Optical Coherence Tomography (OCT): State of the Art

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Optical Diagnostics".

Deadline for manuscript submissions: 31 October 2024 | Viewed by 5525

Special Issue Editor


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Guest Editor
Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
Interests: optical coherence tomography; photoacoustic imaging

Special Issue Information

Dear Colleagues,

Optical coherence tomography (OCT), since its introduction more than three decades ago, has become a go-to solution in ophthalmology for the diagnosis and evaluation of a variety of eye diseases and defects. In addition to ophthalmology, OCT has also found unique translational value in other clinical fields such as dermatology, cardiology, and even neurology. Thus far, many different implementations of OCT have been demonstrated and clinically validated. Functional extensions of OCT, such as OCT angiography and Doppler OCT, are also gradually accepted in daily clinical practice. In this Special Issue, we would like to select the works that represent the state-of-the-art OCT development. Suitable topics include, but are not limited to:

  • AI and OCT;
  • Adaptive optics OCT (AO-OCT);
  • Polarization sensitive OCT (PS-OCT);
  • OCT angiography (OCTA);
  • Doppler OCT;
  • Multimodal imaging incorporating OCT;
  • Clinical application of OCT;
  • Preclinical application of OCT. 

All types of articles are welcome, be it a perspective, review, or original research article. However, as the Special Issue’s title suggests, the submitted work needs to present the state of the art of OCT. Understandably, OCT has also been applied in non-biophotonics field. We would kindly ask interested authors to only submit articles that are relevant to biophotonics due to the scope of the journal.

Dr. Mengyang Liu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • optical coherence tomography
  • artificial intelligence
  • multimodal imaging
  • ophthalmology
  • angiography
  • adaptive optics
  • dermatology
  • cardiology
  • endoscopy
  • diagnosis
 

Published Papers (4 papers)

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Research

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18 pages, 37697 KiB  
Article
Assessing Lung Fibrosis with ML-Assisted Minimally Invasive OCT Imaging
by Rebecca Steinberg, Jack Meehan, Doran Tavrow, Gopi Maguluri, John Grimble, Michael Primrose and Nicusor Iftimia
Diagnostics 2024, 14(12), 1243; https://doi.org/10.3390/diagnostics14121243 - 13 Jun 2024
Viewed by 243
Abstract
This paper presents a combined optical coherence tomography (OCT) imaging/machine learning (ML) technique for real-time analysis of lung tissue morphology to determine the presence and level of invasiveness of idiopathic lung fibrosis (ILF). This is an important clinical problem as misdiagnosis is common, [...] Read more.
This paper presents a combined optical coherence tomography (OCT) imaging/machine learning (ML) technique for real-time analysis of lung tissue morphology to determine the presence and level of invasiveness of idiopathic lung fibrosis (ILF). This is an important clinical problem as misdiagnosis is common, resulting in patient exposure to costly and invasive procedures and substantial use of healthcare resources. Therefore, biopsy is needed to confirm or rule out radiological findings. Videoscopic-assisted thoracoscopic wedge biopsy (VATS) under general anesthesia is typically necessary to obtain enough tissue to make an accurate diagnosis. This kind of biopsy involves the placement of several tubes through the chest wall, one of which is used to cut off a piece of lung to send for evaluation. The removed tissue is examined histopathologically by microscopy to confirm the presence and the pattern of fibrosis. However, VATS pulmonary biopsy can have multiple side effects, including inflammation, tissue morbidity, and severe bleeding, which further degrade the quality of life for the patient. Furthermore, the results are not immediately available, requiring tissue processing and analysis. Here, we report an initial attempt of using ML-assisted polarization sensitive OCT (PS-OCT) imaging for lung fibrosis assessment. This approach has been preliminarily tested on a rat model of lung fibrosis. Our preliminary results show that ML-assisted PS-OCT imaging can detect the presence of ILF with an average of 77% accuracy and 89% specificity. Full article
(This article belongs to the Special Issue Optical Coherence Tomography (OCT): State of the Art)
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17 pages, 4330 KiB  
Article
Segmentation and Multi-Timepoint Tracking of 3D Cancer Organoids from Optical Coherence Tomography Images Using Deep Neural Networks
by Francesco Branciforti, Massimo Salvi, Filippo D’Agostino, Francesco Marzola, Sara Cornacchia, Maria Olimpia De Titta, Girolamo Mastronuzzi, Isotta Meloni, Miriam Moschetta, Niccolò Porciani, Fabrizio Sciscenti, Alessandro Spertini, Andrea Spilla, Ilenia Zagaria, Abigail J. Deloria, Shiyu Deng, Richard Haindl, Gergely Szakacs, Agnes Csiszar, Mengyang Liu, Wolfgang Drexler, Filippo Molinari and Kristen M. Meiburgeradd Show full author list remove Hide full author list
Diagnostics 2024, 14(12), 1217; https://doi.org/10.3390/diagnostics14121217 - 8 Jun 2024
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Abstract
Recent years have ushered in a transformative era in in vitro modeling with the advent of organoids, three-dimensional structures derived from stem cells or patient tumor cells. Still, fully harnessing the potential of organoids requires advanced imaging technologies and analytical tools to quantitatively [...] Read more.
Recent years have ushered in a transformative era in in vitro modeling with the advent of organoids, three-dimensional structures derived from stem cells or patient tumor cells. Still, fully harnessing the potential of organoids requires advanced imaging technologies and analytical tools to quantitatively monitor organoid growth. Optical coherence tomography (OCT) is a promising imaging modality for organoid analysis due to its high-resolution, label-free, non-destructive, and real-time 3D imaging capabilities, but accurately identifying and quantifying organoids in OCT images remain challenging due to various factors. Here, we propose an automatic deep learning-based pipeline with convolutional neural networks that synergistically includes optimized preprocessing steps, the implementation of a state-of-the-art deep learning model, and ad-hoc postprocessing methods, showcasing good generalizability and tracking capabilities over an extended period of 13 days. The proposed tracking algorithm thoroughly documents organoid evolution, utilizing reference volumes, a dual branch analysis, key attribute evaluation, and probability scoring for match identification. The proposed comprehensive approach enables the accurate tracking of organoid growth and morphological changes over time, advancing organoid analysis and serving as a solid foundation for future studies for drug screening and tumor drug sensitivity detection based on organoids. Full article
(This article belongs to the Special Issue Optical Coherence Tomography (OCT): State of the Art)
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11 pages, 841 KiB  
Article
Anterior Scleral Thickness and Anterior Segment Biometrics Measured with Swept Source Ocular Coherence Tomography in High Myopic Eyes with and without Glaucoma: A Comparative Study
by Bachar Kudsieh, Rocio Vega-González, Sofia Bryan, Elena Almazan-Alonso, Mariluz Puertas, Lucia Gutiérrez-Martin, Ignacio Flores-Moreno, Jorge Ruiz-Medrano, Muhsen Samaan and Jose Maria Ruiz-Moreno
Diagnostics 2024, 14(6), 655; https://doi.org/10.3390/diagnostics14060655 - 20 Mar 2024
Viewed by 793
Abstract
Background: To assess the anterior scleral thickness (AST), Schlemm’s canal diameter (SCD), trabecular meshwork diameter (TMD) and conjunctiva tenon capsule thickness (CTT) in high myopic (HM) subjects and HM subjects with glaucoma (HMG) compared to control eyes. Methods: One hundred and twenty [...] Read more.
Background: To assess the anterior scleral thickness (AST), Schlemm’s canal diameter (SCD), trabecular meshwork diameter (TMD) and conjunctiva tenon capsule thickness (CTT) in high myopic (HM) subjects and HM subjects with glaucoma (HMG) compared to control eyes. Methods: One hundred and twenty eyes were included, and AST at 0, 1, 2 and 3 mm from the scleral spur, SCD, TMD and CTT were measured. Results: Mean age was 64.2 ± 11.0 years, and the temporal SCD and temporal TMD were significantly longer in the HMG subjects compared to the controls (380.0 ± 62 μm vs. 316.7 ± 72 μm, p = 0.001) and (637.6 ± 113 μm vs. 512.1 ± 97 μm, p = 0.000), respectively. There were no significant differences between the HM and HMG subjects in SCD and TMD (all p > 0.025). Compared to the HM subjects, the temporal AST0 (432.5 ± 79 μm vs. 532.8 ± 99 μm, p = 0.000), temporal AST1 (383.9 ± 64 μm vs. 460.5 ± 80 μm, p = 0.000), temporal AST2 (404.0 ± 68 μm vs. 464.0 ± 88 μm, p = 0.006) and temporal AST3 (403.0 ± 80 μm vs. 458.1 ± 91 μm, p = 0.014) were significantly thinner in the HMG group. No differences were found between the CTT in the three groups (all p > 0.025). Conclusions: Our data indicate a thinner AST in HMG subjects and no differences in SCD and TMD between HM and HMG subjects. Full article
(This article belongs to the Special Issue Optical Coherence Tomography (OCT): State of the Art)
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Review

