FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4225656871> ?p ?o ?g. }

• W4225656871 endingPage "906" @default.
• W4225656871 startingPage "895" @default.
• W4225656871 abstract "Abstract Background and Objectives Optical coherence tomography (OCT) is a cross‐sectional imaging method utilizing a low coherence interferometry. The lateral resolution of the OCT is limited by the numerical aperture (NA) of the imaging lens. Using a high NA lens improves the lateral resolution but reduces the depth of focus (DOF). In this study, we propose a method to improve the lateral resolution of OCT images by end‐to‐end training of a deep 1‐D deconvolution network without use of high‐resolution images. Materials and Methods To improve the lateral resolution of the OCT, we trained the 1‐D deconvolution network using lateral profiles of OCT images and the beam spot size. We used our image‐guided laparoscopic surgical tool (IGLaST) to acquire OCT images of nonbiological and biological samples ex vivo. The OCT images were then blurred by applying Gaussian functions with various full width half maximums ranging from 40 to 160 µm. The network was trained using the blurred OCT images as input and the non‐blurred original OCT images as output. We quantitatively evaluated the developed network in terms of similarity and signal‐to‐ratio (SNR), using in‐vivo images of mesenteric tissue from a porcine model that was not used for training. In addition, we performed knife‐edge tests and qualitative evaluation of the network to show the lateral resolution improvement of ex‐vivo and in‐vivo OCT images. Results The proposed method showed an improvement of image quality on both blurred images and non‐blurred images. When the proposed deconvolution network was applied, the similarity to the non‐blurred image was improved by 1.29 times, and the SNR was improved by 1.76 dB compared to the artificially blurred images, which was superior to the conventional deconvolution method. The knife‐edge tests at distances at 200 to 1000 µm from the imaging probe showed an approximately 1.2 times improvement in lateral resolution. In addition, through qualitative evaluation, it was found that the image quality of both ex‐vivo and in‐vivo tissue images was improved with clear structure and less noise. Conclusions This study showed the ability of the 1‐D deconvolution network to improve the image quality of OCT images with variable lateral resolution. We were able to train the network with a small amount of data by constraining the network in 1‐D. The quantitative evaluation showed better results than conventional deconvolution methods for various amount of blurring. Qualitative evaluation showed analogous results with quantitative results. This simple yet powerful image restoration method provides improved lateral resolution and suppresses background noise, making it applicable to a variety of OCT imaging applications." @default.
• W4225656871 created "2022-05-05" @default.
• W4225656871 creator A5012850047 @default.
• W4225656871 creator A5018702490 @default.
• W4225656871 creator A5053052957 @default.
• W4225656871 creator A5070770296 @default.
• W4225656871 creator A5073233812 @default.
• W4225656871 creator A5074029092 @default.
• W4225656871 creator A5081217020 @default.
• W4225656871 creator A5084992532 @default.
• W4225656871 date "2022-04-02" @default.
• W4225656871 modified "2023-10-01" @default.
• W4225656871 title "Lateral image reconstruction of optical coherence tomography using one‐dimensional deep deconvolution network" @default.
• W4225656871 cites W1834839393 @default.
• W4225656871 cites W1901129140 @default.
• W4225656871 cites W1965555277 @default.
• W4225656871 cites W1970140118 @default.
• W4225656871 cites W1972153310 @default.
• W4225656871 cites W1978706462 @default.
• W4225656871 cites W1987382181 @default.
• W4225656871 cites W2009715494 @default.
• W4225656871 cites W2025832555 @default.
• W4225656871 cites W2041253758 @default.
• W4225656871 cites W2045490091 @default.
• W4225656871 cites W2078920062 @default.
• W4225656871 cites W2100471509 @default.
• W4225656871 cites W2105274562 @default.
• W4225656871 cites W2116360511 @default.
• W4225656871 cites W2124358538 @default.
• W4225656871 cites W2124421967 @default.
