International Journal of Biomedical Engineering and Technology (10 papers in press)

Regular Issues

• Simulation and design of an elliptical surface coil for small animal MRI at 3T  
  by Giulio Giovannetti, Benjamin Michael Hardy, Francesca Frijia, Alessandra Flori, Vincenzo Positano 
  Abstract: Custom-designed radiofrequency coils are commonly utilised in preclinical magnetic resonance imaging (MRI) to image small animals due to their cost-effectiveness and flexibility in adaptation to specific anatomical regions. Researchers frequently prefer such specialised coils for targeted metric assessment because their handcrafted nature allows for precise customisation. Rather than repeated experimental iterations, simulation-based refinement of coil architecture streamlines the design process. Numerical simulation methods offer more accurate estimations of signal-to-noise ratio (SNR) compared to magnetostatic models. The present study presents a comprehensive validation, using finite-difference time-domain (FDTD) full-wave simulation, of an elliptical radiofrequency (RF) coil tailored for small animal MRI applications. The approach incorporates calculations of coil and sample-induced resistances, inductance parameters, and magnetic field distributions under loading conditions with both phantom and whole-body mouse models. The accuracy of the simulation data is verified with data acquired from a transmit/receive elliptical coil prototype for a 3T MRI clinical scanner.
  Keywords: magnetic resonance; radiofrequency coils; inductance; magnetic field; resistance; signal-to-noise ratio; SNR.
  DOI: 10.1504/IJBET.2026.10078039
   
  
• Piezoelectricity in biomedical innovation: a systematic review of human-centric devices, applications, challenges and future directions  
  by Fatima Hassan, Taha Sana, Hamna Rana 
  Abstract: Piezoelectricity has emerged as a key mechanism in biomedical engineering, enabling localised electrical stimulation, sensing, and energy harvesting in human-centric devices. This systematic review analyses recent advances (2021-2025) in piezoelectric materials, device architectures, and biomedical applications, including tissue engineering, implantable and wearable systems, biosensors, neural interfaces, and drug delivery. Polymer-based materials such as PVDF exhibit superior flexibility and biocompatibility, whereas ceramic materials provide higher electromechanical efficiency but face limitations related to toxicity and mechanical mismatch. Despite their potential for self-powered operation and bioelectric modulation, clinical translation remains constrained by low power output, signal-to-noise limitations, material instability, and integration challenges with biological tissues. This review identifies material innovation, device miniaturisation, and system integration as key barriers to deployment, and highlights future directions toward lead-free nanomaterials, flexible hybrid electronics, and scalable biomedical applications.
  Keywords: piezoelectric biomaterials; implantable biomedical devices; bioelectric stimulation; self-powered sensing; flexible piezoelectric systems; clinical translation.
  DOI: 10.1504/IJBET.2026.10078103
   
  
• Leakage-Safe Case-Level Evaluation of ResNet50V2 for Benign-Malignant Liver Tumour Classification on CT Slices  
  by Smitha B, Vinod Kumar, Kumar S. S 
  Abstract: Binary (benign vs. malignant) liver tumour classification from computed tomography (CT) slices is often evaluated using slice-level splits that can introduce patient-level leakage. We present a leakage-safe, case-level evaluation of ResNet50V2 using stratified 5-fold cross-validation repeated over three random seeds. Slice probabilities were aggregated per Case_ID using mean probability (primary) and vote rate (secondary), with decision thresholds derived only from validation folds. Two preprocessing variants were assessed (BASIC and HE_GAUSS_CONTRAST). Across 15 runs, pooled case-level discrimination was AUC = 0.765 +_
  Keywords: leakage-safe evaluation; case-level cross-validation; liver tumour classification; computed tomography (CT); ResNet50V2; slice aggregation; calibration.
  DOI: 10.1504/IJBET.2026.10078321
   
  
• Structured Radiology Report Generation Using ViT-B/16 and Clinical-T5: A Multimodal Approach on IU X-Ray Dataset  
  by Nilam Khairnar, Shirish Sane 
  Abstract: This work presents a multimodal framework for generating structured radiology reports directly from the chest X-ray images. The model combines the Vision Transformer (ViT-B/16) to extract rich visual representations with Clinical-T5, which is a domain-tuned language model that interprets the clinical text using a co-attention module. The visual and textual streams interact to allow the system to link features of images with clinical descriptions that match the image. The decoder then generates well-structured reports containing Indication, Findings, and Impression sections. The framework was trained and evaluated on the IU X-Ray dataset and was found to have significant advances in the BLEU and ROUGE-L metrics, and strong CheXbert F1 results compared with previous methods. The results show that a combination of transformer-based vision and language models can generate coherent, interpretable, and clinically reliable radiology reports, highlighting the importance of multimodal learning for automated radiology report generation.
  Keywords: BLEU; Clinical T5; NLP; Radiology Report Generation; ROUGE; Vision Transformer.
  DOI: 10.1504/IJBET.2026.10078324
   