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20 pages, 3953 KiB  
Review
Optical Coherence Tomography Angiography in Retinal Vascular Disorders
by Charles Jit Teng Ong, Mark Yu Zheng Wong, Kai Xiong Cheong, Jinzhi Zhao, Kelvin Yi Chong Teo and Tien-En Tan
Diagnostics 2023, 13(9), 1620; https://doi.org/10.3390/diagnostics13091620 - 3 May 2023
Cited by 5 | Viewed by 3147
Abstract
Traditionally, abnormalities of the retinal vasculature and perfusion in retinal vascular disorders, such as diabetic retinopathy and retinal vascular occlusions, have been visualized with dye-based fluorescein angiography (FA). Optical coherence tomography angiography (OCTA) is a newer, alternative modality for imaging the retinal vasculature, [...] Read more.
Traditionally, abnormalities of the retinal vasculature and perfusion in retinal vascular disorders, such as diabetic retinopathy and retinal vascular occlusions, have been visualized with dye-based fluorescein angiography (FA). Optical coherence tomography angiography (OCTA) is a newer, alternative modality for imaging the retinal vasculature, which has some advantages over FA, such as its dye-free, non-invasive nature, and depth resolution. The depth resolution of OCTA allows for characterization of the retinal microvasculature in distinct anatomic layers, and commercial OCTA platforms also provide automated quantitative vascular and perfusion metrics. Quantitative and qualitative OCTA analysis in various retinal vascular disorders has facilitated the detection of pre-clinical vascular changes, greater understanding of known clinical signs, and the development of imaging biomarkers to prognosticate and guide treatment. With further technological improvements, such as a greater field of view and better image quality processing algorithms, it is likely that OCTA will play an integral role in the study and management of retinal vascular disorders. Artificial intelligence methods—in particular, deep learning—show promise in refining the insights to be gained from the use of OCTA in retinal vascular disorders. This review aims to summarize the current literature on this imaging modality in relation to common retinal vascular disorders. Full article
(This article belongs to the Special Issue Optical Coherence Tomography (OCT): State of the Art)
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