Synthesizing the degree of polarization uniformity from non-polarization-sensitive optical coherence tomography signals using a neural network

Makita, Shuichi; Miura, Masahiro; Azuma, Shinnosuke; Mino, Toshihiro; Yasuno, Yoshiaki

doi:10.1364/boe.482199

Cited by 3 publications

(1 citation statement)

References 67 publications

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“…This approach involves utilizing the input image as a condition to generate the corresponding output image. Similar endeavors also become common in the medical image field, which include generating polarization-sensitive OCT images from conventional OCT images [27,28], translating between multimodal magnetic resonance (MR) images [29], and converting MR images into computed tomography (CT) images [30].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Motion Artifact-Suppressed OCTA Image Generation from Both Repeated and Adjacent OCT Scans

Lin,

Zhang,

Lan

et al. 2024

Mathematics

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Optical coherence tomography angiography (OCTA) is a popular technique for imaging microvascular networks, but OCTA image quality is commonly affected by motion artifacts. Deep learning (DL) has been used to generate OCTA images from structural OCT images, yet limitations persist, such as low label image quality caused by motion and insufficient use of neighborhood information. In this study, an attention-based U-Net incorporating both repeated and adjacent structural OCT images in network input and high-quality label OCTA images in training was proposed to generate high-quality OCTA images with motion artifact suppression. A sliding-window correlation-based adjacent position (SWCB-AP) image fusion method was proposed to generate high-quality OCTA label images with suppressed motion noise. Six different DL schemes with various configurations of network inputs and label images were compared to demonstrate the superiority of the proposed method. Motion artifact severity was evaluated by a motion noise index in B-scan (MNI-B) and in en-face (MNI-C) OCTA images, which were specifically defined in this study for the purpose of evaluating various DL models’ capability in motion noise suppression. Experimental results on a nailfold OCTA image dataset showed that the proposed DL method generated the best results with a peak signal-to-noise ratio (PSNR) of 32.666 ± 7.010 dB, structural similarity (SSIM) of 0.926 ± 0.051, mean absolute error (MAE) of 1.798 ± 1.575, and MNI-B of 0.528 ± 0.124 in B-scan OCTA images and a contrast-to-noise ratio (CNR) of 1.420 ± 0.291 and MNI-C of 0.156 ± 0.057 in en-face OCTA images. Our proposed DL approach generated OCTA images with improved blood flow contrast and reduced motion artifacts, which could be used as a fundamental signal processing module in generating high-quality OCTA images from structural OCT images.

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Section: Introductionmentioning

confidence: 99%

Deep Learning for Motion Artifact-Suppressed OCTA Image Generation from Both Repeated and Adjacent OCT Scans

Lin,

Zhang,

Lan

et al. 2024

Mathematics

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Birefringence-derived artifact in optical coherence tomography imaging of the lamina cribrosa in eyes with glaucoma

Miura,

Makita,

Yasuno

et al. 2023

Sci Rep

Self Cite

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We investigated birefringence-derived artifacts that potentially mimic focal defects of the lamina cribrosa (focal LC defects) in optical coherence tomography (OCT) imaging of eyes with glaucoma. This study included 74 eyes of 48 patients with glaucoma. Five horizontal line B-scan images of the optic disc were obtained using commercial swept-source OCT. From a dataset of prototype swept-source polarization-diversity OCT, we calculated the following types of OCT images: polarization-dependent, polarization-dependent attenuation-coefficient, polarization-independent, and polarization-independent attenuation-coefficient. We assessed the commercial OCT images for the presence of birefringence-derived artifacts by comparison with the polarization-diversity OCT images. Commercial OCT showed suggestive findings of focal LC defects in 17 of 74 eyes. Reevaluation using polarization-independent OCT revealed that the focal LC defects in one of 17 eyes (5.9%) were actually birefringence-derived artifacts. This study demonstrated the existence of birefringence-derived artifacts mimicking focal LC defects in commercial OCT imaging and indicated that polarization-diversity OCT is an effective tool to evaluate the presence of these artifacts.

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Digital calibration method to enable depth-resolved all-fiber polarization sensitive optical coherence tomography with an arbitrary input polarization state

Tang,

Wang,

Chao

2024

Biomed. Opt. Express

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We present a fully integrated depth-resolved all fiber-based polarization sensitive optical coherence tomography (PSOCT). In contrast to conventional fiber-based PSOCT systems, which require additional modules to generate two or more input polarization states, or a pre-adjustment procedure to generate a circularly polarized light, the proposed all-fiber PSOCT system can provide depth-resolved birefringent imaging using an arbitrary single input polarization state. Utilizing the discrete differential geometry (DDG)-based polarization state tracing (PST) method, combined with several geometric rotations and transformations in the Stokes space, two problems induced by the optical fibers can be mitigated: 1) The change in the polarization state introduced by the optical fibers can be effectively compensated using a calibration target at the distal end of the probe, and the computations of the local axis orientation and local phase retardation can be achieved with a single arbitrary input polarization state, eliminating the need for a pre-defined input polarization state, allowing a flexible system design and user-friendly experimental procedure; 2) The polarization mode dispersion (PMD) induced by the optical fibers can be compensated digitally without the requirement of additional input polarization states, providing an accurate PSOCT imaging result. To demonstrate the performance of the proposed method, the depth resolved PSOCT results of a plastic phantom and in vivo skin imaging are obtained using the proposed all-fiber PSOCT system.

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Synthesizing the degree of polarization uniformity from non-polarization-sensitive optical coherence tomography signals using a neural network

Cited by 3 publications

References 67 publications

Deep Learning for Motion Artifact-Suppressed OCTA Image Generation from Both Repeated and Adjacent OCT Scans

Deep Learning for Motion Artifact-Suppressed OCTA Image Generation from Both Repeated and Adjacent OCT Scans

Birefringence-derived artifact in optical coherence tomography imaging of the lamina cribrosa in eyes with glaucoma

Digital calibration method to enable depth-resolved all-fiber polarization sensitive optical coherence tomography with an arbitrary input polarization state

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