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CHINTADA BHASKARARAO, RUIZ-LOPERA SEBASTIÁN, RESTREPO RENÉ, BOUMA BRETTE, VILLIGER MARTIN, URIBE-PATARROYO NÉSTOR. Probabilistic volumetric speckle suppression in OCT using deep learning. ARXIV 2023:arXiv:2312.04460v1. [PMID: 38106457 PMCID: PMC10723542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
We present a deep learning framework for volumetric speckle reduction in optical coherence tomography (OCT) based on a conditional generative adversarial network (cGAN) that leverages the volumetric nature of OCT data. In order to utilize the volumetric nature of OCT data, our network takes partial OCT volumes as input, resulting in artifact-free despeckled volumes that exhibit excellent speckle reduction and resolution preservation in all three dimensions. Furthermore, we address the ongoing challenge of generating ground truth data for supervised speckle suppression deep learning frameworks by using volumetric non-local means despeckling-TNode to generate training data. We show that, while TNode processing is computationally demanding, it serves as a convenient, accessible gold-standard source for training data; our cGAN replicates efficient suppression of speckle while preserving tissue structures with dimensions approaching the system resolution of non-local means despeckling while being two orders of magnitude faster than TNode. We demonstrate fast, effective, and high-quality despeckling of the proposed network in different tissue types acquired with three different OCT systems compared to existing deep learning methods. The open-source nature of our work facilitates re-training and deployment in any OCT system with an all-software implementation, working around the challenge of generating high-quality, speckle-free training data.
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Affiliation(s)
- BHASKARA RAO CHINTADA
- Wellman Center for Photomedicine, Massachusetts General Hospital, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - SEBASTIÁN RUIZ-LOPERA
- Wellman Center for Photomedicine, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - RENÉ RESTREPO
- Applied Optics Group, Universidad EAFIT, Carrera 49 # 7 Sur-50, Medellín, Colombia
| | - BRETT E. BOUMA
- Wellman Center for Photomedicine, Massachusetts General Hospital, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
- Institute of Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - MARTIN VILLIGER
- Wellman Center for Photomedicine, Massachusetts General Hospital, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - NÉSTOR URIBE-PATARROYO
- Wellman Center for Photomedicine, Massachusetts General Hospital, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
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Chang S, Giannico GA, Haugen E, Jardaneh A, Baba J, Mahadevan-Jansen A, Chang SS, Bowden AK. Multiparameter interferometric polarization-enhanced imaging differentiates carcinoma in situ from inflammation of the bladder: an ex vivo study. JOURNAL OF BIOMEDICAL OPTICS 2023; 28:102907. [PMID: 37576611 PMCID: PMC10415042 DOI: 10.1117/1.jbo.28.10.102907] [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: 05/04/2023] [Revised: 07/28/2023] [Accepted: 07/31/2023] [Indexed: 08/15/2023]
Abstract
Significance Successful differentiation of carcinoma in situ (CIS) from inflammation in the bladder is key to preventing unnecessary biopsies and enabling accurate therapeutic decisions. Current standard-of-care diagnostic imaging techniques lack the specificity needed to differentiate these states, leading to false positives. Aim We introduce multiparameter interferometric polarization-enhanced (MultiPIPE) imaging as a promising technology to improve the specificity of detection for better biopsy guidance and clinical outcomes. Approach In this ex vivo study, we extract tissue attenuation-coefficient-based and birefringence-based parameters from MultiPIPE imaging data, collected with a bench-top system, to develop a classifier for the differentiation of benign and CIS tissues. We also analyze morphological features from second harmonic generation imaging and histology slides and perform imaging-to-morphology correlation analysis. Results MultiPIPE enhances specificity to differentiate CIS from benign tissues by nearly 20% and reduces the false-positive rate by more than four-fold over clinical standards. We also show that the MultiPIPE measurements correlate well with changes in morphological features in histological assessments. Conclusions The results of our study show the promise of MultiPIPE imaging to be used for better differentiation of bladder inflammation from flat tumors, leading to a fewer number of unnecessary procedures and shorter operating room (OR) time.
