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Application of Optical Imaging in Medical and Biomedical Research

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 2557

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Special Issue Editor

Dr. Dongyu Li

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Guest Editor

School of Optical and Electronic Information-Advanced Biomedical Imaging Facility, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: in vivo optical imaging; light-field microscopy; multi-photon microscopy; tissue optical clearing; light therapy

Special Issue Information

Dear Colleagues,

Optical imaging has emerged as a cornerstone technology in bioanalysis research, offering the non-invasive, high-resolution, and real-time visualization of biological systems at the molecular, cellular, and tissue levels. Its applications span critical areas such as disease diagnosis, drug development, cellular dynamics monitoring, and biomarker discovery. Enabling the precise spatial and temporal mapping of biological processes, optical imaging can bridge the gap between fundamental biological insights and clinical translation, driving innovations in personalized medicine, point-of-care diagnostics, and advanced therapeutics. Integrating cutting-edge optical techniques with computational and analytical tools further amplifies its potential to address complex biomedical challenges with unprecedented accuracy and efficiency.

This Special Issue will highlight recent advancements and interdisciplinary synergies in optical imaging for bioanalysis. Topics of interest include but are not limited to the following: fluorescence imaging for molecular tracking and multiplexed detection; Raman imaging for label-free chemical analysis; scattering-based techniques such as optical coherence tomography (OCT) for structural and functional assessment; computational imaging methods that enhance resolution and overcome optical limitations; and artificial intelligence (AI)-driven approaches for automated image analysis, pattern recognition, and predictive modeling. Contributions exploring novel instrumentation, algorithm development, multimodal imaging integration, and translational applications in diagnostics or therapeutics are particularly encouraged.

We welcome the submission of comprehensive reviews and original research articles that advance developments in optical imaging and bioanalytical sciences. This issue aims to foster dialog among researchers in fields such as optics, bioengineering, computational sciences, and clinical medicine to accelerate the advancement of next-generation bioimaging solutions.

Dr. Dongyu Li
Guest Editor

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Keywords

optical imaging
optical coherence tomography
medical research
biomedical research

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Published Papers (4 papers)

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Research

19 pages, 623 KB

Open AccessArticle

A Unified AI-Driven Multimodal Framework Integrating Visual Sensing and Wearable Sensors for Robust Human Motion Monitoring in Biomedical Applications

by Qiang Chen, Xiaoya Wang, Ranran Chen, Surui Hua, Yufei Li, Siyuan Liu and Yan Zhan

Sensors 2026, 26(8), 2314; https://doi.org/10.3390/s26082314 - 9 Apr 2026

Viewed by 571

Abstract

This study proposes a unified multimodal temporal motion state perception framework for optical imaging-oriented biomedical applications, integrating visual skeleton sequences, inertial measurement unit (IMU) signals, and surface electromyography (EMG) signals. The framework utilizes modality-specific encoders and a cross-modal temporal alignment attention mechanism to explicitly model temporal offsets from heterogeneous sensing streams. A multimodal temporal Transformer backbone is introduced to capture long-range motion dependencies and cross-modal interactions, while an uncertainty-aware fusion module dynamically allocates weights based on modality confidence. Experimental results demonstrate that the proposed approach achieves an accuracy of 94.37%, an F1-score of 93.95%, and a mean average precision of 96.02%, outperforming mainstream baseline models. Robustness evaluations further confirm stable performance under visual occlusion and sensor noise. These results indicate that the framework provides a highly accurate and robust solution for rehabilitation assessment, sports training monitoring, and wearable intelligent interaction systems. Full article

(This article belongs to the Special Issue Application of Optical Imaging in Medical and Biomedical Research)

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25 pages, 649 KB

Open AccessArticle

A Multimodal Biomedical Sensing Approach for Muscle Activation Onset Detection

by Qiang Chen, Haofei Li, Zhe Xiang, Moxian Lin, Yinfei Yi, Haoran Tang and Yan Zhan

Sensors 2026, 26(6), 1907; https://doi.org/10.3390/s26061907 - 18 Mar 2026

Viewed by 386

Abstract

Muscle onset detection is a fundamental problem in electromyography signal analysis, human–machine interaction, and rehabilitation assessment. In medical and biomedical applications, slow muscle activation onset processes are widely encountered in scenarios such as rehabilitation training, postural regulation, and fine motor control. Such processes are typically characterized by slowly varying amplitudes, long temporal durations, and high susceptibility to noise interference, which poses significant challenges for accurate identification of onset timing. To address these issues, a lightweight temporal attention method for slow muscle activation onset detection is proposed and systematically validated under multimodal experimental settings. The proposed method takes surface electromyography signals as the primary input, while synchronously acquired optical motion image data are incorporated into the experimental design and result analysis, thereby aligning with the common joint use of optical imaging and physiological signals in medical and biomedical research. From a methodological perspective, the proposed framework is composed of lightweight temporal feature encoding, a slow activation-aware temporal attention mechanism, and noise suppression with stable decision strategies. Under the constraint of low computational complexity, the ability to model progressive activation signals is effectively enhanced. Experiments are conducted on a dataset containing multiple types of slow activation movements, and model performance is evaluated using five-fold cross-validation. The results demonstrate that under regular signal-to-noise ratio conditions, the proposed method significantly outperforms traditional threshold-based approaches, classical machine learning models, and several deep learning baselines in terms of onset detection accuracy, recall, and precision. Specifically, onset detection accuracy reaches approximately

