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Optical Imaging: Trends, Impact, and Application in Medical and Biomedical...
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Optical Imaging: Trends, Impact, and Application in Medical and Biomedical Diagnostics

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

Deadline for manuscript submissions: 28 February 2025 | Viewed by 4270

Special Issue Editor

Dr. Naresh Kumar Ravichandran

E-Mail Website
Guest Editor
Center for Scientific Instrumentation, Korea Basic Science Institute, 169-148 Gwahak-ro Yuseong-gu, Daejeon 34133, Republic of Korea
Interests: optical imaging; optical design; medical and biomedical investigation; optical coherence tomography (OCT); confocal fluorescence microscopy (FM); multiphoton microscopy (MPM); 3D scanning systems; 3D image analysis; algorithm development

Special Issue Information

Dear Colleagues,

Optical imaging has emerged as a transformative force in medical and biomedical diagnostics, offering unprecedented insights into cellular and molecular processes with remarkable precision, helping researchers, clinicians, and doctors understand the biological process with high precision and assisting them to make informed diagnostic conclusions. This Special Issue delves into the dynamic landscape of optical imaging, exploring the latest trends, assessing its profound impact, and uncovering its diverse applications in the ever-evolving field of medical diagnostics.

The trajectory of optical imaging technologies has witnessed a paradigm shift propelled by continuous advancements in instrumentation and methodologies, from Traditional Fluorescence Microscopy (FM), Ultrasound Imaging, Computed Tomography (CT), and Magnetic Resonance Imaging (MRI) to cutting-edge techniques like Optical Coherence Tomography (OCT), Multiphoton Microscopy (MPM), Confocal Fluorescence Imaging, Structured Illumination Microscopy (SIM), Micro-Computed Tomography (µCT), Conebeam Computed Tomography (CBCT), and Endoscopy. Wiith similar emerging optical techniques, this issue captures the essence of these trends. The assimilation of artificial intelligence and machine learning into optical imaging workflows further amplifies its diagnostic capabilities, presenting a new era of intelligent medical imaging.

The impact of optical imaging on medical and biomedical diagnostics cannot be overstated. Its non-invasive nature, high resolution, and ability to provide real-time data empower clinicians and researchers alike. This Special Issue explores studies and breakthroughs where optical imaging has played a pivotal role in early disease detection, therapeutic monitoring, and personalized medicine.

In the realm of medical diagnostics, optical imaging finds diverse applications. It helps in understanding intricate biological processes, aids in understanding disease mechanisms at the cellular level, and facilitates the development of novel diagnostic biomarkers. Additionally, optical imaging technologies have found a niche in intraoperative guidance, offering surgeons unprecedented views during procedures.

This Special Issue hopes to bring together a collection of articles that reflect the multifaceted impact of optical imaging. It will serve as a comprehensive resource for researchers, clinicians, and professionals engaged in pushing the boundaries of medical and biomedical diagnostics. As we navigate the complex interplay of optical imaging trends, their profound impact, and their wide-ranging applications, we aim to foster a deeper understanding and appreciation for the pivotal role these technologies play in shaping the future of healthcare.

We welcome experts and young aspiring researchers, clinicians, and doctors alike who are users of optical imaging techniques to contribute and explore the current trends’ convergence with translational potential, providing resonating impact and applications to inspire new frontiers in medical and biomedical diagnostics.

Dr. Naresh Kumar Ravichandran
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

  • vascular and tumor imaging
  • drug testing
  • blood flow characterization
  • tissue characterization and cell migration analysis
  • fluorescence microscopy (FM)
  • optical coherence tomography (OCT)
  • ultrasound imaging (sonography)
  • multiphoton microscopy (MPM)
  • photoacoustic tomography (PAT)
  • computed tomography (CT)
  • conebeam computed tomography (CBCT)
  • magnetic resonance imaging (MRI)
  • endoscopy
  • scanning laser ophthalmoscopy
  • super-resolution microscopy
  • hyperspectral imaging
  • diffuse optical tomography
  • spectroscopy
  • structured illumination microscopy (SIM)
  • laser doppler imaging
  • artificial intelligence
  • machine learning
  • algorithm development

