion Optical absorption and scattering property estimation is an increasingly important and accessible approach for medical diagnosis and health monitoring.
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Affiliation(s)
- Ran Tao
- University of Cambridge, Department of Physics, Cambridge, United Kingdom
- University of Cambridge, Cancer Research UK Cambridge Institute, Li Ka Shing Centre, Cambridge, United Kingdom
| | - Janek Gröhl
- University of Cambridge, Department of Physics, Cambridge, United Kingdom
- University of Cambridge, Cancer Research UK Cambridge Institute, Li Ka Shing Centre, Cambridge, United Kingdom
| | - Lina Hacker
- University of Oxford, Department of Oncology, Oxford, United Kingdom
| | | | - Darren Roblyer
- Boston University, Department of Electrical and Computer Engineering, Boston, Massachusetts, United States
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Sarah E. Bohndiek
- University of Cambridge, Department of Physics, Cambridge, United Kingdom
- University of Cambridge, Cancer Research UK Cambridge Institute, Li Ka Shing Centre, Cambridge, United Kingdom
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Crowley J, Gordon GSD. Designing and simulating realistic spatial frequency domain imaging systems using open-source 3D rendering software. BIOMEDICAL OPTICS EXPRESS 2023; 14:2523-2538. [PMID: 37342713 PMCID: PMC10278632 DOI: 10.1364/boe.484286] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 04/24/2023] [Accepted: 04/25/2023] [Indexed: 06/23/2023]
Abstract
Spatial frequency domain imaging (SFDI) is a low-cost imaging technique that maps absorption and reduced scattering coefficients, offering improved contrast for important tissue structures such as tumours. Practical SFDI systems must cope with various imaging geometries including imaging planar samples ex vivo, imaging inside tubular lumen in vivo e.g. for endoscopy, and measuring tumours or polyps of varying morphology. There is a need for a design and simulation tool to accelerate design of new SFDI systems and simulate realistic performance under these scenarios. We present such a system implemented using open-source 3D design and ray-tracing software Blender that simulates media with realistic absorption and scattering in a wide range of geometries. By using Blender's Cycles ray-tracing engine, our system simulates effects such as varying lighting, refractive index changes, non-normal incidence, specular reflections and shadows, enabling realistic evaluation of new designs. We first demonstrate quantitative agreement between Monte-Carlo simulated absorption and reduced scattering coefficients with those simulated from our Blender system, achieving 16 % discrepancy in absorption coefficient and 18 % in reduced scattering coefficient. However, we then show that using an empirically derived look-up table the errors reduce to 1 % and 0.7 % respectively. Next, we simulate SFDI mapping of absorption, scattering and shape for simulated tumour spheroids, demonstrating enhanced contrast. Finally we demonstrate SFDI mapping inside a tubular lumen, which highlighted a important design insight: custom look-up tables must be generated for different longitudinal sections of the lumen. With this approach we achieved 2 % absorption error and 2 % scattering error. We anticipate our simulation system will aid in the design of novel SFDI systems for key biomedical applications.
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Affiliation(s)
- Jane Crowley
- Optics & Photonics Group, Department of Electrical and
Electronic Engineering, University of Nottingham, Nottingham, United
Kingdom
| | - George S. D. Gordon
- Optics & Photonics Group, Department of Electrical and
Electronic Engineering, University of Nottingham, Nottingham, United
Kingdom
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Extracting Tissue Optical Properties and Detecting Bruised Tissue in Pears Quickly and Accurately Based on Spatial Frequency Domain Imaging and Machine Learning. Foods 2023; 12:foods12020238. [PMID: 36673330 PMCID: PMC9858491 DOI: 10.3390/foods12020238] [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: 11/20/2022] [Revised: 12/28/2022] [Accepted: 12/29/2022] [Indexed: 01/07/2023] Open
Abstract
Recently, Spatial Frequency Domain Imaging (SFDI) has gradually become an alternative method to extract tissue optical properties (OPs), as it provides a wide-field, no-contact acquisition. SFDI extracts OPs by least-square fitting (LSF) based on the diffuse approximation equation, but there are shortcomings in the speed and accuracy of extracting OPs. This study proposed a Long Short-term Memory Regressor (LSTMR) solution to extract tissue OPs. This method allows for fast and accurate extraction of tissue OPs. Firstly, the imaging system was developed, which is more compact and portable than conventional SFDI systems. Next, numerical simulation was performed using the Monte Carlo forward model to obtain the dataset, and then the mapping model was established using the dataset. Finally, the model was applied to detect the bruised tissue of 'crown' pears. The results show that the mean absolute errors of the absorption coefficient and the reduced scattering coefficient are no more than 0.32% and 0.21%, and the bruised tissue of 'crown' pears can be highlighted by the change of OPs. Compared with the LSF, the speed of extracting tissue OPs is improved by two orders of magnitude, and the accuracy is greatly improved. The study contributes to the rapid and accurate extraction of tissue OPs based on SFDI and has great potential in food safety assessment.
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Zhang L, Huang D, Chen X, Zhu L, Chen X, Xie Z, Huang G, Gao J, Shi W, Cui G. Visible near-infrared hyperspectral imaging and supervised classification for the detection of small intestinal necrosis tissue in vivo. BIOMEDICAL OPTICS EXPRESS 2022; 13:6061-6080. [PMID: 36733734 PMCID: PMC9872898 DOI: 10.1364/boe.470202] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 09/23/2022] [Accepted: 09/26/2022] [Indexed: 06/18/2023]
Abstract
Complete recognition of necrotic areas during small bowel tissue resection remains challenging due to the lack of optimal intraoperative aid identification techniques. This research utilizes hyperspectral imaging techniques to automatically distinguish normal and necrotic areas of small intestinal tissue. Sample data were obtained from the animal model of small intestinal tissue of eight Japanese large-eared white rabbits developed by experienced physicians. A spectral library of normal and necrotic regions of small intestinal tissue was created and processed using six different supervised classification algorithms. The results show that hyperspectral imaging combined with supervised classification algorithms can be a suitable technique to automatically distinguish between normal and necrotic areas of small intestinal tissue. This new technique could aid physicians in objectively identify normal and necrotic areas of small intestinal tissue.
