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Bioengineering
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Optical Imaging for Biomedical Applications
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Optical Imaging for Biomedical Applications

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 20 November 2024 | Viewed by 3605

Special Issue Editors

Dr. Johannes Dominikus Pallua

E-Mail Website
Guest Editor
Department of Orthopaedics and Traumatology, Medical University of Innsbruck, Anichstraße 35, 6020 Innsbruck, Austria
Interests: osteological pathologies; bone infection diagnosis; multimodal imaging; microscopy; digital pathology; infrared spectroscopy; raman spectroscopy; FTIR imaging; NIR spectroscopy; hyperspectral imaging; MRI; CT; micro-CT; MALDI imaging; imaging-guided surgery and treatment; 3D printing; orthopedics and traumatology; pathology; forensic science; analytical chemistry; molecular biology; microbiology; quality management
Prof. Dr. Christian Huck

E-Mail Website
Guest Editor
Institute of Analytical Chemistry and Radiochemistry, CCB-Center for Chemistry and Biomedicine, Leopold-Franzens University, Innrain 80-82, 6020 Innsbruck, Austria
Interests: vibrational spectroscopy; separation science; enrichment techniques; mass spectrometry
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jürgen Popp

E-Mail Website
Guest Editor
1. Leibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Albert Einstein Straße 9, 07745 Jena, Germany
2. Institute of Physical Chemistry, Abbe Center of Photonics (ACP), Friedrich Schiller University Jena, Helmholtzweg 4, 07743 Jena, Germany
Interests: biophotonics; vibrational spectroscopy; linear and non-linear Raman spectroscopy; multimodal nonlinear imaging; translational biophotonics; spectral histopathology; microbial diagnostics; AI-driven biomedical spectroscopy

Special Issue Information

Dear Colleagues,

Optical imaging is a type of imaging technique that uses visible, ultraviolet, and infrared light to create images of soft tissue without using any harmful radiation. Unlike X-rays, which require ionizing radiation, optical imaging is a safe and non-invasive way to monitor disease progression and treatment efficacy. This technique measures multiple properties of soft tissue, which makes it beneficial for detecting early metabolic changes that indicate abnormal organ and tissue functioning. By examining how soft tissues absorb and scatter light, researchers can use optical imaging to gain enhanced information about complex diseases. Optical imaging can also be combined with other imaging techniques, such as MRI or X-rays, to better understand the disease or tissue being studied. The Special Issue, entitled "Optical Imaging for Biomedical Applications," showcases recent advancements in developing, designing, modeling, implementing, and characterizing optical imaging techniques for biomedical applications. This Special Issue covers, but is not limited to, the following topics:

  • Microscopy;
  • Digital pathology;
  • Infrared spectroscopy;
  • Raman spectroscopy;
  • NIR spectroscopy;
  • Hyperspectral imaging;
  • Combinations with MRI, X-ray, CT, or micro-CT;
  • Advanced imaging techniques and systems;
  • Imaging-guided surgery and treatment;
  • Artificial intelligence-based computational imaging techniques for biomedical imaging;
  • Image processing.

Dr. Johannes Dominikus Pallua
Prof. Dr. Christian Huck
Prof. Dr. Jürgen Popp
Guest Editors

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. Bioengineering is an international peer-reviewed open access monthly 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 2700 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

  • microscopy
  • digital pathology
  • infrared spectroscopy
  • Raman spectroscopy
  • NIR spectroscopy
  • hyperspectral imaging
  • combinations with MRI, X-ray, CT, or micro-CT
  • advanced imaging techniques and systems
  • imaging-guided surgery and treatment
  • artificial intelligence-based computational imaging techniques for biomedical imaging
  • image processing

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

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17 pages, 9733 KiB  
Article
Raman Handheld Versus Microscopic Spectroscopy for Estimating the Post-Mortem Interval of Human Bones: A Comparative Pilot Study
by Johannes Dominikus Pallua, Christina Louis, Nicole Gattermair, Andrea Brunner, Bettina Zelger, Michael Schirmer, Jovan Badzoka, Christoph Kappacher, Christian Wolfgang Huck, Jürgen Popp, Walter Rabl and Claudia Wöss
Bioengineering 2024, 11(11), 1151; https://doi.org/10.3390/bioengineering11111151 (registering DOI) - 15 Nov 2024
Viewed by 13
Abstract
The post-mortem interval estimation for human skeletal remains is critical in forensic medicine. This study used Raman spectroscopy, specifically comparing a handheld device to a Raman microscope for PMI estimations. Analyzing 99 autopsy bone samples and 5 archeological samples, the research categorized them [...] Read more.
The post-mortem interval estimation for human skeletal remains is critical in forensic medicine. This study used Raman spectroscopy, specifically comparing a handheld device to a Raman microscope for PMI estimations. Analyzing 99 autopsy bone samples and 5 archeological samples, the research categorized them into five PMI classes using conventional methods. Key parameters—like ν1PO43− intensity and crystallinity—were measured and analyzed. A principal component analysis effectively distinguished between PMI classes, indicating high classification accuracy for both devices. While both methods proved reliable, the fluorescence interference presented challenges in accurately determining the age of archeological samples. Ultimately, the study highlighted how Raman spectroscopy could enhance PMI estimation accuracy, especially in non-specialized labs, suggesting the potential for improved device optimization in the field. Full article
(This article belongs to the Special Issue Optical Imaging for Biomedical Applications)
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21 pages, 11958 KiB  
Article
Deep Learning-Based Fine-Tuning Approach of Coarse Registration for Ear–Nose–Throat (ENT) Surgical Navigation Systems
by Dongjun Lee, Ahnryul Choi and Joung Hwan Mun
Bioengineering 2024, 11(9), 941; https://doi.org/10.3390/bioengineering11090941 - 20 Sep 2024
Viewed by 651
Abstract
Accurate registration between medical images and patient anatomy is crucial for surgical navigation systems in minimally invasive surgeries. This study introduces a novel deep learning-based refinement step to enhance the accuracy of surface registration without disrupting established workflows. The proposed method integrates a [...] Read more.
Accurate registration between medical images and patient anatomy is crucial for surgical navigation systems in minimally invasive surgeries. This study introduces a novel deep learning-based refinement step to enhance the accuracy of surface registration without disrupting established workflows. The proposed method integrates a machine learning model between conventional coarse registration and ICP fine registration. A deep-learning model was trained using simulated anatomical landmarks with introduced localization errors. The model architecture features global feature-based learning, an iterative prediction structure, and independent processing of rotational and translational components. Validation with silicon-masked head phantoms and CT imaging compared the proposed method to both conventional registration and a recent deep-learning approach. The results demonstrated significant improvements in target registration error (TRE) across different facial regions and depths. The average TRE for the proposed method (1.58 ± 0.52 mm) was significantly lower than that of the conventional (2.37 ± 1.14 mm) and previous deep-learning (2.29 ± 0.95 mm) approaches (p < 0.01). The method showed a consistent performance across various facial regions and enhanced registration accuracy for deeper areas. This advancement could significantly enhance precision and safety in minimally invasive surgical procedures. Full article
(This article belongs to the Special Issue Optical Imaging for Biomedical Applications)
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Review

