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Advances in Biomedical Imaging and Sensing: Technologies, Applications, and Future Directions

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

Deadline for manuscript submissions: closed (28 February 2026) | Viewed by 11101

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Special Issue Editors

Dr. Rizwan Ali Naqvi

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Guest Editor

Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Republic of Korea
Interests: medical 2D/3D image analysis; computer vision; computer aided diagnosis; applied AI; deep learning
Special Issues, Collections and Topics in MDPI journals

Dr. Abbas Jafar

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Guest Editor

Department of Computer Engineering, Gachon University, Seongnam 13120, Republic of Korea
Interests: machine learning; computer vision; performance optimization; medical imaging; explainable AI

Dr. Wazir Muhammad

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Guest Editor

Department of Physics, Charles E. Schmidt College of Science, Florida Atlantic University, Boca Raton, FL 33431-0991, USA
Interests: data mining; big data analytics; machine learning; AI in healthcare
Special Issues, Collections and Topics in MDPI journals

Dr. Fareed Ud Din

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Guest Editor

School of Science and Technology, The University of New England, Armidale, NSW 2350, Australia
Interests: artificial intelligence; machine learning; multi-agent systems; project management

Special Issue Information

Dear Colleagues,

Recent advances in biomedical imaging and sensing technologies have revolutionized healthcare by enabling non-invasive visualization and the precise quantification of physiological and pathological processes. These innovations play a pivotal role in disease diagnosis, treatment planning, and therapeutic monitoring.

This Special Issue of Sensors, titled "Advances in Biomedical Imaging and Sensing: Technologies, Applications, and Future Directions", invites original research articles, reviews, and short communications that contribute to the development and application of advanced imaging and sensing techniques in biomedical contexts. We aim to highlight cutting-edge research on imaging modalities (such as X-Rays, MRI, CT, PET, ultrasound, optical coherence tomography, and photoacoustic imaging) and sensing systems integrated with AI, robotics, or novel materials. Topics related to the development of imaging sensors, image reconstruction algorithms, data fusion strategies, deep learning frameworks, and real-time diagnostic tools are particularly welcome. Contributions that bridge the gap between engineering and clinical applications—ranging from early-stage prototypes to validated clinical tools—are encouraged.

Topics of interest include, but are not limited to, the following:

Magnetic resonance imaging (MRI) and computed tomography (CT);
Optical imaging and photoacoustic imaging;
Ultrasound and elastography imaging;
Positron emission tomography (PET) and SPECT;
Multimodal and hybrid imaging systems;
Smart biosensors and imaging sensors;
Ai and deep learning for biomedical image analysis;
Real-time medical image reconstruction;
Biomedical signal and image fusion;
Medical image segmentation, registration, and classification;
Image-guided interventions and therapy;
Wearable imaging devices and portable sensors;
Novel sensor materials and imaging hardware designs.

We welcome contributions from both academia and industry that address theoretical advances, practical applications, and case studies with real-world implications in biomedical imaging and sensing.

Dr. Rizwan Ali Naqvi
Dr. Abbas Jafar
Dr. Wazir Muhammad
Dr. Fareed Ud Din
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 250 words) can be sent to the Editorial Office for assessment.

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. Sensors 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

biomedical imaging
magnetic resonance imaging (MRI)
computed tomography (CT)
biomedical signal processing

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Published Papers (5 papers)

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Research

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20 pages, 13035 KB

Open AccessArticle

Development of Wideband Circular Microstrip Patch Antenna for Use in Microwave Imaging for Brain Tumor Detection

by Hüseyin Özmen, Mengwei Wu and Mariana Dalarsson

Sensors 2026, 26(7), 2062; https://doi.org/10.3390/s26072062 - 25 Mar 2026

Viewed by 784

Abstract

This work presents the design of a compact, wideband circular microstrip patch antenna for microwave imaging-based brain tumor detection. The main contribution is the development of a compact antenna structure incorporating enhanced ground-plane slot modifications, which significantly improves impedance bandwidth while maintaining a small electrical size, making it highly suitable for medical imaging systems. In addition, the study integrates antenna design, safety evaluation, and microwave imaging analysis within a unified framework to assess tumor localization feasibility using a realistic head model in CST Microwave Studio. The proposed antenna is fabricated on an FR-4 substrate with dimensions of 37 × 54.5 × 1.6 mm³, corresponding to an electrical size of 0.176λ × 0.260λ × 0.0076λ at the lowest operating frequency of 1.43 GHz. Ground-plane slot enhancements are introduced to achieve wideband performance, resulting in an impedance bandwidth from 1.43 to 4 GHz and a fractional bandwidth of 94.7%. The antenna exhibits a maximum realized gain of 3.7 dB. To evaluate its suitability for medical applications, specific absorption rate (SAR) analysis is performed using a realistic human head model at multiple antenna positions and at 1.5, 2.1, 2.5, 3.3, and 3.9 GHz frequencies. The computed SAR values range from 0.109 to 1.56 W/kg averaged over 10 g of tissue, satisfying the IEEE C95.1 safety guideline limit of 2 W/kg. For tumor detection assessment, time-domain simulations are conducted in CST Microwave Studio using a monostatic radar configuration, where the antenna operates as both transmitter and receiver at twelve angular positions around the head with 30° increments. The collected scattered signals are processed using the Delay-and-Sum (DAS) beamforming algorithm to reconstruct dielectric contrast maps and localize the tumor. It should be noted that the tumor-imaging demonstrations presented in this work are based on numerical simulations, while experimental validation is limited to the characterization of the fabricated antenna. Nevertheless, the findings indicate that the proposed antenna is a promising candidate for noninvasive, low-cost microwave brain tumor imaging applications. Full article