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Affiliation(s)
- LeChao Zhang
- College of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun, Jilin, 130000, China
- Zhongshan Research Institute, Changchun University of Science and Technology, Zhongshan, Guangdong, 528400, China
| | - DanFei Huang
- College of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun, Jilin, 130000, China
- Zhongshan Research Institute, Changchun University of Science and Technology, Zhongshan, Guangdong, 528400, China
| | - XiaoJing Chen
- College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou, Zhejiang, 325000, China
| | - LiBin Zhu
- Pediatric General Surgery, The Second Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China
| | - XiaoQing Chen
- Pediatric General Surgery, The Second Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China
| | - ZhongHao Xie
- College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou, Zhejiang, 325000, China
| | - GuangZao Huang
- College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou, Zhejiang, 325000, China
| | - JunZhao Gao
- College of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun, Jilin, 130000, China
- Zhongshan Research Institute, Changchun University of Science and Technology, Zhongshan, Guangdong, 528400, China
| | - Wen Shi
- College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou, Zhejiang, 325000, China
| | - GuiHua Cui
- College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou, Zhejiang, 325000, China
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Song B, Yin X, Fan Y, Zhao Y. Quantitative spatial mapping of tissue water and lipid content using spatial frequency domain imaging in the 900- to 1000-nm wavelength region. JOURNAL OF BIOMEDICAL OPTICS 2022; 27:105005. [PMID: 36303279 PMCID: PMC9612091 DOI: 10.1117/1.jbo.27.10.105005] [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: 06/01/2022] [Accepted: 10/06/2022] [Indexed: 06/16/2023]
Abstract
SIGNIFICANCE Water and lipid are key participants of many biological processes, but there are few label-free, non-contact optical methods that can spatially map these components in-vivo. Shortwave infrared meso-patterned imaging (SWIR-MPI) is an emerging technique that successfully addresses this need. However, it requires a dedicated SWIR camera to probe the 900- to 1300-nm wavelength region, which hinders practical translation of the technology. AIM Compared with SWIR-MPI, we aim to develop a new technique that can dramatically reduce the cost in detector while maintaining high accuracy for the quantification of tissue water and lipid content. APPROACH By utilizing water and lipid absorption features in the 900- to 1000-nm wavelength region as well as optimal wavelength and spatial frequency combinations, we develop a new imaging technique based on spatial frequency domain imaging to quantitatively map tissue water and lipid content using a regular silicon-based camera. RESULTS The proposed method is validated with a phantom study, which shows average error of 0.9 ± 1.2 % for water content estimation, and -0.4 ± 0.7 % for lipid content estimation, respectively. The proposed method is also demonstrated for ex vivo porcine tissue lipid mapping as well as in-vivo longitudinal water content monitoring. CONCLUSIONS The proposed technique enables spatial mapping of tissue water and lipid content with the cost in detector reduced by two orders of magnitude compared with SWIR-MPI while maintaining high accuracy. The experimental results highlight the potential of this technique for substantial impact in both scientific and industrial applications.
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Affiliation(s)
- Bowen Song
- Beihang University, School of Engineering Medicine, Beijing Advanced Innovation Center for Biomedical Engineering, Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing, China
| | - Xinman Yin
- Beihang University, School of Engineering Medicine, Beijing Advanced Innovation Center for Biomedical Engineering, Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing, China
| | - Yubo Fan
- Beihang University, School of Engineering Medicine, Beijing Advanced Innovation Center for Biomedical Engineering, Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing, China
| | - Yanyu Zhao
- Beihang University, School of Engineering Medicine, Beijing Advanced Innovation Center for Biomedical Engineering, Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing, China
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Wang Y, Kang X, Zhang Y, Shi Z, Ren H, Wang Q, Chen M, Zhang Y. Wavelength and frequency optimization in spatial frequency domain imaging for two-layer tissue. BIOMEDICAL OPTICS EXPRESS 2022; 13:3224-3242. [PMID: 35781948 PMCID: PMC9208585 DOI: 10.1364/boe.455386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 03/19/2022] [Accepted: 04/11/2022] [Indexed: 06/15/2023]
Abstract
Spatial frequency domain imaging is a non-contact, wide-field, fast-diffusion optical imaging technique, which in principle uses steady-state spatially modulated light to irradiate biological tissue, reconstruct two-dimensional or three-dimensional tissue optical characteristic map through optical transmission model, and further quantify the spatial distribution of tissue physiological parameters by multispectral imaging technique. The selection of light source wavelength and light field spatial modulation frequency is directly related to the accuracy of tissue optical properties and tissue physiological parameters extraction. For improvement of the measurement accuracy of optical properties and physiological parameters in the two-layer tissue, a multispectral spatial frequency domain imaging system is built based on liquid crystal tunable filter, and a data mapping table of spatially resolved diffuse reflectance and optical properties of two-layer tissue is established based on scaling Monte Carlo method. Combined with the dispersion effect and window effect of light-tissue interaction, the study applies numerical simulation to optimize the wavelength in the 650-850 nm range with spectral resolution of 10 nm. In order to minimize the uncertainty of the optical properties, Cramér-Rao bound is used to optimize the optical field spatial modulation frequency by transmitting the uncertainty of optical properties. The results showed that in order to realize the detection of melanin, oxyhemoglobin, deoxyhemoglobin, water and other physiological parameters in two-layer tissue, the best wavelength combination was determined as 720, 730, 760 and 810 nm according to the condition number. The findings of the Cramér-Rao bound analysis reveal that the uncertainty of optical characteristics for the frequency combinations [0, 0.3] mm-1, [0, 0.2] mm-1, and [0, 0.1] mm-1 increases successively. Under the optimal combination of wavelength and frequency, the diffuse reflectance of the gradient gray-scale plate measured by the multi-spectral spatial frequency domain imaging system is linearly correlated with the calibration value. The error between the measured liquid phantom absorption coefficient and the collimation projection system based on colorimetric dish is less than 2%. The experimental results of human brachial artery occlusion indicate that under the optimal wavelength combination, the change of the second layer absorption coefficient captured by the three frequency combinations decreases in turn, so as the change of oxygen saturation.
