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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: 31 July 2026 | Viewed by 13068

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Dr. Xiaojun Yu

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School of Automation, Northwestern Polytechnical University, Xi’an 710072, China
Interests: bio-optical imaging; biomedical image processing and analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biomedical imaging and signal processing stand at the forefront of medical innovation, providing unprecedented insights into human physiology and pathology. The continuous evolution of imaging technologies generates increasingly complex datasets, while sophisticated sensors capture a wide array of physiological signals. Effectively harnessing this wealth of information is crucial for enhancing diagnostic accuracy, tailoring personalized treatments, and improving patient monitoring. The true potential, however, lies in the synergistic application of advanced processing techniques to extract meaningful patterns from both images and signals.

This Special Issue, “Advanced Biomedical Imaging and Signal Processing”, aims to bring together leading research on cutting-edge methodologies that address the challenges and opportunities in acquiring, processing, analyzing, and interpreting complex biomedical data. We seek contributions that push the boundaries of current techniques, explore novel applications, and foster the integration of imaging and signal data for a more holistic understanding of health and disease.

We invite submissions of high-quality original research articles, comprehensive reviews, perspective pieces, and methodological papers. Topics of interest include, but are not limited to:

Novel Algorithms for Image Analysis:

Advanced image reconstruction techniques for MRI, CT, PET, SPECT, ultrasound, optical imaging (e.g., OCT, microscopy), and X-ray.
AI-driven image segmentation, registration, and feature extraction.
Quantitative imaging biomarkers and radiomics/radio-genomics.
Machine learning and deep learning approaches for image enhancement, classification, and interpretation.

Advanced Biomedical Signal Processing:

Sophisticated processing techniques (e.g., deep learning, statistical signal processing, source separation, time-frequency analysis, and nonlinear dynamics) applied to physiological signals.
Analysis of electrophysiological signals: EEG, MEG, ECG, EMG, and EOG.
Analysis of cardiovascular signals: blood pressure waveforms, PPG, and heart sounds (PCG).
Analysis of respiratory signals: airflow, respiratory effort, and SpO2.
Analysis of neuromechanical signals: motion capture, force/pressure sensor data.
Analysis of other physiological signals: GSR, body temperature, acoustic signals (e.g., lung sounds, cough sounds).
Wearable sensor data processing and artifact removal.
Real-time signal analysis for monitoring and feedback systems.

Integration and Multimodal Analysis:

Techniques for fusing information from multiple imaging modalities (e.g., PET-MRI, CT-Ultrasound).
Methods for integrating biomedical imaging data with physiological signals (e.g., combining fMRI with EEG, correlating imaging biomarkers with ECG features).
Development of computational models informed by both imaging and signal data.
AI and machine learning frameworks for multimodal data analysis in diagnostics, prognostics, and treatment response prediction.

Applications and Systems:

Development of novel imaging systems or sensor technologies coupled with advanced processing.
Application of advanced techniques in specific clinical areas (e.g., neurology, cardiology, oncology, ophthalmology).
Point-of-care diagnostic systems leveraging imaging and signal processing.
Computational tools and platforms for biomedical image and signal analysis.

We look forward to receiving your valuable contributions showcasing the latest advancements at the intersection of biomedical imaging and signal processing.

Dr. Xiaojun Yu
Guest Editor

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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-anonymized 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.

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Keywords

bio-optical imaging
biomedical image denoising
biomedical signal processing
computer-aided disease diagnosis
biomedical image segmentation techniques

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Published Papers (13 papers)

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Research

27 pages, 3062 KB

Open AccessArticle

Group-Aware Registration for Lesion-Level Quantitative Motion Correction in Respiratory-Gated PET/CT Biomedical Imaging

by Hui Zhou, Longxi He, Yangsheng Hu, Zhouyuan Qin, Feng Wang and Jianfeng He

Sensors 2026, 26(14), 4554; https://doi.org/10.3390/s26144554 (registering DOI) - 17 Jul 2026