  
• Experimental Analysis on Effect of Type-2 Diabetic Mellitus: on Strength Behaviour of Trabecular Bone Structures  
  by Swapnil S. Barekar, Tushar A. Jadhav, Navin Kumar 
  Abstract: The type-2 diabetes affected bones are more susceptible to fragile fractures due to the deterioration in bone mineral density (BMD). This study aims to investigate the structural integrity of human trabecular bone by comparing nanoindentation-based mechanical evaluation outcomes of three diabetic and two non-diabetic bone samples. The obtained results stated that a 45-year-old T2DM sample has lower modulus of elasticity (40.81%), toughness (34.24%) and diminished bone quality when compared to normal bones. Mechanical analysis shows that T2DM causes mean reductions of the elastic modulus by 5.3 GPa, stiffness to 4.9 N/nm, and hardness by 0.27 GPa in bones. The reverse problem-solving method also effectively approximates yield stress, yielding results comparable to direct calculations that eliminate the requirement for another destructive testing. The findings also highlight the significance of multi-parametric analysis in fracture-risk assessment strategies, other than focusing only on bone mineral density for individuals with T2DM.
  Keywords: Bone Defects; Trabecular Bone; T2DM; Berkovich Nano Indentation; Elastic Modulus; Yield Strength.
  DOI: 10.1504/IJBET.2026.10078458
   
  
• Anatomical-Prior-Based Polar Regression Method for Intravascular Ultrasound Contour Delineation  
  by Yu Xu, Liu Xun, Lin Yutao, Tu ShengXian 
  Abstract: Accurate delineation of the lumen and external elastic lamina in intravascular ultrasound images is critical for quantitative vascular analysis but remains challenging due to noise, artifacts, contour ambiguity, and limited temporal consistency. This study proposes a contour delineation framework that is better aligned with the radial imaging mechanism of IVUS and the closed-topology geometry of vascular contours. Radius-aware circular positional encoding is used to capture the intrinsic circular vessel topology, and a dual-branch architecture with radial and global pathways enables attention-based multi-scale feature fusion. A temporal-spatial smoothing constraint is further incorporated during training to enhance inter-frame consistency. Experiments on a multi-centre IVUS dataset comprising 112 pullback examinations from 58 patients (43,867 frames) demonstrate that the proposed method outperforms state-of-the-art approaches across multiple evaluation metrics, achieving accurate and sequence-consistent contour delineation.
  Keywords: Intravascular ultrasound; Deep learning; Contour regression; Atherosclerosis.
  DOI: 10.1504/IJBET.2026.10079205
   
  
• Multi-Head Batch Attention-Enabled Dual Learning-Based Deep Spiking Convolutional Neural Network Model for Epileptic Seizure Detection  
  by Pankaj Kunekar, Puja Cholke, Rajnikant B. Wagh, Jayshri Sonawane, Vishal Thakare, Atul S. Chaudhari 
  Abstract: The Suzuki-Miyaura cross-coupling reaction remains one of the most widely used C-C bond-forming transformations in synthetic chemistry. Machine learning models promise to accelerate reaction optimisation, yet systematic benchmarking across different catalytic systems remains limited. Here, we develop and validate a comprehensive machine learning framework for predicting reaction yields across five metal catalysts (Pd, Ni, Ru, Fe, Cu) using a dataset of 5,760 reactions modelled on high-throughput experimentation platforms. Our XGBoost model achieves R2 = 0.903 (RMSE = 6.10%), substantially outperforming transformer-based YieldBERT (R2 = 0.81, RMSE = 11%) while approaching graph neural network performance with dramatically lower computational costs. Systematic catalyst comparison reveals that nickel catalysis achieves superior performance (46.7% mean yield, 42% success rate) compared to conventional palladium (45.8% mean yield, 40% success rate), particularly for challenging chloride electrophiles.
  Keywords: Epileptic Seizure Detection; Electroencephalography; Dual Learning; Multi-Head Batch Attention; Spiking Network.
  DOI: 10.1504/IJBET.2026.10079561
   