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Affiliation(s)
- Yikun Wang
- Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Anhui Provincial Engineering Technology Research Center for Biomedical Optical Instrument, Anhui Provincial Engineering Laboratory for Medical Optical Diagnosis Treatment Technology and Instrument, Hefei 230031, China
- These authors contributed equally to this work and should be considered co-first authors
| | - Xu Kang
- Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Anhui Provincial Engineering Technology Research Center for Biomedical Optical Instrument, Anhui Provincial Engineering Laboratory for Medical Optical Diagnosis Treatment Technology and Instrument, Hefei 230031, China
- Institutes of Physical Science and Information Technology, Anhui University, Hefei 230601, China
- These authors contributed equally to this work and should be considered co-first authors
| | - Yang Zhang
- Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Anhui Provincial Engineering Technology Research Center for Biomedical Optical Instrument, Anhui Provincial Engineering Laboratory for Medical Optical Diagnosis Treatment Technology and Instrument, Hefei 230031, China
| | - Zhiguo Shi
- Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Anhui Provincial Engineering Technology Research Center for Biomedical Optical Instrument, Anhui Provincial Engineering Laboratory for Medical Optical Diagnosis Treatment Technology and Instrument, Hefei 230031, China
- School of Biomedical Engineering, Anhui Medical University, Hefei 230009, China
| | - Huiming Ren
- Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Anhui Provincial Engineering Technology Research Center for Biomedical Optical Instrument, Anhui Provincial Engineering Laboratory for Medical Optical Diagnosis Treatment Technology and Instrument, Hefei 230031, China
- Institutes of Physical Science and Information Technology, Anhui University, Hefei 230601, China
| | - Quanfu Wang
- Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Anhui Provincial Engineering Technology Research Center for Biomedical Optical Instrument, Anhui Provincial Engineering Laboratory for Medical Optical Diagnosis Treatment Technology and Instrument, Hefei 230031, China
| | - Mingwei Chen
- Department of Endocrinology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China
| | - Yuanzhi Zhang
- Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Anhui Provincial Engineering Technology Research Center for Biomedical Optical Instrument, Anhui Provincial Engineering Laboratory for Medical Optical Diagnosis Treatment Technology and Instrument, Hefei 230031, China
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Pilvar A, Plutzky J, Pierce MC, Roblyer D. Shortwave infrared spatial frequency domain imaging for non-invasive measurement of tissue and blood optical properties. JOURNAL OF BIOMEDICAL OPTICS 2022; 27:JBO-220043GR. [PMID: 35715883 PMCID: PMC9204261 DOI: 10.1117/1.jbo.27.6.066003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 05/26/2022] [Indexed: 05/11/2023]
Abstract
SIGNIFICANCE The shortwave infrared (SWIR) optical window (∼900 to 2000 nm) has attracted interest for deep tissue imaging due to the lower scattering of light. SWIR spatial frequency domain imaging (SWIR SFDI) provides wide-field tissue optical property measurements in this wavelength band. Key design and performance characteristics, such as portability, wavelength selection, measurement resolution, and the effect of skin have not yet been addressed for SWIR SFDI. AIM To fabricate and characterize a SWIR SFDI system for clinical use. APPROACH The optimal choice of wavelengths was identified based on optical property uncertainty estimates and imaging depth. A compact light-emitting diode-based dual wavelength SWIR SFDI system was fabricated. A two-layer inverse model was developed to account for the layered structure of skin. Performance was validated using tissue-simulating phantoms and in-vivo measurements from three healthy subjects. RESULTS The SWIR SFDI system had a μs' resolution of at least 0.03 mm - 1 at 880 nm and 0.02 mm - 1 at 1100 nm. The two-layer inverse model reduced the error in deeper layer μs' extractions by at least 24% in the phantom study. The two-layer model also increased the contrast between superficial vessels and the surrounding tissue for in-vivo measurements. CONCLUSION The clinic-ready SWIR SFDI device is sensitive to small optical property alterations in diffuse media, provides enhanced accuracy in quantifying optical properties in the deeper layers in phantoms, and provided enhanced contrast of subcutaneous blood vessels.
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Affiliation(s)
- Anahita Pilvar
- Boston University, Department of Electrical and Computer Engineering, Boston, Massachusetts, United States
| | - Jorge Plutzky
- Brigham and Women’s Hospital, Harvard Medical School, Department of Medicine, Boston, Massachusetts, United States
| | - Mark C. Pierce
- Rutgers, The State University of New Jersey, Department of Biomedical Engineering, Piscataway, New Jersey, United States
| | - Darren Roblyer
- Boston University, Department of Electrical and Computer Engineering, Boston, Massachusetts, United States
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
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Ultracompact Deep Neural Network for Ultrafast Optical Property Extraction in Spatial Frequency Domain Imaging (SFDI). PHOTONICS 2022. [DOI: 10.3390/photonics9050327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Spatial frequency domain imaging (SFDI) is a powerful, label-free imaging technique capable of the wide-field quantitative mapping of tissue optical properties and, subsequently, chromophore concentrations. While SFDI hardware acquisition methods have advanced towards video-rate, the inverse problem (i.e., the mapping of acquired diffuse reflectance to optical properties) has remained a bottleneck for real-time data processing and visualization. Deep learning methods are adept at fitting nonlinear patterns, and may be ideal for rapidly solving the SFDI inverse problem. While current deep neural networks (DNN) are growing increasingly larger and more complex (e.g., with millions of parameters or more), our study shows that it can also be beneficial to move in the other direction, i.e., make DNNs that are smaller and simpler. Here, we propose an ultracompact, two-layer, fully connected DNN structure (each layer with four and two neurons, respectively) for ultrafast optical property extractions, which is 30×–600× faster than current methods with a similar or improved accuracy, allowing for an inversion time of 5.5 ms for 696 × 520 pixels. We further demonstrated the proposed inverse model in numerical simulations, and comprehensive phantom characterization, as well as offering in vivo measurements of dynamic physiological processes. We further demonstrated that the computation time could achieve another 200× improvement with a GPU device. This deep learning structure will help to enable fast and accurate real-time SFDI measurements, which are crucial for pre-clinical, clinical, and industrial applications.