• W4225656871 cites W2133665775 @default.
• W4225656871 cites W2150333870 @default.
• W4225656871 cites W2168020664 @default.
• W4225656871 cites W2194775991 @default.
• W4225656871 cites W2302255633 @default.
• W4225656871 cites W2418802570 @default.
• W4225656871 cites W2463262466 @default.
• W4225656871 cites W2560219585 @default.
• W4225656871 cites W2570343428 @default.
• W4225656871 cites W2578192693 @default.
• W4225656871 cites W2592929672 @default.
• W4225656871 cites W2605760442 @default.
• W4225656871 cites W2741826584 @default.
• W4225656871 cites W2937484554 @default.
• W4225656871 cites W2963446712 @default.
• W4225656871 cites W2963470893 @default.
• W4225656871 cites W2967073593 @default.
• W4225656871 cites W3000499123 @default.
• W4225656871 cites W3012216802 @default.
• W4225656871 cites W3091889996 @default.
• W4225656871 cites W3102734610 @default.
• W4225656871 doi "https://doi.org/10.1002/lsm.23543" @default.
• W4225656871 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35366377" @default.
• W4225656871 hasPublicationYear "2022" @default.
• W4225656871 type Work @default.
• W4225656871 citedByCount "3" @default.
• W4225656871 countsByYear W42256568712022 @default.
• W4225656871 countsByYear W42256568712023 @default.
• W4225656871 crossrefType "journal-article" @default.
• W4225656871 hasAuthorship W4225656871A5012850047 @default.
• W4225656871 hasAuthorship W4225656871A5018702490 @default.
• W4225656871 hasAuthorship W4225656871A5053052957 @default.
• W4225656871 hasAuthorship W4225656871A5070770296 @default.
• W4225656871 hasAuthorship W4225656871A5073233812 @default.
• W4225656871 hasAuthorship W4225656871A5074029092 @default.
• W4225656871 hasAuthorship W4225656871A5081217020 @default.
• W4225656871 hasAuthorship W4225656871A5084992532 @default.
• W4225656871 hasConcept C102290492 @default.
• W4225656871 hasConcept C115961682 @default.
• W4225656871 hasConcept C120665830 @default.
• W4225656871 hasConcept C121332964 @default.
• W4225656871 hasConcept C141379421 @default.
• W4225656871 hasConcept C154945302 @default.
• W4225656871 hasConcept C174576160 @default.
• W4225656871 hasConcept C205372480 @default.
• W4225656871 hasConcept C24890656 @default.
• W4225656871 hasConcept C2778818243 @default.
• W4225656871 hasConcept C31972630 @default.
• W4225656871 hasConcept C41008148 @default.
• W4225656871 hasConcept C55020928 @default.
• W4225656871 hasConcept C78336883 @default.
• W4225656871 hasConceptScore W4225656871C102290492 @default.
• W4225656871 hasConceptScore W4225656871C115961682 @default.
• W4225656871 hasConceptScore W4225656871C120665830 @default.
• W4225656871 hasConceptScore W4225656871C121332964 @default.
• W4225656871 hasConceptScore W4225656871C141379421 @default.
• W4225656871 hasConceptScore W4225656871C154945302 @default.
• W4225656871 hasConceptScore W4225656871C174576160 @default.
• W4225656871 hasConceptScore W4225656871C205372480 @default.
• W4225656871 hasConceptScore W4225656871C24890656 @default.
• W4225656871 hasConceptScore W4225656871C2778818243 @default.
• W4225656871 hasConceptScore W4225656871C31972630 @default.
• W4225656871 hasConceptScore W4225656871C41008148 @default.
• W4225656871 hasConceptScore W4225656871C55020928 @default.
• W4225656871 hasConceptScore W4225656871C78336883 @default.
• W4225656871 hasIssue "6" @default.
• W4225656871 hasLocation W42256568711 @default.
• W4225656871 hasLocation W42256568712 @default.

• next