Abstract

Respiratory-gated PET/CT generates phase-resolved biomedical imaging data, but respiratory motion can cause spatial mismatch and lesion-level quantitative instability, especially for small thoracoabdominal lesions. This study proposes SCAR-Net, a Similarity-Constrained Adaptive Respiratory Registration Network, for retrospective phase-to-reference correction of respiratory-gated ¹⁸F-FDG PET/CT. SCAR-Net treats motion correction as a quantitative stability problem in phase-resolved PET/CT rather than only a generic registration task. It combines sampled group-aware feature encoding with adaptive group-attentive modulation to represent structured respiratory deformation and enhance motion-sensitive correspondence. The method was evaluated using controlled respiratory simulations and a retrospective two-center clinical cohort of 100 patients, jointly assessing lesion-level SUV repeatability, spatial correspondence, image similarity, deformation plausibility, center-stratified performance with B-spline FFD as a conventional reference, cross-dataset testing without target-domain fine-tuning, ablation behavior, and computational efficiency. In the independent clinical test set, lesion-level evaluation included 20 patients, 43 independent PET-avid lesions, and 245 evaluable lesion-phase pairs. In small-lesion phase pairs, SCAR-Net reduced median phase-to-reference variability to 6.45% for |ΔSUV_max| and 4.73% for |ΔSUV_mean|, and increased median lesion Dice from 0.55 to 0.72. In large-lesion phase pairs, SCAR-Net achieved a post-correction Dice of 0.89 and remained competitive for SUV repeatability. Descriptive center-stratified analysis showed a consistent lesion-level performance pattern across the two clinical acquisition settings. These findings suggest that SCAR-Net can improve phase consistency and quantitative stability in respiratory-gated PET/CT, with the clearest benefit observed in small-lesion assessment. Downstream clinical endpoints, such as diagnostic accuracy, tumor staging, PERCIST-based response assessment, and patient outcomes, were not evaluated and require future prospective validation. Full article

(This article belongs to the Special Issue Advanced Biomedical Imaging and Signal Processing)

15 pages, 12656 KB

Open AccessArticle

Optical Coherence Tomography with Gapped Spectrum Using Sparse Iterative Covariance-Based Estimation

by Xiaonan Pan, Miao Yuan, Jianrui Zhang and Xiaojun Yu

Sensors 2026, 26(12), 3906; https://doi.org/10.3390/s26123906 - 19 Jun 2026

Viewed by 388

Abstract

Optical coherence tomography (OCT) is an optical imaging modality that provides high-resolution cross-sectional imaging of biological tissues noninvasively. In Fourier-domain OCT, axial resolution is governed by both the center wavelength and the spectral bandwidth of the light source; therefore, limited or discontinuous bandwidth degrades depth resolution and introduces sidelobes and artifacts in OCT images. To address these issues in OCT image reconstruction from gapped spectra, a sparse parameter estimation approach based on Sparse Iterative Covariance-based Estimation (SPICE) is proposed in this study. By utilizing a sparse parameter estimation framework to directly resolve depth-dependent components from discontinuous interferograms, SPICE enhances axial resolution while suppressing sidelobe artifacts inherent in standard interpolation. Experiments on multi-layered tape, oral epithelium, and finger skin show that SPICE visually suppresses gap-induced sidelobe artifacts and improves structural interpretability under representative gap conditions. Quantitative evaluations on multi-layer tape and biological tissues show that SPICE reduces axial FWHM by 30–45%, increases SSIM by 0.15–0.25, and achieves significantly lower computational cost than GAPES (p < 0.01). Full article

(This article belongs to the Special Issue Advanced Biomedical Imaging and Signal Processing)

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24 pages, 7276 KB

Open AccessArticle

Personalized Adaptive Gabor Filtering with Three-Stage Semi-Supervised Domain-Adversarial Learning for Cross-Subject SSVEP Decoding

by Junjun Guo, Xiaonan Pan, Ning Mi, Jianrui Zhang and Ting Huyan

Sensors 2026, 26(12), 3694; https://doi.org/10.3390/s26123694 - 10 Jun 2026

Viewed by 300

Abstract

Improving the decoding accuracy and information transfer rate (ITR) of steady-state visual evoked potential brain–computer interface (SSVEP-BCI) systems, while enhancing cross-subject generalization and reducing calibration cost, is essential for practical deployment. This study proposes an end-to-end framework that integrates adaptive filtering with semi-supervised domain adaptation. The framework incorporates a Gabor adaptive filter bank (G-AFB) to optimize time–frequency representations and extract features matched to individual neural responses. It also introduces a three-stage semi-supervised domain-adversarial neural network (TriS-DANN), which combines unsupervised pre-alignment and supervised fine-tuning to align cross-subject feature distributions and enable lightweight calibration. On the 1.0 s public benchmark dataset, G-AFB-tCNN achieved 89.13% accuracy, a 4.63 percentage-point improvement over its conventional filter-bank counterpart. On the 0.4 s in-house dataset, G-AFB-tCNN achieved 91.85% accuracy, a 3.22 percentage-point improvement over the conventional fixed filter bank. In transfer learning, TriS-DANN reached 86.60% accuracy using 0.4 s segments extracted from the stimulation period and only 23.07% of the available target-domain training/calibration trials, demonstrating higher efficiency and stability than conventional fine-tuning. These results support the proposed framework as a feasible route toward reliable, low-calibration SSVEP-BCI systems. Full article