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Zhao Y, Song B, Wang M, Zhao Y, Fan Y. Halftone spatial frequency domain imaging enables kilohertz high-speed label-free non-contact quantitative mapping of optical properties for strongly turbid media. LIGHT, SCIENCE & APPLICATIONS 2021; 10:245. [PMID: 34887375 PMCID: PMC8660769 DOI: 10.1038/s41377-021-00681-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 10/28/2021] [Accepted: 11/23/2021] [Indexed: 05/05/2023]
Abstract
The ability to quantify optical properties (i.e., absorption and scattering) of strongly turbid media has major implications on the characterization of biological tissues, fluid fields, and many others. However, there are few methods that can provide wide-field quantification of optical properties, and none is able to perform quantitative optical property imaging with high-speed (e.g., kilohertz) capabilities. Here we develop a new imaging modality termed halftone spatial frequency domain imaging (halftone-SFDI), which is approximately two orders of magnitude faster than the state-of-the-art, and provides kilohertz high-speed, label-free, non-contact, wide-field quantification for the optical properties of strongly turbid media. This method utilizes halftone binary patterned illumination to target the spatial frequency response of turbid media, which is then mapped to optical properties using model-based analysis. We validate the halftone-SFDI on an array of phantoms with a wide range of optical properties as well as in vivo human tissue. We demonstrate with an in vivo rat brain cortex imaging study, and show that halftone-SFDI can longitudinally monitor the absolute concentration as well as spatial distribution of functional chromophores in tissue. We also show that halftone-SFDI can spatially map dual-wavelength optical properties of a highly dynamic flow field at kilohertz speed. Together, these results highlight the potential of halftone-SFDI to enable new capabilities in fundamental research and translational studies including brain science and fluid dynamics.
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Affiliation(s)
- Yanyu Zhao
- Beijing Advanced Innovation Center for Biomedical Engineering, Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, School of Engineering Medicine, and with the School of Biological Science and Medical Engineering, Beihang University, 100191, Beijing, China.
| | - Bowen Song
- Beijing Advanced Innovation Center for Biomedical Engineering, Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, School of Engineering Medicine, and with the School of Biological Science and Medical Engineering, Beihang University, 100191, Beijing, China
| | - Ming Wang
- Institute of Spacecraft Application System Engineering, China Academy of Space Technology, 100094, Beijing, China
| | - Yang Zhao
- Beijing Institute of Spacecraft Engineering, 100094, Beijing, China
| | - Yubo Fan
- Beijing Advanced Innovation Center for Biomedical Engineering, Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, School of Engineering Medicine, and with the School of Biological Science and Medical Engineering, Beihang University, 100191, Beijing, China.
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Dai L, Luo Y, Fu X. Simple demodulation method for optical property extraction in spatial frequency domain imaging. APPLIED OPTICS 2021; 60:7878-7887. [PMID: 34613046 DOI: 10.1364/ao.430937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 08/03/2021] [Indexed: 06/13/2023]
Abstract
Different demodulation methods affect the efficiency and accuracy of spatial frequency domain imaging (SFDI). A simple and effective method of sum-to-product identities (STPI) demodulation was proposed in this study. STPI requires one fewer image than conventional three-phase demodulation (TPD) at a spatial frequency. Numerical simulation and phantom experiments were performed. The result proved the feasibility of STPI and showed that STPI combined with subtraction can achieve high-precision demodulation in the low spatial frequency domain. Through extraction of phantom optical properties, STPI had similar accuracy compared with other demodulation methods in extracting optical properties in phantoms. STPI was also used to extract the optical properties of milk, and it had highly consistent results with TPD, which can distinguish milk with different fat content. The demodulation effect of this method in the low spatial frequencies is better than other fast demodulation methods.
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Pardo A, Streeter SS, Maloney BW, Gutierrez-Gutierrez JA, McClatchy DM, Wells WA, Paulsen KD, Lopez-Higuera JM, Pogue BW, Conde OM. Modeling and Synthesis of Breast Cancer Optical Property Signatures With Generative Models. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:1687-1701. [PMID: 33684035 PMCID: PMC8224479 DOI: 10.1109/tmi.2021.3064464] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Is it possible to find deterministic relationships between optical measurements and pathophysiology in an unsupervised manner and based on data alone? Optical property quantification is a rapidly growing biomedical imaging technique for characterizing biological tissues that shows promise in a range of clinical applications, such as intraoperative breast-conserving surgery margin assessment. However, translating tissue optical properties to clinical pathology information is still a cumbersome problem due to, amongst other things, inter- and intrapatient variability, calibration, and ultimately the nonlinear behavior of light in turbid media. These challenges limit the ability of standard statistical methods to generate a simple model of pathology, requiring more advanced algorithms. We present a data-driven, nonlinear model of breast cancer pathology for real-time margin assessment of resected samples using optical properties derived from spatial frequency domain imaging data. A series of deep neural network models are employed to obtain sets of latent embeddings that relate optical data signatures to the underlying tissue pathology in a tractable manner. These self-explanatory models can translate absorption and scattering properties measured from pathology, while also being able to synthesize new data. The method was tested on a total of 70 resected breast tissue samples containing 137 regions of interest, achieving rapid optical property modeling with errors only limited by current semi-empirical models, allowing for mass sample synthesis and providing a systematic understanding of dataset properties, paving the way for deep automated margin assessment algorithms using structured light imaging or, in principle, any other optical imaging technique seeking modeling. Code is available.