92 %

, recall is around

90 %

, and precision is approximately

93 %

. Meanwhile, the average onset detection error and detection delay are reduced to about

41 ms

and

28 ms

, respectively, with the false positive rate controlled at approximately

2.2 %

. Stable performance is further maintained under different noise levels and cross-subject settings, indicating strong robustness and generalization capability. Full article

(This article belongs to the Special Issue Application of Optical Imaging in Medical and Biomedical Research)

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18 pages, 3650 KB

Open AccessArticle

Scattering Coefficient Estimation Using Thin-Film Phantoms with a Spectral-Domain Dental OCT System

by H. M. S. S. Herath, Nuwan Madusanka, Eun Seo Choi, Song Woosub, RyungKee Chang, GyuHyun Lee, Myunggi Yi, Jae Sung Ahn and Byeong-il Lee

Sensors 2026, 26(3), 815; https://doi.org/10.3390/s26030815 - 26 Jan 2026

Viewed by 539

Abstract

This study introduces a framework for estimating the optical scattering properties of thin-film phantoms using a custom-built Spectral-Domain Dental Optical Coherence Tomography (DEN-OCT) system operating within the 780–900 nm spectral range. The purpose of this work was to assess the performance of this system. The system exhibited high depth-resolved imaging performance with an axial resolution of approximately 16.30 µm, a signal-to-noise ratio of about 32.4 dB, and a 6 dB sensitivity roll-off depth near 2 mm, yielding an effective imaging range of 2.5 mm. Thin-film phantoms with controlled optical characteristics were fabricated and analyzed using Beer–Lambert and diffusion approximation models to evaluate attenuation behavior. Samples representing different tissue analogs demonstrated distinct scattering responses: one sample showed strong scattering similar to hard tissues, while the others exhibited lower scattering and higher transmission, resembling soft-tissue properties. Spectrophotometric measurements at 840 nm supported these trends through characteristic transmittance and reflectance profiles. While homogeneous samples conformed to analytical models, the highly scattering sample deviated due to structural non-uniformity, requiring Monte Carlo simulation to accurately describe photon transport. OCT A-scan analyses fitted with exponential decay models produced attenuation coefficients consistent with spectrophotometric data, confirming the dominance of scattering over absorption. The integration of OCT imaging, optical modeling, and Monte Carlo simulation establishes a reliable methodology for quantitative scattering estimation and demonstrates the potential of the developed DEN-OCT system for advanced dental and biomedical imaging applications. The innovation of this work lies in the integration of phantom-based optical calibration, multi-model scattering analysis, and depth-resolved OCT signal modeling, providing a validated pathway for quantitative parameter extraction in dental OCT applications. Full article

(This article belongs to the Special Issue Application of Optical Imaging in Medical and Biomedical Research)

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30 pages, 1726 KB

Open AccessArticle

A Sensor-Oriented Multimodal Medical Data Acquisition and Modeling Framework for Tumor Grading and Treatment Response Analysis

by Linfeng Xie, Shanhe Xiao, Bihong Ming, Zhe Xiang, Zibo Rui, Xinyi Liu and Yan Zhan

Sensors 2026, 26(2), 737; https://doi.org/10.3390/s26020737 - 22 Jan 2026

Viewed by 669

Abstract

In precision oncology research, achieving joint modeling of tumor grading and treatment response, together with interpretable mechanism analysis, based on multimodal medical imaging and clinical data remains a challenging and critical problem. From a sensing perspective, these imaging and clinical data can be regarded as heterogeneous sensor-derived signals acquired by medical imaging sensors and clinical monitoring systems, providing continuous and structured observations of tumor characteristics and patient states. Existing approaches typically rely on invasive pathological grading, while grading prediction and treatment response modeling are often conducted independently. Moreover, multimodal fusion procedures generally lack explicit structural constraints, which limits their practical utility in clinical decision-making. To address these issues, a grade-guided multimodal collaborative modeling framework was proposed. Built upon mature deep learning models, including 3D ResNet-18, MLP, and CNN–Transformer, tumor grading was incorporated as a weakly supervised prior into the processes of multimodal feature fusion and treatment response modeling, thereby enabling an integrated solution for non-invasive grading prediction, treatment response subtype discovery, and intrinsic mechanism interpretation. Through a grade-guided feature fusion mechanism, discriminative information that is highly correlated with tumor malignancy and treatment sensitivity is emphasized in the multimodal joint representation, while irrelevant features are suppressed to prevent interference with model learning. Within a unified framework, grading prediction and grade-conditioned treatment response modeling are jointly realized. Experimental results on real-world clinical datasets demonstrate that the proposed method achieved an accuracy of

84.6 %

and a kappa coefficient of

0.81

in the tumor-grading prediction task, indicating a high level of consistency with pathological grading. In the treatment response prediction task, the proposed model attained an AUC of

0.85

, a precision of

0.81

, and a recall of

0.79

, significantly outperforming single-modality models, conventional early-fusion models, and multimodal CNN–Transformer models without grading constraints. In addition, treatment-sensitive and treatment-resistant subtypes identified under grading conditions exhibited stable and significant stratification differences in clustering consistency and survival analysis, validating the potential value of the proposed approach for clinical risk assessment and individualized treatment decision-making. Full article

(This article belongs to the Special Issue Application of Optical Imaging in Medical and Biomedical Research)

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