Cardiovascular Engineering and Technology最新文献

{"title":"Sources of Variability in Intracranial Aneurysm Model Reconstruction and Evaluation: A Systematic Investigation.","authors":"Jared T Chong, Hang Yi, Alex E Wang, Cindy Ju, Luke Bramlage, Bryan Ludwig, Zifeng Yang","doi":"10.1007/s13239-026-00841-1","DOIUrl":"https://doi.org/10.1007/s13239-026-00841-1","url":null,"abstract":"<p><strong>Purpose: </strong>The intracranial aneurysm (IA) model reconstruction is critical for pathophysiology diagnosis and computational simulations. This study aimed to quantify the impact of segmentation thresholds and software platforms on the reconstruction of IA geometry as well as the impact of inter-user variability on the assessment of morphology.</p><p><strong>Methods: </strong>600 IA models were reconstructed from 100 patient DSA datasets using Materialise Mimics and 3D Slicer at three grey value (GV) thresholds; 1000, 1500, and 2500. Geometric measurements were performed in 3-matic by three users. Measurements included vessel diameters and aneurysm morphology parameters. Mimics, the 2500 GV threshold, and the most experienced user (R1) served as baselines for comparison. Normality was evaluated using Shapiro-Wilk tests, and statistical differences were assessed with paired t-tests and relative percent differences.</p><p><strong>Results: </strong>All anatomical regions showed statistically significant geometric variation across software and threshold. Model evaluation showed potential statistically significant variation between users. Models from 3D Slicer were consistently smaller than those from Mimics with percent differences ranging from - 1.27 to - 4.38% (all p < .05). Lower thresholds produced consistently larger models; decreasing from 2500 to 1000 GV increased average diameters by up to 15.9%, depending on specific region. User-related variability was most pronounced in the least experienced user, with size measurements deviating by up to 22.67% from the baseline.</p><p><strong>Conclusion: </strong>Segmentation software, threshold selection, and user interaction each introduce meaningful and statistically significant variability into IA model geometry and evaluation. Standardization of segmentation protocols-especially threshold values and operator training-is essential to improve reproducibility.</p>","PeriodicalId":54322,"journal":{"name":"Cardiovascular Engineering and Technology","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147845966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Vein Attempt? Experimental Models for Arteriovenous Fistula Research.","authors":"Nasir A Shah, Calvin D Li, Shannon D Thomas, Ramon L Varcoe, Jelena Rnjak-Kovacina, Carmine Gentile, Zoltan H Endre, Tracie J Barber, Jonathan H Erlich, Blake J Cochran","doi":"10.1007/s13239-026-00838-w","DOIUrl":"https://doi.org/10.1007/s13239-026-00838-w","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Chronic kidney disease (CKD) affects more than 10% of adults worldwide and is associated with rising mortality and increasing demand for hemodialysis. Hemodialysis requires durable vascular access, with surgically created arteriovenous fistulas (AVFs) being the preferred modality. However, up to 60% of AVFs fail to mature in time for clinical use, whereas others develop excessively high flow that can drive adverse cardiac remodeling and heart failure. These problems reflect incomplete understanding of the biological and biomechanical processes that govern AVF maturation and failure.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Aim: &lt;/strong&gt;To summarize the experimental models currently used to study AVF maturation and failure-including animal models, computational approaches and in vitro flow systems-and to compare their respective strengths, limitations and complementary roles in vascular access research. AVF maturation depends on coordinated arterial and venous remodeling in response to abrupt hemodynamic changes after anastomosis. Altered wall shear stress, pressure and cyclic strain activate endothelial and vascular smooth muscle signaling pathways that promote vasodilation, outward remodeling, matrix turnover and wall thickening. When these adaptive responses are blunted or dysregulated, neointimal hyperplasia, stenosis and access failure ensue. These biomechanical stimuli act within a pro-inflammatory, pro-oxidant uremic milieu, in which circulating toxins impair endothelial function, enhance oxidative stress and bias remodeling towards maladaptation. A broad spectrum of experimental platforms has been developed to interrogate these processes: animal models that recapitulate whole-organism physiology, computational models, including computational fluid dynamics and emerging fluid-structure interaction simulations, that resolve local hemodynamics and wall mechanics; conventional in vitro systems for controlled mechanobiology studies; and emerging microfluidic and macrofluidic devices that impose defined shear waveforms in physiologically relevant geometries. Each model captures selected spatial, temporal or biochemical dimensions of AVF biology, but each is constrained by trade-offs in scalability, fidelity, throughput or translational relevance.