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Robbins CM, Tabassum S, Baumhauer MF, Yang J, Antaki JF, Kainerstorfer JM. Two-layer spatial frequency domain imaging of compression-induced hemodynamic changes in breast tissue. JOURNAL OF BIOMEDICAL OPTICS 2021; 26:056005. [PMCID: PMC8145994 DOI: 10.1117/1.jbo.26.5.056005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 05/04/2021] [Indexed: 06/15/2023]
Abstract
Significance: Longitudinal tracking of hemodynamic changes in the breast has shown potential for neoadjuvant chemotherapy (NAC) outcome prediction. Spatial frequency domain imaging (SFDI) could be suitable for frequent monitoring of shallow breast tumors, but strong sensitivity to superficial absorbers presents a challenge. Aim: We investigated the efficacy of a two-layer SFDI inverse model that accounts for varying melanin concentration in the skin to improve discrimination of optical properties of deep tissue of the breast. Approach: Hemodynamic changes in response to localized breast compression were measured in 13 healthy volunteers using a handheld SFDI device. Epidermis optical thickness was determined based on spectral fitting of the model output and used to calculate subcutaneous optical properties. Results: Optical properties from a homogeneous model yielded physiologically unreasonable absorption and scattering coefficients for highly pigmented volunteers. The two-layer model compensated for the effect of melanin and yielded properties in the expected range for healthy breast. Extracted epidermal optical thickness was higher for higher Fitzpatrick types. Compression induced a decrease in total hemoglobin consistent with tissue blanching. Conclusions: The handheld SFDI device and two-layer model show potential for imaging hemodynamic responses that potentially could help predict efficacy of NAC in patients of varying skin tones.
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Affiliation(s)
- Constance M. Robbins
- Carnegie Mellon University, Department of Biomedical Engineering, Pittsburgh, Pennsylvania, United States
| | - Syeda Tabassum
- Carnegie Mellon University, Department of Biomedical Engineering, Pittsburgh, Pennsylvania, United States
| | - Molly F. Baumhauer
- Carnegie Mellon University, Department of Physics, Pittsburgh, Pennsylvania, United States
| | - Jason Yang
- Carnegie Mellon University, Department of Biomedical Engineering, Pittsburgh, Pennsylvania, United States
| | - James F. Antaki
- Cornell University, School of Biomedical Engineering, Ithaca, New York, United States
| | - Jana M. Kainerstorfer
- Carnegie Mellon University, Department of Biomedical Engineering, Pittsburgh, Pennsylvania, United States
- Carnegie Mellon University, Neuroscience Institute, Pittsburgh, Pennsylvania, United States
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Chen MT, Papadakis M, Durr NJ. Speckle illumination SFDI for projector-free optical property mapping. OPTICS LETTERS 2021; 46:673-676. [PMID: 33528438 PMCID: PMC8285059 DOI: 10.1364/ol.411187] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Accepted: 12/27/2020] [Indexed: 05/08/2023]
Abstract
Spatial frequency domain imaging can map tissue scattering and absorption properties over a wide field of view, making it useful for clinical applications such as wound assessment and surgical guidance. This technique has previously required the projection of fully characterized illumination patterns. Here, we show that random and unknown speckle illumination can be used to sample the modulation transfer function of tissues at known spatial frequencies, allowing the quantitative mapping of optical properties with simple laser diode illumination. We compute low- and high-spatial frequency response parameters from the local power spectral density for each pixel and use a lookup table to accurately estimate absorption and scattering coefficients in tissue phantoms, in vivo human hand, and ex vivo swine esophagus. Because speckle patterns can be generated over a large depth of field and field of view with simple coherent illumination, this approach may enable optical property mapping in new form-factors and applications, including endoscopy.
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Affiliation(s)
- Mason T. Chen
- Department of Biomedical Engineering, Johns Hopkins University, 3400 N. Charles Street, Baltimore, Maryland 21218, USA
| | - Melina Papadakis
- Department of Biomedical Engineering, Johns Hopkins University, 3400 N. Charles Street, Baltimore, Maryland 21218, USA
| | - Nicholas J. Durr
- Department of Biomedical Engineering, Johns Hopkins University, 3400 N. Charles Street, Baltimore, Maryland 21218, USA
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14
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Zhao Y, Deng Y, Yue S, Wang M, Song B, Fan Y. Direct mapping from diffuse reflectance to chromophore concentrations in multi- fx spatial frequency domain imaging (SFDI) with a deep residual network (DRN). BIOMEDICAL OPTICS EXPRESS 2021; 12:433-443. [PMID: 33659081 PMCID: PMC7899520 DOI: 10.1364/boe.409654] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 11/11/2020] [Accepted: 11/13/2020] [Indexed: 05/18/2023]
Abstract
Spatial frequency domain imaging (SFDI) is an emerging technology that enables label-free, non-contact, and wide-field mapping of tissue chromophore contents, such as oxy- and deoxy-hemoglobin concentrations. It has been shown that the use of more than two spatial frequencies (multi-fx ) can vastly improve measurement accuracy and reduce chromophore estimation uncertainties, but real-time multi-fx SFDI for chromophore monitoring has been limited in practice due to the slow speed of available chromophore inversion algorithms. Existing inversion algorithms have to first convert the multi-fx diffuse reflectance to optical absorptions, and then solve a set of linear equations to estimate chromophore concentrations. In this work, we present a deep learning framework, noted as a deep residual network (DRN), that is able to directly map from diffuse reflectance to chromophore concentrations. The proposed DRN is over 10x faster than the state-of-the-art method for chromophore inversion and enables 25x improvement on the frame rate for in vivo real-time oxygenation mapping. The proposed deep learning model will help enable real-time and highly accurate chromophore monitoring with multi-fx SFDI.
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Affiliation(s)
- Yanyu Zhao
- Beijing Advanced Innovation Center for Biomedical Engineering, Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Yue Deng
- School of Astronautics, Beihang University, Beijing 100191, China
| | - Shuhua Yue
- Beijing Advanced Innovation Center for Biomedical Engineering, Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Ming Wang
- Institute of Spacecraft Application System Engineering, China Academy of Space Technology, Beijing, 100094, China
| | - Bowen Song
- Beijing Advanced Innovation Center for Biomedical Engineering, Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Yubo Fan
- Beijing Advanced Innovation Center for Biomedical Engineering, Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
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15
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Zhao Y, Pilvar A, Tank A, Peterson H, Jiang J, Aster JC, Dumas JP, Pierce MC, Roblyer D. Shortwave-infrared meso-patterned imaging enables label-free mapping of tissue water and lipid content. Nat Commun 2020; 11:5355. [PMID: 33097705 PMCID: PMC7585425 DOI: 10.1038/s41467-020-19128-7] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Accepted: 09/29/2020] [Indexed: 12/11/2022] Open
Abstract
Water and lipids are key participants in many biological processes, but there are few non-invasive methods that provide quantification of these components in vivo, and none that can isolate and quantify lipids in the blood. Here we develop a new imaging modality termed shortwave infrared meso-patterned imaging (SWIR-MPI) to provide label-free, non-contact, spatial mapping of water and lipid concentrations in tissue. The method utilizes patterned hyperspectral illumination to target chromophore absorption bands in the 900-1,300 nm wavelength range. We use SWIR-MPI to monitor clinically important physiological processes including edema, inflammation, and tumor lipid heterogeneity in preclinical models. We also show that SWIR-MPI can spatially map blood-lipids in humans, representing an example of non-invasive and contact-free measurements of in vivo blood lipids. Together, these results highlight the potential of SWIR-MPI to enable new capabilities in fundamental studies and clinical monitoring of major conditions including obesity, cancer, and cardiovascular disease.