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Affiliation(s)
- Shuang Chang
- Vannderbilt University, Vanderbilt Biophotonics Center, Department of Biomedical Engineering, Nashville, Tennessee, United States
| | - Giovanna A. Giannico
- Vanderbilt University Medical Center, Department of Pathology, Microbiology, and Immunology, Nashville, Tennessee, United States
| | - Ezekiel Haugen
- Vannderbilt University, Vanderbilt Biophotonics Center, Department of Biomedical Engineering, Nashville, Tennessee, United States
| | - Ali Jardaneh
- Vanderbilt University Medical Center, Department of Urology, Nashville, Tennessee, United States
| | - Justin Baba
- Vannderbilt University, Vanderbilt Biophotonics Center, Department of Biomedical Engineering, Nashville, Tennessee, United States
| | - Anita Mahadevan-Jansen
- Vannderbilt University, Vanderbilt Biophotonics Center, Department of Biomedical Engineering, Nashville, Tennessee, United States
| | - Sam S. Chang
- Vanderbilt University Medical Center, Department of Urology, Nashville, Tennessee, United States
| | - Audrey K. Bowden
- Vannderbilt University, Vanderbilt Biophotonics Center, Department of Biomedical Engineering, Nashville, Tennessee, United States
- Vanderbilt University, Department of Electrical and Computer Engineering, Nashville, Tennessee, United States
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Wang Y, Wei S, Kang JU. Depth-dependent attenuation and backscattering characterization of optical coherence tomography by stationary iterative method. JOURNAL OF BIOMEDICAL OPTICS 2023; 28:085002. [PMID: 37638109 PMCID: PMC10449262 DOI: 10.1117/1.jbo.28.8.085002] [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: 03/14/2023] [Revised: 08/10/2023] [Accepted: 08/11/2023] [Indexed: 08/29/2023]
Abstract
Significance Extracting optical properties of tissue [e.g., the attenuation coefficient (μ ) and the backscattering fraction] from the optical coherence tomography (OCT) images is a valuable tool for parametric imaging and related diagnostic applications. Previous attenuation estimation models depend on the assumption of the uniformity of the backscattering fraction (R ) within layers or whole samples, which does not accurately represent real-world conditions. Aim Our aim is to develop a robust and accurate model that calculates depth-wise values of attenuation and backscattering fractions simultaneously from OCT signals. Furthermore, we aim to develop an attenuation compensation model for OCT images that utilizes the optical properties we obtained to improve the visual representation of tissues. Approach Using the stationary iteration method under suitable constraint conditions, we derived the approximated solutions of μ and R on a single scattering model. During the iteration, the estimated value of μ can be rectified by introducing the large variations of R , whereas the small ones were automatically ignored. Based on the calculation of the structure information, the OCT intensity with attenuation compensation was deduced and compared with the original OCT profiles. Results The preliminary validation was performed in the OCT A-line simulation and Monte Carlo modeling, and the subsequent experiment was conducted on multi-layer silicone-dye-TiO 2 phantoms and ex vivo cow eyes. Our method achieved robust and precise estimation of μ and R for both simulated and experimental data. Moreover, corresponding OCT images with attenuation compensation provided an improved resolution over the entire imaging range. Conclusions Our proposed method was able to correct the estimation bias induced by the variations of R and provided accurate depth-resolved measurements of both μ and R simultaneously. The method does not require prior knowledge of the morphological information of tissue and represents more real-life tissues. Thus, it has the potential to help OCT imaging based disease diagnosis of complex and multi-layer biological tissue.
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Affiliation(s)
- Yaning Wang
- Johns Hopkins University, Department of Electrical and Computer Engineering, Baltimore, Maryland, United States
| | - Shuwen Wei
- Johns Hopkins University, Department of Electrical and Computer Engineering, Baltimore, Maryland, United States
| | - Jin U. Kang
- Johns Hopkins University, Department of Electrical and Computer Engineering, Baltimore, Maryland, United States
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Zheng S, Shuyan W, Yingsa H, Meichen S. QOCT-Net: A Physics-Informed Neural Network for Intravascular Optical Coherence Tomography Attenuation Imaging. IEEE J Biomed Health Inform 2023; 27:3958-3969. [PMID: 37192030 DOI: 10.1109/jbhi.2023.3276422] [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: 05/18/2023]
Abstract
Intravascular optical coherence tomography (IVOCT) provides high-resolution, depth-resolved images of coronary arterial microstructure by acquiring backscattered light. Quantitative attenuation imaging is important for accurate characterization of tissue components and identification of vulnerable plaques. In this work, we proposed a deep learning method for IVOCT attenuation imaging based on the multiple scattering model of light transport. A physics-informed deep network named Quantitative OCT Network (QOCT-Net) was designed to recover pixel-level optical attenuation coefficients directly from standard IVOCT B-scan images. The network was trained and tested on simulation and in vivo datasets. Results showed superior attenuation coefficient estimates both visually and based on quantitative image metrics. The structural similarity, energy error depth and peak signal-to-noise ratio are improved by at least 7%, 5% and 12.4%, respectively, compared with the state-of-the-art non-learning methods. This method potentially enables high-precision quantitative imaging for tissue characterization and vulnerable plaque identification.