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19 pages, 1445 KiB  
Review
Systematic Meta-Analysis of Computer-Aided Detection of Breast Cancer Using Hyperspectral Imaging
by Joseph-Hang Leung, Riya Karmakar, Arvind Mukundan, Pacharasak Thongsit, Meei-Maan Chen, Wen-Yen Chang and Hsiang-Chen Wang
Bioengineering 2024, 11(11), 1060; https://doi.org/10.3390/bioengineering11111060 - 24 Oct 2024
Viewed by 541
Abstract
The most commonly occurring cancer in the world is breast cancer with more than 500,000 cases across the world. The detection mechanism for breast cancer is endoscopist-dependent and necessitates a skilled pathologist. However, in recent years many computer-aided diagnoses (CADs) have been used [...] Read more.
The most commonly occurring cancer in the world is breast cancer with more than 500,000 cases across the world. The detection mechanism for breast cancer is endoscopist-dependent and necessitates a skilled pathologist. However, in recent years many computer-aided diagnoses (CADs) have been used to diagnose and classify breast cancer using traditional RGB images that analyze the images only in three-color channels. Nevertheless, hyperspectral imaging (HSI) is a pioneering non-destructive testing (NDT) image-processing technique that can overcome the disadvantages of traditional image processing which analyzes the images in a wide-spectrum band. Eight studies were selected for systematic diagnostic test accuracy (DTA) analysis based on the results of the Quadas-2 tool. Each of these studies’ techniques is categorized according to the ethnicity of the data, the methodology employed, the wavelength that was used, the type of cancer diagnosed, and the year of publication. A Deeks’ funnel chart, forest charts, and accuracy plots were created. The results were statistically insignificant, and there was no heterogeneity among these studies. The methods and wavelength bands that were used with HSI technology to detect breast cancer provided high sensitivity, specificity, and accuracy. The meta-analysis of eight studies on breast cancer diagnosis using HSI methods reported average sensitivity, specificity, and accuracy of 78%, 89%, and 87%, respectively. The highest sensitivity and accuracy were achieved with SVM (95%), while CNN methods were the most commonly used but had lower sensitivity (65.43%). Statistical analyses, including meta-regression and Deeks’ funnel plots, showed no heterogeneity among the studies and highlighted the evolving performance of HSI techniques, especially after 2019. Full article
(This article belongs to the Special Issue Optical Imaging for Biomedical Applications)
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15 pages, 2577 KiB  
Review
A Review of Medical Image Registration for Different Modalities
by Fatemehzahra Darzi and Thomas Bocklitz
Bioengineering 2024, 11(8), 786; https://doi.org/10.3390/bioengineering11080786 - 2 Aug 2024
Viewed by 1877
Abstract
Medical image registration has become pivotal in recent years with the integration of various imaging modalities like X-ray, ultrasound, MRI, and CT scans, enabling comprehensive analysis and diagnosis of biological structures. This paper provides a comprehensive review of registration techniques for medical images, [...] Read more.
Medical image registration has become pivotal in recent years with the integration of various imaging modalities like X-ray, ultrasound, MRI, and CT scans, enabling comprehensive analysis and diagnosis of biological structures. This paper provides a comprehensive review of registration techniques for medical images, with an in-depth focus on 2D-2D image registration methods. While 3D registration is briefly touched upon, the primary emphasis remains on 2D techniques and their applications. This review covers registration techniques for diverse modalities, including unimodal, multimodal, interpatient, and intra-patient. The paper explores the challenges encountered in medical image registration, including geometric distortion, differences in image properties, outliers, and optimization convergence, and discusses their impact on registration accuracy and reliability. Strategies for addressing these challenges are highlighted, emphasizing the need for continual innovation and refinement of techniques to enhance the accuracy and reliability of medical image registration systems. The paper concludes by emphasizing the importance of accurate medical image registration in improving diagnosis. Full article
(This article belongs to the Special Issue Optical Imaging for Biomedical Applications)
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