(This article belongs to the Special Issue Advances in Biomedical Imaging and Sensing: Technologies, Applications, and Future Directions)

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17 pages, 8581 KB

Open AccessArticle

A Fully Automated Deep Learning Pipeline for Anatomical Landmark Localization on Three-Dimensional Pelvic Surface Scans

by Woosu Choi and Jun-Su Jang

Sensors 2026, 26(6), 1760; https://doi.org/10.3390/s26061760 - 10 Mar 2026

Viewed by 468

Abstract

Accurate identification of anatomical landmarks on three-dimensional (3D) pelvic surface scans is essential for musculoskeletal assessment, yet manual procedures remain limited by operator dependence and soft tissue variability. This study presents a fully automated deep learning pipeline for localizing anatomical landmarks on the posterior pelvic region from raw 3D point cloud data. The pipeline integrates three modules: PelvicROINet for extracting the region of interest, PelvicAlignNet for rotation correction to standardize posture, and PelvicLandmarkNet for localizing six anatomical landmarks including the bilateral posterior superior iliac spines, bilateral iliac crests, L1, and L4. The models were trained independently with task-specific annotations and combined sequentially during inference. Under a subject-level split evaluation setting, the fully integrated system achieved a median error of 11.25 mm, demonstrating consistent localization performance across unseen subjects. Compared with manual landmark marking, the automated measurements showed improved within-visit repeatability, with reduced variability and higher intraclass correlation coefficients. The entire inference process required approximately three seconds per scan, supporting near real-time clinical applicability. These results indicate that the proposed modular framework enhances numerical consistency and robustness in surface-based pelvic landmark assessment and provides a scalable foundation for AI-assisted musculoskeletal evaluation and longitudinal monitoring. Full article

(This article belongs to the Special Issue Advances in Biomedical Imaging and Sensing: Technologies, Applications, and Future Directions)

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20 pages, 3247 KB

Open AccessArticle

Repeatability of Corneal Astigmatism and Equivalent Power with the MS-39 Tomographer Derived from Model Surface Fitting in a Cataractous Population

by Achim Langenbucher, Nóra Szentmáry, Alan Cayless, Muntadher Al Karam, Peter Hoffmann, Theo G. Seiler and Jascha Wendelstein

Sensors 2025, 25(19), 6171; https://doi.org/10.3390/s25196171 - 5 Oct 2025

Viewed by 976

Abstract

We investigated the repeatability of the MS-39 in determining power vector components—the spherical equivalent (SEQ) and astigmatic powers (C0 and C45) and asphericity (Q)—of corneal epithelium, stroma, and endothelium in a large patient cohort. In this retrospective cross-sectional single-centre study, we evaluated a dataset containing 600 MS-39 anterior segment tomography measurements from 200 eyes (three repeat measurements each) taken prior to cataract surgery. The exported measurements included height map data for the epithelium, stroma, and endothelium surface. Model surfaces (spherocylinder (SphCyl), cylindrical conoid (CylConoid), and biconic (Biconic), all in the 3/6 mm zone) were fitted using nonlinear iterative optimisation, minimising the height difference between the measurement and model. The mean (MEAN) and standard deviation (SD) for each sequence of measurements were derived and analysed. In the 3 mm and 6 mm zone, the MEAN SEQ was 53.47/53.56/53.57 and 53.21/53.54/53.54 D for SphCyl/CylConoid/Biconic for the epithelium, −4.47/−4.51/−4.51 and −4.45/−4.50/−4.50 D for the stroma, and −6.23/−6.26/−6.26 and −6.18/−6.29/−6.30 D for the endothelium. With the three surface models and the 3/6 mm zone, the SD for SEQ/C0/C45 was in the range of 0.04 to 0.11/0.05 to 0.13/0.04 to 0.11 D for epithelium; 0.01 to 0.02/0.01 to 0.05/0.01 to 0.06 D for stroma; and 0.01 to 0.02/0.02 to 0.07/0.03 to 0.07 D for endothelium. Fitting floating model surfaces with astigmatism to map data of the corneal epithelium, stroma, and endothelium seems to be a robust and reliable method for extracting equivalent power and astigmatism using all the datapoints within a region of interest. Full article