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Cannon TM, Bouma BE, Uribe-Patarroyo N. Mapping optical scattering properties to physical particle information in singly and multiply scattering samples. BIOMEDICAL OPTICS EXPRESS 2023; 14:4326-4348. [PMID: 37799686 PMCID: PMC10549752 DOI: 10.1364/boe.494518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 07/10/2023] [Accepted: 07/19/2023] [Indexed: 10/07/2023]
Abstract
Optical coherence tomography (OCT) leverages light scattering by biological tissues as endogenous contrast to form structural images. Light scattering behavior is dictated by the optical properties of the tissue, which depend on microstructural details at the cellular or sub-cellular level. Methods to measure these properties from OCT intensity data have been explored in the context of a number of biomedical applications seeking to access this sub-resolution tissue microstructure and thereby increase the diagnostic impact of OCT. Most commonly, the optical attenuation coefficient, an analogue of the scattering coefficient, has been used as a surrogate metric linking OCT intensity to subcellular particle characteristics. To record attenuation coefficient data that is accurately representative of the underlying physical properties of a given sample, it is necessary to account for the impact of the OCT imaging system itself on the distribution of light intensity in the sample, including the numerical aperture (NA) of the system and the location of the focal plane with respect to the sample surface, as well as the potential contribution of multiple scattering to the reconstructed intensity signal. Although these considerations complicate attenuation coefficient measurement and interpretation, a suitably calibrated system may potentiate a powerful strategy for gaining additional information about the scattering behavior and microstructure of samples. In this work, we experimentally show that altering the OCT system geometry minimally impacts measured attenuation coefficients in samples presumed to be singly scattering, but changes these measurements in more highly scattering samples. Using both depth-resolved attenuation coefficient data and layer-resolved backscattering coefficients, we demonstrate the retrieval of scattering particle diameter and concentration in tissue-mimicking phantoms, and the impact of presumed multiple scattering on these calculations. We further extend our approach to characterize a murine brain tissue sample and highlight a tumor-bearing region based on increased scattering particle density. Through these methods, we not only enhance conventional OCT attenuation coefficient analysis by decoupling the independent effects of particle size and concentration, but also discriminate areas of strong multiple scattering through minor changes to system topology to provide a framework for assessing the accuracy of these measurements.
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Affiliation(s)
- Taylor M. Cannon
- Massachusetts Institute of Technology, Institute of Medical Engineering and Science, 70 Massachusetts Avenue, Cambridge, MA 02141, USA
- Wellman Center for Photomedicine, Massachusetts General Hospital, 40 Blossom St, Boston, MA 02114, USA
| | - Brett E. Bouma
- Massachusetts Institute of Technology, Institute of Medical Engineering and Science, 70 Massachusetts Avenue, Cambridge, MA 02141, USA
- Wellman Center for Photomedicine, Massachusetts General Hospital, 40 Blossom St, Boston, MA 02114, USA
| | - Néstor Uribe-Patarroyo
- Wellman Center for Photomedicine, Massachusetts General Hospital, 40 Blossom St, Boston, MA 02114, USA
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Neubrand LB, van Leeuwen TG, Faber DJ. Accuracy and precision of depth-resolved estimation of attenuation coefficients in optical coherence tomography. JOURNAL OF BIOMEDICAL OPTICS 2023; 28:066001. [PMID: 37325192 PMCID: PMC10265837 DOI: 10.1117/1.jbo.28.6.066001] [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: 02/18/2023] [Revised: 05/24/2023] [Accepted: 05/30/2023] [Indexed: 06/17/2023]
Abstract
Significance Parametric imaging of the attenuation coefficient μ OCT using optical coherence tomography (OCT) is a promising approach for evaluating abnormalities in tissue. To date, a standardized measure of accuracy and precision of μ OCT by the depth-resolved estimation (DRE) method, as an alternative to least squares fitting, is missing. Aim We present a robust theoretical framework to determine accuracy and precision of the DRE of μ OCT . Approach We derive and validate analytical expressions for the accuracy and precision of μ OCT determination by the DRE using simulated OCT signals in absence and presence of noise. We compare the theoretically achievable precisions of the DRE method and the least-squares fitting approach. Results Our analytical expressions agree with the numerical simulations for high signal-to-noise ratios and qualitatively describe the dependence on noise otherwise. A commonly used simplification of the DRE method results in a systematic overestimation of the attenuation coefficient in the order of μ OCT 2 × Δ , where Δ is the pixel stepsize. When μ OCT · | AFR | ≲ 1.8 , μ OCT is reconstructed with higher precision by the depth-resolved method compared to fitting over the length of an axial fitting range | AFR | . Conclusions We derived and validated expressions for the accuracy and precision of DRE of μ OCT . A commonly used simplification of this method is not recommended as being used for OCT-attenuation reconstruction. We give a rule of thumb providing guidance in the choice of estimation method.