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Published Papers (4 papers)

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Research

13 pages, 4759 KiB  
Article
White Light Diffraction Phase Microscopy in Imaging of Breast and Colon Tissues
by Adriana Smarandache, Ruxandra A. Pirvulescu, Ionut-Relu Andrei, Andra Dinache, Mihaela Oana Romanitan, Daniel Constantin Branisteanu, Mihail Zemba, Nicoleta Anton, Mihail-Lucian Pascu and Viorel Nastasa
Diagnostics 2024, 14(17), 1966; https://doi.org/10.3390/diagnostics14171966 - 6 Sep 2024
Viewed by 515
Abstract
This paper reports results obtained using white light diffraction phase microscopy (wDPM) on captured images of breast and colon tissue samples, marking a contribution to the advancement in biomedical imaging. Unlike conventional brightfield microscopy, wDPM offers the capability to capture intricate details of [...] Read more.
This paper reports results obtained using white light diffraction phase microscopy (wDPM) on captured images of breast and colon tissue samples, marking a contribution to the advancement in biomedical imaging. Unlike conventional brightfield microscopy, wDPM offers the capability to capture intricate details of biological specimens with enhanced clarity and precision. It combines high resolution, enhanced contrast, and quantitative capabilities with non-invasive, label-free imaging. These features make it a useful tool for tissue imaging, providing detailed and accurate insights into tissue structure and dynamics without compromising the integrity of the samples. Our findings underscore the potential of quantitative phase imaging in histopathology, in the context of automating the process of tissue analysis and diagnosis. Of particular note are the insights gained from the reconstructed phase images, which provide physical data regarding peripheral glandular cell membranes. These observations serve to focus attention on pathologies involving the basal membrane, such as early invasive carcinoma. Through our analysis, we aim to contribute to catalyzing further advancements in tissue (breast and colon) imaging. Full article
(This article belongs to the Special Issue Optical Imaging: Trends, Impact, and Application in Medical and Biomedical Diagnostics)
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16 pages, 27101 KiB  
Article
Separating Surface Reflectance from Volume Reflectance in Medical Hyperspectral Imaging
by Lynn-Jade S. Jong, Anouk L. Post, Freija Geldof, Behdad Dashtbozorg, Theo J. M. Ruers and Henricus J. C. M. Sterenborg
Diagnostics 2024, 14(16), 1812; https://doi.org/10.3390/diagnostics14161812 - 20 Aug 2024
Viewed by 715
Abstract
Hyperspectral imaging has shown great promise for diagnostic applications, particularly in cancer surgery. However, non-bulk tissue-related spectral variations complicate the data analysis. Common techniques, such as standard normal variate normalization, often lead to a loss of amplitude and scattering information. This study investigates [...] Read more.
Hyperspectral imaging has shown great promise for diagnostic applications, particularly in cancer surgery. However, non-bulk tissue-related spectral variations complicate the data analysis. Common techniques, such as standard normal variate normalization, often lead to a loss of amplitude and scattering information. This study investigates a novel approach to address these spectral variations in hyperspectral images of optical phantoms and excised human breast tissue. Our method separates surface and volume reflectance, hypothesizing that spectral variability arises from significant variations in surface reflectance across pixels. An illumination setup was developed to measure samples with a hyperspectral camera from different axial positions but with identical zenith angles. This configuration, combined with a novel data analysis approach, allows for the estimation and separation of surface reflectance for each direction and volume reflectance across all directions. Validated with optical phantoms, our method achieved an 83% reduction in spectral variability. Its functionality was further demonstrated in excised human breast tissue. Our method effectively addresses variations caused by surface reflectance or glare while conserving surface reflectance information, which may enhance sample analysis and evaluation. It benefits samples with unknown refractive index spectra and can be easily adapted and applied across a wide range of fields where hyperspectral imaging is used. Full article