(This article belongs to the Special Issue Advances in Biomedical Imaging and Sensing: Technologies, Applications, and Future Directions)

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Review

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48 pages, 2867 KB

Open AccessReview

Generative Models for Medical Image Creation and Translation: A Scoping Review

by Haowen Pang, Tiande Zhang, Yanan Wu, Shannan Chen, Wei Qian, Yudong Yao, Chuyang Ye, Patrice Monkam and Shouliang Qi

Sensors 2026, 26(3), 862; https://doi.org/10.3390/s26030862 - 28 Jan 2026

Viewed by 1141

Abstract

Generative models play a pivotal role in the field of medical imaging. This paper provides an extensive and scholarly review of the application of generative models in medical image creation and translation. In the creation aspect, the goal is to generate new images based on potential conditional variables, while in translation, the aim is to map images from one or more modalities to another, preserving semantic and informational content. The review begins with a thorough exploration of a diverse spectrum of generative models, including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Diffusion Models (DMs), and their respective variants. The paper then delves into an insightful analysis of the merits and demerits inherent to each model type. Subsequently, a comprehensive examination of tasks related to medical image creation and translation is undertaken. For the creation aspect, papers are classified based on downstream tasks such as image classification, segmentation, and others. In the translation facet, papers are classified according to the target modality. A chord diagram depicting medical image translation across modalities, including Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Cone Beam CT (CBCT), X-ray radiography, Positron Emission Tomography (PET), and ultrasound imaging, is presented to illustrate the direction and relative quantity of previous studies. Additionally, the chord diagram of MRI image translation across contrast mechanisms is also provided. The final section offers a forward-looking perspective, outlining prospective avenues and implementation guidelines for future research endeavors. Full article

(This article belongs to the Special Issue Advances in Biomedical Imaging and Sensing: Technologies, Applications, and Future Directions)

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Other

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31 pages, 1368 KB

Open AccessSystematic Review

eXplainable Artificial Intelligence (XAI): A Systematic Review for Unveiling the Black Box Models and Their Relevance to Biomedical Imaging and Sensing

by Nadeesha Hettikankanamage, Niusha Shafiabady, Fiona Chatteur, Robert M. X. Wu, Fareed Ud Din and Jianlong Zhou

Sensors 2025, 25(21), 6649; https://doi.org/10.3390/s25216649 - 30 Oct 2025

Cited by 17 | Viewed by 7100

Abstract

Artificial Intelligence (AI) has achieved immense progress in recent years across a wide array of application domains, with biomedical imaging and sensing emerging as particularly impactful areas. However, the integration of AI in safety-critical fields, particularly biomedical domains, continues to face a major challenge of explainability arising from the opacity of complex prediction models. Overcoming this obstacle falls within the realm of eXplainable Artificial Intelligence (XAI), which is widely acknowledged as an essential aspect for successfully implementing and accepting AI techniques in practical applications to ensure transparency, fairness, and accountability in the decision-making processes and mitigate potential biases. This article provides a systematic cross-domain review of XAI techniques applied to quantitative prediction tasks, with a focus on their methodological relevance and potential adaptation to biomedical imaging and sensing. To achieve this, following PRISMA guidelines, we conducted an analysis of 44 Q1 journal articles that utilised XAI techniques for prediction applications across different fields where quantitative databases were used, and their contributions to explaining the predictions were studied. As a result, 13 XAI techniques were identified for prediction tasks. Shapley Additive eXPlanations (SHAP) was identified in 35 out of 44 articles, reflecting its frequent computational use for feature-importance ranking and model interpretation. Local Interpretable Model-Agnostic Explanations (LIME), Partial Dependence Plots (PDPs), and Permutation Feature Index (PFI) ranked second, third, and fourth in popularity, respectively. The study also recognises theoretical limitations of SHAP and related model-agnostic methods, such as their additive and causal assumptions, which are particularly critical in heterogeneous biomedical data. Furthermore, a synthesis of the reviewed studies reveals that while many provide computational evaluation of explanations, none include structured human–subject usability validation, underscoring an important research gap for clinical translation. Overall, this study offers an integrated understanding of quantitative XAI techniques, identifies methodological and usability gaps for biomedical adaptation, and provides guidance for future research aimed at safe and interpretable AI deployment in biomedical imaging and sensing. Full article

(This article belongs to the Special Issue Advances in Biomedical Imaging and Sensing: Technologies, Applications, and Future Directions)

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