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Affiliation(s)
- Linda B. Neubrand
- Amsterdam UMC, Location University of Amsterdam, Department of Biomedical Engineering and Physics, Amsterdam, The Netherlands
- Amsterdam Cardiovascular Sciences, Atherosclerosis and Ischemic Syndromes, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - Ton G. van Leeuwen
- Amsterdam UMC, Location University of Amsterdam, Department of Biomedical Engineering and Physics, Amsterdam, The Netherlands
- Amsterdam Cardiovascular Sciences, Atherosclerosis and Ischemic Syndromes, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - Dirk J. Faber
- Amsterdam UMC, Location University of Amsterdam, Department of Biomedical Engineering and Physics, Amsterdam, The Netherlands
- Amsterdam Cardiovascular Sciences, Atherosclerosis and Ischemic Syndromes, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
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Lu J, Cheng Y, Li J, Liu Z, Shen M, Zhang Q, Liu J, Herrera G, Hiya FE, Morin R, Joseph J, Gregori G, Rosenfeld PJ, Wang RK. Automated segmentation and quantification of calcified drusen in 3D swept source OCT imaging. BIOMEDICAL OPTICS EXPRESS 2023; 14:1292-1306. [PMID: 36950236 PMCID: PMC10026581 DOI: 10.1364/boe.485999] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 02/18/2023] [Accepted: 02/19/2023] [Indexed: 06/18/2023]
Abstract
Qualitative and quantitative assessments of calcified drusen are clinically important for determining the risk of disease progression in age-related macular degeneration (AMD). This paper reports the development of an automated algorithm to segment and quantify calcified drusen on swept-source optical coherence tomography (SS-OCT) images. The algorithm leverages the higher scattering property of calcified drusen compared with soft drusen. Calcified drusen have a higher optical attenuation coefficient (OAC), which results in a choroidal hypotransmission defect (hypoTD) below the calcified drusen. We show that it is possible to automatically segment calcified drusen from 3D SS-OCT scans by combining the OAC within drusen and the hypoTDs under drusen. We also propose a correction method for the segmentation of the retina pigment epithelium (RPE) overlying calcified drusen by automatically correcting the RPE by an amount of the OAC peak width along each A-line, leading to more accurate segmentation and quantification of drusen in general, and the calcified drusen in particular. A total of 29 eyes with nonexudative AMD and calcified drusen imaged with SS-OCT using the 6 × 6 mm2 scanning pattern were used in this study to test the performance of the proposed automated method. We demonstrated that the method achieved good agreement with the human expert graders in identifying the area of calcified drusen (Dice similarity coefficient: 68.27 ± 11.09%, correlation coefficient of the area measurements: r = 0.9422, the mean bias of the area measurements = 0.04781 mm2).
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Affiliation(s)
- Jie Lu
- Department of Bioengineering, University of Washington, Seattle, Washington, USA
| | - Yuxuan Cheng
- Department of Bioengineering, University of Washington, Seattle, Washington, USA
| | - Jianqing Li
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Ziyu Liu
- Department of Bioengineering, University of Washington, Seattle, Washington, USA
| | - Mengxi Shen
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Qinqin Zhang
- Department of Bioengineering, University of Washington, Seattle, Washington, USA
- Research and Development, Carl Zeiss Meditec, Inc., Dublin, CA, USA
| | - Jeremy Liu
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Gissel Herrera
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Farhan E. Hiya
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Rosalyn Morin
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Joan Joseph
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Giovanni Gregori
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Philip J. Rosenfeld
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Ruikang K. Wang
- Department of Bioengineering, University of Washington, Seattle, Washington, USA
- Department of Ophthalmology, University of Washington, Seattle, Washington, USA
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Xu W, Wang H. Using beam-offset optical coherence tomography to reconstruct backscattered photon profiles in scattering media. BIOMEDICAL OPTICS EXPRESS 2022; 13:6124-6135. [PMID: 36733762 PMCID: PMC9872868 DOI: 10.1364/boe.469082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 09/10/2022] [Accepted: 10/09/2022] [Indexed: 06/18/2023]
Abstract
Raster scanning imaging technologies capture least scattered photons (LSPs) and reject multiple scattered photons (MSPs) in backscattered photons to image the underlying structures of a scattering medium. However, MSPs can still squeeze into the images, resulting in limited imaging depth, degraded contrast, and significantly reduced lateral resolution. Great efforts have been made to understand how MSPs affect imaging performance through modeling, but the techniques for visualizing the backscattered photon profile (BSPP) in scattering media during imaging are unavailable. Here, a method of reconstructing BSPP is demonstrated using beam-offset optical coherence tomography (OCT), in which OCT images are acquired at offset positions from the illumination beam. The separation of LSPs and MSPs based on the BSPP enables quantification of imaging depth, contrast, and lateral resolution, as well as access to the depth-resolved modulated transfer function (MTF). This approach presents great opportunities for better retrieving tissue optical properties, correctly interpreting images, or directly using MTF as the feedback for adaptive optical imaging.