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Affiliation(s)
- Yanyu Zhao
- Department of Biomedical Engineering, Boston University, 44 Cummington Mall, Boston, MA, 02215, USA.,Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Anahita Pilvar
- Department of Biomedical Engineering, Boston University, 44 Cummington Mall, Boston, MA, 02215, USA
| | - Anup Tank
- Department of Biomedical Engineering, Boston University, 44 Cummington Mall, Boston, MA, 02215, USA
| | - Hannah Peterson
- Department of Biomedical Engineering, Boston University, 44 Cummington Mall, Boston, MA, 02215, USA
| | - John Jiang
- Department of Biomedical Engineering, Boston University, 44 Cummington Mall, Boston, MA, 02215, USA
| | - Jon C Aster
- Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA
| | - John Paul Dumas
- Department of Biomedical Engineering, Rutgers, The State University of New Jersey, 599 Taylor Road, Piscataway, NJ, 08854, USA
| | - Mark C Pierce
- Department of Biomedical Engineering, Rutgers, The State University of New Jersey, 599 Taylor Road, Piscataway, NJ, 08854, USA
| | - Darren Roblyer
- Department of Biomedical Engineering, Boston University, 44 Cummington Mall, Boston, MA, 02215, USA.
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16
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Applegate MB, Karrobi K, Angelo Jr. JP, Austin W, Tabassum SM, Aguénounon E, Tilbury K, Saager RB, Gioux S, Roblyer D. OpenSFDI: an open-source guide for constructing a spatial frequency domain imaging system. JOURNAL OF BIOMEDICAL OPTICS 2020; 25:1-13. [PMID: 31925946 PMCID: PMC7008504 DOI: 10.1117/1.jbo.25.1.016002] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Accepted: 12/12/2019] [Indexed: 05/09/2023]
Abstract
Significance: Spatial frequency domain imaging (SFDI) is a diffuse optical measurement technique that can quantify tissue optical absorption (μa) and reduced scattering (<inline-formula>μs'</inline-formula>) on a pixel-by-pixel basis. Measurements of μa at different wavelengths enable the extraction of molar concentrations of tissue chromophores over a wide field, providing a noncontact and label-free means to assess tissue viability, oxygenation, microarchitecture, and molecular content. We present here openSFDI: an open-source guide for building a low-cost, small-footprint, three-wavelength SFDI system capable of quantifying μa and <inline-formula>μs'</inline-formula> as well as oxyhemoglobin and deoxyhemoglobin concentrations in biological tissue. The companion website provides a complete parts list along with detailed instructions for assembling the openSFDI system.<p> Aim: We describe the design of openSFDI and report on the accuracy and precision of optical property extractions for three different systems fabricated according to the instructions on the openSFDI website.</p> <p> Approach: Accuracy was assessed by measuring nine tissue-simulating optical phantoms with a physiologically relevant range of μa and <inline-formula>μs'</inline-formula> with the openSFDI systems and a commercial SFDI device. Precision was assessed by repeatedly measuring the same phantom over 1 h.</p> <p> Results: The openSFDI systems had an error of 0 ± 6 % in μa and -2 ± 3 % in <inline-formula>μs'</inline-formula>, compared to a commercial SFDI system. Bland-Altman analysis revealed the limits of agreement between the two systems to be ± 0.004 mm - 1 for μa and -0.06 to 0.1 mm - 1 for <inline-formula>μs'</inline-formula>. The openSFDI system had low drift with an average standard deviation of 0.0007 mm - 1 and 0.05 mm - 1 in μa and <inline-formula>μs'</inline-formula>, respectively.</p>,<p> Conclusion: The openSFDI provides a customizable hardware platform for research groups seeking to utilize SFDI for quantitative diffuse optical imaging.</p>
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Affiliation(s)
- Matthew B. Applegate
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Kavon Karrobi
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | | | - Wyatt Austin
- University of Maine, Department of Chemical and Biomedical Engineering, Orono, Maine, United States
| | - Syeda M. Tabassum
- Boston University, Department of Electrical and Computer Engineering, Boston, Massachusetts, United States
| | | | - Karissa Tilbury
- University of Maine, Department of Chemical and Biomedical Engineering, Orono, Maine, United States
| | - Rolf B. Saager
- Linköping University, Department of Biomedical Engineering, Linköping Sweden
| | - Sylvain Gioux
- University of Strasbourg, ICube Laboratory, Strasbourg, France
| | - Darren Roblyer
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
- Address all correspondence to Darren Roblyer, E-mail:
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17
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Hayakawa CK, Karrobi K, Pera V, Roblyer D, Venugopalan V. Optical sampling depth in the spatial frequency domain. JOURNAL OF BIOMEDICAL OPTICS 2019; 24:JBO-180100R. [PMID: 30218504 PMCID: PMC6675966 DOI: 10.1117/1.jbo.24.7.071603] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2018] [Accepted: 07/10/2018] [Indexed: 05/11/2023]
Abstract
We present a Monte Carlo (MC) method to determine depth-dependent probability distributions of photon visitation and detection for optical reflectance measurements performed in the spatial frequency domain (SFD). These distributions are formed using an MC simulation for radiative transport that utilizes a photon packet weighting procedure consistent with the two-dimensional spatial Fourier transform of the radiative transport equation. This method enables the development of quantitative metrics for SFD optical sampling depth in layered tissue and its dependence on both tissue optical properties and spatial frequency. We validate the computed depth-dependent probability distributions using SFD measurements in a layered phantom system with a highly scattering top layer of variable thickness supported by a highly absorbing base layer. We utilize our method to establish the spatial frequency-dependent optical sampling depth for a number of tissue types and also provide a general tool to determine such depths for tissues of arbitrary optical properties.