(This article belongs to the Special Issue Advanced Biomedical Imaging and Signal Processing)

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19 pages, 1012 KB

Open AccessArticle

A Robust Multivariate Thresholding Function for Sparse and Biomedical Signal Reconstruction

by Hayat Ullah, Sunil Gaire and Corey A. Graves

Sensors 2026, 26(11), 3595; https://doi.org/10.3390/s26113595 - 5 Jun 2026

Viewed by 322

Abstract

This paper presents a computationally efficient Multivariate Mixture Model Thresholding (MMMT) technique for sparse signal denoising and recovery, with the goal of improving data quality in modern sensing and biomedical systems. The proposed method extends classical thresholding approaches by modeling nonzero signal coefficients using a multivariate Gaussian mixture prior, thereby capturing cross-channel and intercomponent dependencies commonly observed in multi-sensor and physiological signals. The thresholding rule is analytically derived through maximum a posteriori (MAP) estimation within a majorization–minimization (MM) optimization framework, while the associated model parameters are adaptively estimated using an expectation–maximization (EM) algorithm. Experimental results on noisy sinusoidal signals and synthetic ECG data demonstrate that MMMT consistently achieves higher correlation with ground-truth signals and improved preservation of pulse amplitude and morphological characteristics compared with benchmark methods, including the

l_{1}

-fused lasso and convex–non-convex (CNC) fused lasso. Quantitative evaluations based on correlation metrics, signal-to-noise ratio (SNR), and peak signal-to-noise ratio (PSNR) further confirm the effectiveness of the proposed approach. Owing to its scalability, robustness, and strong statistical interpretability, MMMT provides a promising framework for real-time ECG signal enhancement. Although the proposed framework is general and can be adapted to other biomedical modalities such as EEG, CT, and MRI, experimental validation in this study is limited to ECG signals. Full article

(This article belongs to the Special Issue Advanced Biomedical Imaging and Signal Processing)

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19 pages, 11502 KB

Open AccessArticle

PSAML: A Methodological Approach for Noninvasive Computerized Hydration Level Estimation

by Xin Liu, Xuezhao Kang, Liqun He, Jianrui Zhang, Huyan Ting and Xiaojun Yu

Sensors 2026, 26(11), 3362; https://doi.org/10.3390/s26113362 - 26 May 2026

Viewed by 325

Abstract

Hydration level (HL) is a critical physiological indicator of human health and functional status, and accurate HL monitoring is essential for applications in healthcare, sports, and wellness assessment. However, existing methods are either invasive and inconvenient or noninvasive but limited by system complexity and insufficient accuracy. To address these limitations, this study proposes a methodological approach for noninvasive computerized HL estimation based on galvanic skin response (GSR) signals, termed the PSAML approach, which integrates principal component analysis (PCA), successive decomposition index (SDI), and machine learning (ML) classifiers. A representative GSR dataset was collected from three healthy subjects under dehydrated, normal, and overhydrated states in sitting, standing, and posture-independent scenarios. After preprocessing, including outlier removal, Butterworth filtering, and time-window segmentation, conventional time-domain features were extracted and compared with PCA- and SDI-based representations. Six ML algorithms were used for classification. The results show that the conventional feature method achieved a maximum accuracy of 63.97%, whereas PCA-based feature reduction significantly improved performance, with PCA+SVM, PCA+LR, and PCA+LDA achieving accuracies above 99% in most cases. SDI-based features also demonstrated strong performance with suitable classifiers under smaller time windows. These findings demonstrate that the proposed PSAML approach provides an accurate and efficient solution for wearable noninvasive HL monitoring. Full article

(This article belongs to the Special Issue Advanced Biomedical Imaging and Signal Processing)