  
• A preliminary report: Examining the effects of varying pressure levels of blood flow restriction cuffs during acute exercise on four collegiate-level athletes  
  by Janaya Battles, Dan Swier, Rajat Emanuel Singh 
  Abstract: Blood flow restriction (BFR) is a rehabilitation technique used in physical therapy to improve muscle growth. This technique involves using BFR cuffs on the limbs to occlude blood flow, limiting the buildup of muscle growth inhibitors. While current research addresses growth inhibitor-related changes, it is limited regarding BFR cuff pressures impact on neuro-muscular coordination at constant exercise loads. This preliminary report examined how varying BFR cuff pressure influences muscle activation and heart rate. We recruited four collegiate athletes and recorded heart rate and surface electromyography data from the vastus medialis, rectus femoris, and vastus lateralis during bodyweight squats. Pressure levels ranged from 0 to 350 mmHg. Results showed a positive relationship between heart rate and pressure. However, we found no correlation between pressure and independent muscle activation, nor a consistent correlation between pressure and muscle activation symmetry between legs during bodyweight squats.
  Keywords: Blood Flow Restriction cuffs; high pressure levels; muscle activation; heart rate.
  DOI: 10.1504/IJBET.2026.10079573
   
  
• A Template-based One-Dimensional Gaussian Filter with Down-sampling for ECG Signal Denoising  
  by Tongnan Xia, Fu Yan, Bei Wang, Enruo Huang, Laiwu Zhang, Guoqiang Lou, Ming Liu, Yaojie Sun 
  Abstract: Noise reduction is a critical step in electrocardiogram (ECG) preprocessing, as it directly affects the accuracy of subsequent diagnoses and analyses. This study proposes a template-based one-dimensional Gaussian filter derived from a two-dimensional spatial-domain Gaussian kernel using a structured down-sampling strategy. Unlike conventional one-dimensional Gaussian filters, the proposed method leverages the probabilistic structure of the Gaussian distribution to enhance denoising performance while maintaining low computational complexity. Experiments on synthetic signals with multiple noise types, together with ECG recordings from the MIT-BIH Arrhythmia Database and the MIT-BIH noise stress test database, demonstrate consistent improvements in signal-to-noise ratio (SNR) across a range of noise conditions. Comparative evaluations further show that, under identical kernel configurations, the proposed method achieves higher SNR values than MATLABs built-in Gaussian filter while preserving waveform fidelity. Morphology-oriented metrics, including percent root-mean-square difference (PRD), correlation coefficients, and R-peak amplitude errors, together with paired-sample t-test analysis, confirm that the proposed filter effectively suppresses noise without introducing systematic amplitude bias or notable waveform distortion. These results indicate that the proposed approach offers a practical, lightweight, and reliable solution for real-time ECG denoising and other one-dimensional biomedical signal-processing applications, particularly in resource-constrained environments.
  Keywords: signal denoising; gaussian filter; electrocardiogram (ECG); signal-to-Noise ratio (SNR); noise reduction algorithms.
  DOI: 10.1504/IJBET.2026.10079884
   
  
• Systematic Review on Engineered Biomaterials for Cardiac Regeneration and Repair: From Emerging Technologies to Translational Barriers  
  by Jahanzeb Sheikh, Nabeeha Sahar, Kah Meng Leong, Jawad Shafique, Sidra Abid Syed, Tan Tian Swee, Syafiqah Saidin, Madeeha Sadia, Maheza Irna Mohamad Salim, Jose Javier Serrano, Umme Rubab 
  Abstract: Cardiovascular diseases (CVDs) remain the leading cause of death worldwide with limited regenerative capacity of adult cardiomyocytes posing a critical barrier to effective treatment. Traditional therapies such as pharmacological interventions, surgical revascularisation and organ transplantation provide symptomatic relief or functional stabilisation but fall short in reversing myocardial damage or promoting tissue regeneration. On the opposite end of the spectrum, engineered biomaterials represent a transformative paradigm in cardiac tissue engineering by providing structural support biochemical cues and electromechanical integration. These materials serve not only as scaffolds for cell delivery and retention but also as dynamic platforms to enhance angiogenesis, modulate inflammation and restore electrical conductivity. This review provides a comprehensive overview of current biomaterial strategies for cardiac regeneration including hydrogels, cardiac patches, stents and organoid platforms and classifies them by composition and functionality. Furthermore, it critically discusses the challenges in clinical translation such as immune responses, mechanical mismatch and delivery limitations. Finally, the review explores emerging technologies including 3D and 4D bioprinting, smart and stimuli responsive materials and extracellular vesicle-based the
  Keywords: Cardiovascular diseases; Cardiac tissue engineering; Biomaterials; Myocardial regeneration; Hydrogels; Cardiac patches; Stents; Angiogenesis; Clinical translation; Regenerative medicine.