(This article belongs to the Special Issue Optical Imaging: Trends, Impact, and Application in Medical and Biomedical Diagnostics)
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18 pages, 3428 KiB  
Article
Assessing the Efficacy of the Spectrum-Aided Vision Enhancer (SAVE) to Detect Acral Lentiginous Melanoma, Melanoma In Situ, Nodular Melanoma, and Superficial Spreading Melanoma
by Teng-Li Lin, Chun-Te Lu, Riya Karmakar, Kalpana Nampalley, Arvind Mukundan, Yu-Ping Hsiao, Shang-Chin Hsieh and Hsiang-Chen Wang
Diagnostics 2024, 14(15), 1672; https://doi.org/10.3390/diagnostics14151672 - 1 Aug 2024
Cited by 1 | Viewed by 1061
Abstract
Skin cancer is the predominant form of cancer worldwide, including 75% of all cancer cases. This study aims to evaluate the effectiveness of the spectrum-aided visual enhancer (SAVE) in detecting skin cancer. This paper presents the development of a novel algorithm for snapshot [...] Read more.
Skin cancer is the predominant form of cancer worldwide, including 75% of all cancer cases. This study aims to evaluate the effectiveness of the spectrum-aided visual enhancer (SAVE) in detecting skin cancer. This paper presents the development of a novel algorithm for snapshot hyperspectral conversion, capable of converting RGB images into hyperspectral images (HSI). The integration of band selection with HSI has facilitated the identification of a set of narrow band images (NBI) from the RGB images. This study utilizes various iterations of the You Only Look Once (YOLO) machine learning (ML) framework to assess the precision, recall, and mean average precision in the detection of skin cancer. YOLO is commonly preferred in medical diagnostics due to its real-time processing speed and accuracy, which are essential for delivering effective and efficient patient care. The precision, recall, and mean average precision (mAP) of the SAVE images show a notable enhancement in comparison to the RGB images. This work has the potential to greatly enhance the efficiency of skin cancer detection, as well as improve early detection rates and diagnostic accuracy. Consequently, it may lead to a reduction in both morbidity and mortality rates. Full article
(This article belongs to the Special Issue Optical Imaging: Trends, Impact, and Application in Medical and Biomedical Diagnostics)
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11 pages, 11038 KiB  
Article
Prediction of the Cause of Glaucoma Disease Identified by Glaucoma Optical Coherence Tomography Test in Relation to Diabetes and Hypertension at a National Hospital in Seoul: A Retrospective Study
by Sun Jung Lee, Jae-Sik Jeon, Ji-Hyuk Kang and Jae Kyung Kim
Diagnostics 2024, 14(13), 1418; https://doi.org/10.3390/diagnostics14131418 - 3 Jul 2024
Viewed by 933
Abstract
Glaucoma remains the primary cause of long-term blindness. While diabetes mellitus (DM) and hypertension (HTN) are known to influence glaucoma, other factors such as age and sex may be involved. In this retrospective study, we aimed to investigate the associations between age, sex, [...] Read more.
Glaucoma remains the primary cause of long-term blindness. While diabetes mellitus (DM) and hypertension (HTN) are known to influence glaucoma, other factors such as age and sex may be involved. In this retrospective study, we aimed to investigate the associations between age, sex, DM, HTN, and glaucoma risk. We employed optical coherence tomography (OCT) conducted using a 200 × 200-pixel optic cube (Cirrus HD OCT 6000, version 10.0; Carl Zeiss Meditec, Dublin, CA, USA). Effects obscured by low-test signals were disregarded. Data were amassed from 1337 patients. Among them, 218 and 402 patients had DM and HTN, respectively, with 133 (10%) exhibiting both. A sex-based comparison revealed slightly greater retinal nerve fiber layer (RNFL) and ganglion cell–inner plexiform layer (GCIPL) thickness in females. Patients without DM and HTN were predominantly in their 50 s and 60 s, whereas DM and HTN were most prevalent in those in their 60 s and 70 s. Both RNFL and GCIPL thicknesses decreased with advancing age in most patients. The study revealed that older individuals were more prone to glaucoma than younger individuals, with a higher incidence among patients with DM and HTN and reduced RNFL and GCIPL thicknesses. Furthermore, early detection before advancing age could furnish valuable preventive insights. Full article
(This article belongs to the Special Issue Optical Imaging: Trends, Impact, and Application in Medical and Biomedical Diagnostics)
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