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Affiliation(s)
- Weiming Xu
- The Department of Chemical, Paper, and Biomedical Engineering, Miami University, Oxford, 45056 OH, USA
- The Department of Biomedical Engineering, Texas A&M University, College Station, TX 77843, USA
| | - Hui Wang
- The Department of Chemical, Paper, and Biomedical Engineering, Miami University, Oxford, 45056 OH, USA
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Seesan T, Abd El-Sadek I, Mukherjee P, Zhu L, Oikawa K, Miyazawa A, Shen LTW, Matsusaka S, Buranasiri P, Makita S, Yasuno Y. Deep convolutional neural network-based scatterer density and resolution estimators in optical coherence tomography. BIOMEDICAL OPTICS EXPRESS 2022; 13:168-183. [PMID: 35154862 PMCID: PMC8803045 DOI: 10.1364/boe.443343] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 11/03/2021] [Accepted: 11/25/2021] [Indexed: 05/02/2023]
Abstract
We present deep convolutional neural network (DCNN)-based estimators of the tissue scatterer density (SD), lateral and axial resolutions, signal-to-noise ratio (SNR), and effective number of scatterers (ENS, the number of scatterers within a resolution volume). The estimators analyze the speckle pattern of an optical coherence tomography (OCT) image in estimating these parameters. The DCNN is trained by a large number (1,280,000) of image patches that are fully numerically generated in OCT imaging simulation. Numerical and experimental validations were performed. The numerical validation shows good estimation accuracy as the root mean square errors were 0.23%, 3.65%, 3.58%, 3.79%, and 6.15% for SD, lateral and axial resolutions, SNR, and ENS, respectively. The experimental validation using scattering phantoms (Intralipid emulsion) shows reasonable estimations. Namely, the estimated SDs were proportional to the Intralipid concentrations, and the average estimation errors of lateral and axial resolutions were 1.36% and 0.68%, respectively. The scatterer density estimator was also applied to an in vitro tumor cell spheroid, and a reduction in the scatterer density during cell necrosis was found.
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Affiliation(s)
- Thitiya Seesan
- Computational Optics Group, University of Tsukuba, Tsukuba, Ibaraki, Japan
- Department of Physics, School of Science, King Mongkut’s Institute of Technology Ladkrabang, Ladkrabang, Bangkok, Thailand
| | - Ibrahim Abd El-Sadek
- Computational Optics Group, University of Tsukuba, Tsukuba, Ibaraki, Japan
- Department of Physics, Faculty of Science, Damietta University, New Damietta City, Damietta, Egypt
| | - Pradipta Mukherjee
- Computational Optics Group, University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Lida Zhu
- Computational Optics Group, University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Kensuke Oikawa
- Computational Optics Group, University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Arata Miyazawa
- Computational Optics Group, University of Tsukuba, Tsukuba, Ibaraki, Japan
- Sky Technology Inc., Tsukuba, Ibaraki, Japan
| | - Larina Tzu-Wei Shen
- Clinical Research and Regional Innovation, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan
| | - Satoshi Matsusaka
- Clinical Research and Regional Innovation, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan
| | - Prathan Buranasiri
- Department of Physics, School of Science, King Mongkut’s Institute of Technology Ladkrabang, Ladkrabang, Bangkok, Thailand
| | - Shuichi Makita
- Computational Optics Group, University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Yoshiaki Yasuno
- Computational Optics Group, University of Tsukuba, Tsukuba, Ibaraki, Japan
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