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Affiliation(s)
- Carole K. Hayakawa
- University of California at Irvine, Department of Chemical Engineering and Materials Science, Irvine, California, United States
- University of California at Irvine, Beckman Laser Institute, Laser Microbeam and Medical Program, Irvine, California, United States
| | - Kavon Karrobi
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Vivian Pera
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Darren Roblyer
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Vasan Venugopalan
- University of California at Irvine, Department of Chemical Engineering and Materials Science, Irvine, California, United States
- University of California at Irvine, Beckman Laser Institute, Laser Microbeam and Medical Program, Irvine, California, United States
- Address all correspondence to: Vasan Venugopalan, E-mail:
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18
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Karrobi K, Tank A, Tabassum S, Pera V, Roblyer D. Diffuse and nonlinear imaging of multiscale vascular parameters for in vivo monitoring of preclinical mammary tumors. JOURNAL OF BIOPHOTONICS 2019; 12:e201800379. [PMID: 30706695 DOI: 10.1002/jbio.201800379] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Revised: 01/25/2019] [Accepted: 01/29/2019] [Indexed: 06/09/2023]
Abstract
Diffuse optical imaging (DOI) techniques provide a wide-field or macro assessment of the functional tumor state and have shown substantial promise for monitoring treatment efficacy in cancer. Conversely, intravital microscopy provides a high-resolution view of the tumor state and has played a key role in characterizing treatment response in the preclinical setting. There has been little prior work in investigating how the macro and micro spatial scales can be combined to develop a more comprehensive and translational view of treatment response. To address this, a new multiscale preclinical imaging technique called diffuse and nonlinear imaging (DNI) was developed. DNI combines multiphoton microscopy with spatial frequency domain imaging (SFDI) to provide multiscale data sets of tumor microvascular architecture coregistered within wide-field hemodynamic maps. A novel method was developed to match the imaging depths of both modalities by utilizing informed SFDI spatial frequency selection. An in vivo DNI study of murine mammary tumors revealed multiscale relationships between tumor oxygen saturation and microvessel diameter, and tumor oxygen saturation and microvessel length (|Pearson's ρ| ≥ 0.5, P < 0.05). Going forward, DNI will be uniquely enabling for the investigation of multiscale relationships in tumors during treatment.
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Affiliation(s)
- Kavon Karrobi
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Anup Tank
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Syeda Tabassum
- Department of Electrical and Computer Engineering, Boston University, Boston, Massachusetts
| | - Vivian Pera
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Darren Roblyer
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
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19
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Gioux S, Mazhar A, Cuccia DJ. Spatial frequency domain imaging in 2019: principles, applications, and perspectives. JOURNAL OF BIOMEDICAL OPTICS 2019; 24:1-18. [PMID: 31222987 PMCID: PMC6995958 DOI: 10.1117/1.jbo.24.7.071613] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Accepted: 05/09/2019] [Indexed: 05/20/2023]
Abstract
Spatial frequency domain imaging (SFDI) has witnessed very rapid growth over the last decade, owing to its unique capabilities for imaging optical properties and chromophores over a large field-of-view and in a rapid manner. We provide a comprehensive review of the principles of this imaging method as of 2019, review the modeling of light propagation in this domain, describe acquisition methods, provide an understanding of the various implementations and their practical limitations, and finally review applications that have been published in the literature. Importantly, we also introduce a group effort by several key actors in the field for the dissemination of SFDI, including publications, advice in hardware and implementations, and processing code, all freely available online.
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Affiliation(s)
- Sylvain Gioux
- University of Strasbourg, ICube Laboratory, Strasbourg, France
- Address all correspondence to Sylvain Gioux, E-mail:
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20
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Gioux S, Mazhar A, Cuccia DJ. Spatial frequency domain imaging in 2019: principles, applications, and perspectives. JOURNAL OF BIOMEDICAL OPTICS 2019. [PMID: 31222987 DOI: 10.1117/1] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
Spatial frequency domain imaging (SFDI) has witnessed very rapid growth over the last decade, owing to its unique capabilities for imaging optical properties and chromophores over a large field-of-view and in a rapid manner. We provide a comprehensive review of the principles of this imaging method as of 2019, review the modeling of light propagation in this domain, describe acquisition methods, provide an understanding of the various implementations and their practical limitations, and finally review applications that have been published in the literature. Importantly, we also introduce a group effort by several key actors in the field for the dissemination of SFDI, including publications, advice in hardware and implementations, and processing code, all freely available online.
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Affiliation(s)
- Sylvain Gioux
- University of Strasbourg, ICube Laboratory, Strasbourg, France
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21
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Abstract
Despite our understanding that the microvasculature plays a multifaceted role in the development and progression of various conditions, we know little about the extent of this involvement. A need exists for non-invasive, clinically meaningful imaging modalities capable of elucidating microvascular information to aid in our understanding of disease, and to aid in the diagnosis/monitoring of disease for more patient-specific care. In this review article, a number of imaging techniques are summarized that have been utilized to investigate the microvasculature of skin, along with their advantages, disadvantages and future perspectives in preclinical and clinical settings. These techniques include dermoscopy, capillaroscopy, Doppler sonography, laser Doppler flowmetry (LDF) and perfusion imaging, laser speckle contrast imaging (LSCI), optical coherence tomography (OCT), including its Doppler and dynamic variant and the more recently developed OCT angiography (OCTA), photoacoustic imaging, and spatial frequency domain imaging (SFDI). Attention is largely, but not exclusively, placed on optical imaging modalities that use intrinsic optical signals to contrast the microvasculature. We conclude that whilst each imaging modality has been successful in filling a particular niche, there is no one, all-encompassing modality without inherent flaws. Therefore, the future of cutaneous microvascular imaging may lie in utilizing a multi-modal approach that will counter the disadvantages of individual systems to synergistically augment our imaging capabilities.