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19 pages, 4992 KB

Open AccessArticle

Research on Denoising Methods for Laser Doppler Blood Flow Signals Based on Time-Domain Noise Perception and DWT

by Quanxin Sun, Jie Duan, Hui Guo and Aoyan Guo

Sensors 2026, 26(5), 1500; https://doi.org/10.3390/s26051500 - 27 Feb 2026

Viewed by 541

Abstract

Addressing the challenges of composite noise (speckle noise, thermal noise, and random pulse interference) and non-stationarity in laser Doppler flow (LDF) signal processing, as well as the technical limitation of traditional threshold methods in balancing noise suppression and signal fidelity, this study proposes an adaptive denoising algorithm integrating temporal noise perception and discrete wavelet transform (DWT). A composite noise model is first established to characterize the interference. The signal undergoes a five-level DWT decomposition, where a local energy detection mechanism distinguishes signal-dominant from noise-dominant regions. An SNR-driven dynamic thresholding strategy is generated by combining inter-layer adaptive allocation with coefficient-level local weighting, followed by processing with an improved smoothing function to effectively suppress reconstruction artifacts. Simulations at a 1 dB input signal-to-noise ratio (SNR) yielded a 15.45 dB output SNR and a 0.05634 root mean square error (RMSE), outperforming traditional wavelet methods and modern benchmarks such as local variance and variational mode decomposition (VMD). Applied to a practical signal from an isolated vascular phantom with an initial SNR of

- 1.04

dB, the algorithm achieved a 13.86 dB output SNR and a 0.00258 RMSE. Results confirm the algorithm’s effectiveness for high-fidelity waveform capture in complex noise environments, offering a robust solution for vascular hemodynamic monitoring Full article

(This article belongs to the Special Issue Advanced Biomedical Imaging and Signal Processing)

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19 pages, 5786 KB

Open AccessArticle

Center of Pressure Measurement Sensing System for Dynamic Biomechanical Signal Acquisition and Its Self-Calibration

by Ni Li, Jianrui Zhang and Keer Zhang

Sensors 2026, 26(3), 910; https://doi.org/10.3390/s26030910 - 30 Jan 2026

Viewed by 558

Abstract

The development of highly dynamic bipedal robots demands sensing capable of capturing key contact-related signals in real time, particularly the Center of Pressure (CoP). CoP is fundamental for locomotion control and state estimation and is also of interest in biomedical applications such as gait analysis and lower-limb assistive devices. To enable reliable CoP acquisition under dynamic walking, this paper presents a foot-mounted measurement system and an online self-calibration method that adapts sensor scale and bias parameters during locomotion using both external foot sensors and the robot’s proprioceptive measurements. We demonstrate an online self-calibration pipeline that updates foot-sensor scale and bias parameters during a walking experiment on a NAO-V5 platform using a sliding window optimization. The reported results indicate improved within-trial consistency relative to an offline-calibrated reference baseline under the tested walking conditions. In addition, the framework reconstructs a digitized estimate of the vertical ground reaction force (vGRF) from load-cell readings; due to ADC quantization and the discrete offline calibration dataset, the vGRF signal may exhibit stepwise behavior and should be interpreted as a reconstructed (digitized) quantity rather than laboratory-grade continuous force metrology. Overall, the proposed sensing-and-calibration pipeline offers a practical solution for dynamic CoP acquisition with low-cost hardware. Full article

(This article belongs to the Special Issue Advanced Biomedical Imaging and Signal Processing)

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28 pages, 19149 KB

Open AccessArticle

Dynamic Thermography-Based Early Breast Cancer Detection Using Multivariate Time Series

by María-Angélica Espejel-Rivera, Carina Toxqui-Quitl, Alfonso Padilla-Vivanco and Raúl Castro-Ortega

Sensors 2025, 25(24), 7649; https://doi.org/10.3390/s25247649 - 17 Dec 2025

Cited by 2 | Viewed by 1487

Abstract

A computational approach for early breast cancer detection using Dynamic Infrared Thermography (DIT) was developed. Thermograms are represented by multivariate time series extracted from thermal hotspots in the breast, capturing five features: maximum and mean temperature, spatial heterogeneity, heat flux, and tumor depth, over 20 thermograms. Features are estimated based on the inverse solution of the Pennes bio-heat equation. Classification is performed using a Time Series Forest (TSF) and a Long Short-Term Memory (LSTM) network. The TSF achieved an accuracy of 86%, while the LSTM reached 94% accuracy. These results indicate that dynamic thermal responses under cold-stress conditions reflect tumor angiogenesis and metabolic activity, demonstrating the potential of combining multivariate thermographic sequences, biophysical modeling, and machine learning for non-invasive breast cancer screening. Full article

(This article belongs to the Special Issue Advanced Biomedical Imaging and Signal Processing)