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Affiliation(s)
- Anthony J Deegan
- Department of Bioengineering, University of Washington, 3720 15th Ave. NE., Seattle, WA 98195, United States of America
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22
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Zhao Y, Applegate MB, Istfan R, Pande A, Roblyer D. Quantitative real-time pulse oximetry with ultrafast frequency-domain diffuse optics and deep neural network processing. BIOMEDICAL OPTICS EXPRESS 2018; 9:5997-6008. [PMID: 31065408 PMCID: PMC6491012 DOI: 10.1364/boe.9.005997] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2018] [Revised: 10/23/2018] [Accepted: 10/24/2018] [Indexed: 05/02/2023]
Abstract
Pulse oximetry is a ubiquitous optical technology, widely used for diagnosis and treatment guidance. Current pulse oximeters provide indications of arterial oxygen saturation. We present here a new quantitative methodology that extends the capability of pulse oximetry and provides real-time molar concentrations of oxy- and deoxy-hemoglobin at rates of up to 27 Hz by using advanced digital hardware, real-time firmware processing, and ultra-fast optical property calculations with a deep neural network (DNN). The technique utilizes a high-speed frequency domain spectroscopy system with five frequency-multiplexed wavelengths. High-speed demultiplexing and data reduction were performed in firmware. The DNN inversion algorithm was benchmarked as five orders of magnitude faster than conventional iterative methods for optical property extractions. The DNN provided unbiased optical property extractions, with an average error of 0 ± 5.6% in absorption and 0 ± 1.4% in reduced scattering. Together, these improvements enabled the measurement, calculation, and real-time continuous display of hemoglobin concentrations. A proof-of-concept cuff occlusion measurement was performed to demonstrate the ability of the device to track oxy- and deoxy-hemoglobin, and measure quantitative photoplethysmographic changes during the cardiac cycle. This technique substantially extends the capability of pulse oximetry and provides unprecedented real-time non-invasive functional information with broad applicability for cardiopulmonary applications.
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Affiliation(s)
- Yanyu Zhao
- Boston University, Department of Biomedical Engineering, 44 Cummington Mall, Boston, MA 02215, USA
| | - Mattew B. Applegate
- Boston University, Department of Biomedical Engineering, 44 Cummington Mall, Boston, MA 02215, USA
| | - Raeef Istfan
- Boston University, Department of Biomedical Engineering, 44 Cummington Mall, Boston, MA 02215, USA
| | - Ashvin Pande
- Boston University School of Medicine, Section of Cardiovascular Medicine, Boston, MA 02118, USA
| | - Darren Roblyer
- Boston University, Department of Biomedical Engineering, 44 Cummington Mall, Boston, MA 02215, USA
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23
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Zhao Y, Deng Y, Bao F, Peterson H, Istfan R, Roblyer D. Deep learning model for ultrafast multifrequency optical property extractions for spatial frequency domain imaging. OPTICS LETTERS 2018; 43:5669-5672. [PMID: 30439924 DOI: 10.1364/ol.43.005669] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Spatial frequency domain imaging (SFDI) is emerging as an important new method in biomedical imaging due to its ability to provide label-free, wide-field tissue optical property maps. Most prior SFDI studies have utilized two spatial frequencies (2-fx) for optical property extractions. The use of more than two frequencies (multi-fx) can vastly improve the accuracy and reduce uncertainties in optical property estimates for some tissue types, but it has been limited in practice due to the slow speed of available inversion algorithms. We present a deep learning solution that eliminates this bottleneck by solving the multi-fx inverse problem 300× to 100,000× faster, with equivalent or improved accuracy compared to competing methods. The proposed deep learning inverse model will help to enable real-time and highly accurate tissue measurements with SFDI.
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24
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Angelo JP, Chen SJ, Ochoa M, Sunar U, Gioux S, Intes X. Review of structured light in diffuse optical imaging. JOURNAL OF BIOMEDICAL OPTICS 2018; 24:1-20. [PMID: 30218503 PMCID: PMC6676045 DOI: 10.1117/1.jbo.24.7.071602] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2018] [Accepted: 05/31/2018] [Indexed: 05/11/2023]
Abstract
Diffuse optical imaging probes deep living tissue enabling structural, functional, metabolic, and molecular imaging. Recently, due to the availability of spatial light modulators, wide-field quantitative diffuse optical techniques have been implemented, which benefit greatly from structured light methodologies. Such implementations facilitate the quantification and characterization of depth-resolved optical and physiological properties of thick and deep tissue at fast acquisition speeds. We summarize the current state of work and applications in the three main techniques leveraging structured light: spatial frequency-domain imaging, optical tomography, and single-pixel imaging. The theory, measurement, and analysis of spatial frequency-domain imaging are described. Then, advanced theories, processing, and imaging systems are summarized. Preclinical and clinical applications on physiological measurements for guidance and diagnosis are summarized. General theory and method development of tomographic approaches as well as applications including fluorescence molecular tomography are introduced. Lastly, recent developments of single-pixel imaging methodologies and applications are reviewed.
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Affiliation(s)
- Joseph P. Angelo
- National Institute of Standards and Technology, Sensor Science Division, Gaithersburg, Maryland, United States
- Address all correspondence to: Joseph P. Angelo, E-mail: ; Sez-Jade Chen, E-mail:
| | - Sez-Jade Chen
- Rensselaer Polytechnic Institute, Department of Biomedical Engineering, Troy, New York, United States
- Address all correspondence to: Joseph P. Angelo, E-mail: ; Sez-Jade Chen, E-mail:
| | - Marien Ochoa
- Rensselaer Polytechnic Institute, Department of Biomedical Engineering, Troy, New York, United States
| | - Ulas Sunar
- Wright State University, Department of Biomedical Industrial and Human Factor Engineering, Dayton, Ohio, United States
| | - Sylvain Gioux
- University of Strasbourg, ICube Laboratory, Strasbourg, France
| | - Xavier Intes
- Rensselaer Polytechnic Institute, Department of Biomedical Engineering, Troy, New York, United States
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