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18 pages, 1070 KB

Open AccessArticle

Advancing Real-Time Polyp Detection in Colonoscopy Imaging: An Anchor-Free Deep Learning Framework with Adaptive Multi-Scale Perception

by Wanyu Qiu, Xiao Yang, Zirui Liu and Chen Qiu

Sensors 2025, 25(24), 7524; https://doi.org/10.3390/s25247524 - 11 Dec 2025

Cited by 3 | Viewed by 1107

Abstract

Accurate and real-time detection of polyps in colonoscopy is a critical task for the early prevention of colorectal cancer. The primary difficulties include insufficient extraction of multi-scale contextual cues for polyps of different sizes, inefficient fusion of multi-level features, and a reliance on hand-crafted anchor priors that require extensive tuning and compromise generalization performance. Therefore, we introduce a one-stage anchor-free detector that achieves state-of-the-art accuracy whilst running in real-time on a GTX 1080-Ti GPU workstation. Specifically, to enrich contextual information across a wide spectrum, our Cross-Stage Pyramid Pooling module efficiently aggregates multi-scale contexts through cascaded pooling and cross-stage partial connections. Subsequently, to achieve a robust equilibrium between low-level spatial details and high-level semantics, our Weighted Bidirectional Feature Pyramid Network adaptively integrates features across all scales using learnable channel-wise weights. Furthermore, by reconceptualizing detection as a direct point-to-boundary regression task, our anchor-free head obviates the dependency on hand-tuned priors. This regression is supervised by a Scale-invariant Distance with Aspect-ratio IoU loss, substantially improving localization accuracy for polyps of diverse morphologies. Comprehensive experiments on a large dataset comprising 103,469 colonoscopy frames substantiate the superiority of our method, achieving 98.8% mAP@0.5 and 82.5% mAP@0.5:0.95 at 35.8 FPS. Our method outperforms widely used CNN-based models (e.g., EfficientDet, YOLO series) and recent Transformer-based competitors (e.g., Adamixer, HDETR), demonstrating its potential for clinical application. Full article

(This article belongs to the Special Issue Advanced Biomedical Imaging and Signal Processing)

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16 pages, 61402 KB

Open AccessArticle

Low-Cost Spinning Disk Confocal Microscopy with a 25-Megapixel Camera

by Guy M. Hagen, Brian Lewis, Summer Levis, Joseph R. Hamilton and Tristan C. Paul

Sensors 2025, 25(23), 7183; https://doi.org/10.3390/s25237183 - 25 Nov 2025

Cited by 1 | Viewed by 2202

Abstract

Spinning disk confocal microscopy enables fast optical sectioning with low phototoxicity but is often inaccessible due to high hardware costs. We present a lower-cost solution using a 25-megapixel machine vision CMOS camera and a custom-built spinning disk. This camera uses a back-illuminated sensor with high quantum efficiency and low read noise. High-resolution images of Thy1-GFP mouse brain slices, Drosophila embryos and larvae, and H&E-stained rat testis verified performance across 3D tissue volumes. The measured resolution was 215.8 nm in X, Y and 521.9 nm in Z with a 60×/1.42 NA objective. The custom disk, made with 18 µm pinholes (180 µm pitch) on a chrome photomask and mounted to an optical chopper motor, enables stable, near-telecentric imaging at lower magnifications. Micromanager software integration allows synchronized control of all hardware, which demonstrates that affordable CMOS sensors can potentially replace sCMOS in spinning disk microscopy, offering an open-access, scalable solution for advanced imaging. Full article

(This article belongs to the Special Issue Advanced Biomedical Imaging and Signal Processing)

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17 pages, 8430 KB

Open AccessArticle

Robust Audio–Visual Speaker Localization in Noisy Aircraft Cabins for Inflight Medical Assistance

by Qiwu Qin and Yian Zhu

Sensors 2025, 25(18), 5827; https://doi.org/10.3390/s25185827 - 18 Sep 2025

Cited by 3 | Viewed by 1345

Abstract

Active Speaker Localization (ASL) involves identifying both who is speaking and where they are speaking from within audiovisual content. This capability is crucial in constrained and acoustically challenging environments, such as aircraft cabins during in-flight medical emergencies. In this paper, we propose a novel end-to-end Cross-Modal Audio–Visual Fusion Network (CMAVFN) designed specifically for ASL under real-world aviation conditions, which are characterized by engine noise, dynamic lighting, occlusions from seats or oxygen masks, and frequent speaker turnover. Our model directly processes raw video frames and multi-channel ambient audio, eliminating the need for intermediate face detection pipelines. It anchors spatially resolved visual features with directional audio cues using a cross-modal attention mechanism. To enhance spatiotemporal reasoning, we introduce a dual-branch localization decoder and a cross-modal auxiliary supervision loss. Extensive experiments on public datasets (AVA-ActiveSpeaker, EasyCom) and our domain-specific AirCabin-ASL benchmark demonstrate that CMAVFN achieves robust speaker localization in noisy, occluded, and multi-speaker aviation scenarios. This framework offers a practical foundation for speech-driven interaction systems in aircraft cabins, enabling applications such as real-time crew assistance, voice-based medical documentation, and intelligent in-flight health monitoring. Full article

(This article belongs to the Special Issue Advanced Biomedical Imaging and Signal Processing)

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29 pages, 2830 KB

Open AccessArticle

BCINetV1: Integrating Temporal and Spectral Focus Through a Novel Convolutional Attention Architecture for MI EEG Decoding

by Muhammad Zulkifal Aziz, Xiaojun Yu, Xinran Guo, Xinming He, Binwen Huang and Zeming Fan

Sensors 2025, 25(15), 4657; https://doi.org/10.3390/s25154657 - 27 Jul 2025

Cited by 10 | Viewed by 2281

Abstract

Motor imagery (MI) electroencephalograms (EEGs) are pivotal cortical potentials reflecting cortical activity during imagined motor actions, widely leveraged for brain-computer interface (BCI) system development. However, effectively decoding these MI EEG signals is often overshadowed by flawed methods in signal processing, deep learning methods that are clinically unexplained, and highly inconsistent performance across different datasets. We propose BCINetV1, a new framework for MI EEG decoding to address the aforementioned challenges. The BCINetV1 utilizes three innovative components: a temporal convolution-based attention block (T-CAB) and a spectral convolution-based attention block (S-CAB), both driven by a new convolutional self-attention (ConvSAT) mechanism to identify key non-stationary temporal and spectral patterns in the EEG signals. Lastly, a squeeze-and-excitation block (SEB) intelligently combines those identified tempo-spectral features for accurate, stable, and contextually aware MI EEG classification. Evaluated upon four diverse datasets containing 69 participants, BCINetV1 consistently achieved the highest average accuracies of 98.6% (Dataset 1), 96.6% (Dataset 2), 96.9% (Dataset 3), and 98.4% (Dataset 4). This research demonstrates that BCINetV1 is computationally efficient, extracts clinically vital markers, effectively handles the non-stationarity of EEG data, and shows a clear advantage over existing methods, marking a significant step forward for practical BCI applications. Full article

(This article belongs to the Special Issue Advanced Biomedical Imaging and Signal Processing)

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11 pages, 1898 KB

Open AccessCommunication

Simulation Design of an Elliptical Loop-Microstrip Array for Brain Lobe Imaging with an 11.74 Tesla MRI System

by Daniel Hernandez, Taewoo Nam, Eunwoo Lee, Yeji Han, Yeunchul Ryu, Jun-Young Chung and Kyoung-Nam Kim

Sensors 2025, 25(13), 4021; https://doi.org/10.3390/s25134021 - 27 Jun 2025

Cited by 3 | Viewed by 1164

Abstract

Magnetic resonance imaging (MRI) is a powerful medical imaging technique used for acquiring high-resolution anatomical and functional images of the human body. With the development of an 11.74 Tesla (T) human MRI system at our facility, we are designing novel radiofrequency (RF) coils optimized for brain imaging at ultra-high fields. To meet specific absorption rate (SAR) safety limits, this study focuses on localized imaging of individual brain lobes rather than whole-brain array designs. Conventional loop coils, while widely used, offer limited |B₁|-field uniformity at 500 MHz—the Larmor frequency at 11.74 T, which can reduce image quality. Therefore, it is important to develop antennas and coils for highly uniform fields. As an alternative, we propose an elliptical microstrip design, which combines the compact resonant properties of microstrips with the enhanced field coverage provided by loop geometry. We simulated a three-element elliptical microstrip array and compared its performance with a conventional loop coil. The proposed design demonstrated improved magnetic field uniformity and coverage across targeted brain regions. Preliminary bench-top validation confirmed the feasibility of resonance tuning at 500 MHz, supporting its potential for future high-field MRI applications. Full article

(This article belongs to the Special Issue Advanced Biomedical Imaging and Signal Processing)

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