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;AVF maturation and failure arise from tightly coupled biomechanical and biological interactions, shaped by hemodynamic forces and uremia-related vascular dysfunction. No single experimental platform can encompass this complexity. Progress will depend on systematic comparison of available models and their deliberate integration into a coherent multimodal framework, in which insights from animal studies, computational simulations and in vitro flow systems are used in a complementary manner. Such an approach is essential to identify predictive biomarkers, clarify mechanisms of maladaptive remodeling and guide the rational design of targeted interventions to improve ","PeriodicalId":54322,"journal":{"name":"Cardiovascular Engineering and Technology","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147846042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Small-Diameter Tricuspid Valve Prosthesis for the Correction of Congenital Heart Defects Based on an Adult Donor Mitral Valve Homograft.","authors":"Anton Vladimirovich Tsaregorodtsev, Anna Grigorevna Erokhina","doi":"10.1007/s13239-026-00834-0","DOIUrl":"https://doi.org/10.1007/s13239-026-00834-0","url":null,"abstract":"<p><strong>Background: </strong>Tricuspid valve replacement is the preferred method when valve repair is not feasible. Stented xenobioprostheses, which limit the growth of the fibrous ring due to their rigid titanium frame and often have a short service life, are particularly problematic in children. An alternative approach is the implantation of a mitral valve homograft in the tricuspid position, which is more resistant to calcification. However, the limited availability of small-diameter homografts from adult donors restricts their use. To address this, a new technique for downsizing an adult mitral homograft was developed and tested in a wet lab and in silico (using 3D printing technologies).</p><p><strong>Methods: </strong>Two techniques for reducing the size of the mitral homograft were developed: one creating a single papillary muscle and the other creating two neo-papillary muscles. The hydrodynamics of the resulting prostheses were assessed using visual inspection and ultrasound evaluation.</p><p><strong>Results: </strong>It was demonstrated that reducing the mitral homograft by resecting the commissures, a portion of the anterior leaflet, and the posterior leaflet along with their subvalvular structures is a reliable method. This technique does not violate the congruence of the anterior and posterior leaflets and ensures optimal hydrodynamic parameters.</p><p><strong>Conclusions: </strong>The proposed technique makes it possible to downsize an adult mitral homograft to a \"pediatric\" size. It can be used clinically as an intraoperative adaptation technique, performed on the \"back table\" in 30-40 minutes.</p>","PeriodicalId":54322,"journal":{"name":"Cardiovascular Engineering and Technology","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147846030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Uncertainty Quantification of Finite Element Strain Predictions for a Nitinol Medical Device: Influence of Input Parameter Probability Distribution on Output Uncertainty.","authors":"Ian A Carr, Kenneth I Aycock, Harshad Paranjape, Craig Bonsignore, Jason D Weaver, Brent A Craven","doi":"10.1007/s13239-026-00837-x","DOIUrl":"https://doi.org/10.1007/s13239-026-00837-x","url":null,"abstract":"<p><p>Establishing credibility of computational modeling of medical devices is critical when an incorrect decision could cause patient harm. Uncertainty quantification (UQ) can impart credibility by providing an estimate of the model uncertainty due to variability in the input parameters. To perform UQ, non-deterministic simulations are performed where model inputs are represented by probability density functions (PDFs). Computational modeling of medical devices, however, is typically constrained by limited sample sizes and sparse experimental data. As a result, it is generally not possible to definitively characterize the true input distributions for UQ. The sparse data must instead be fit with an assumed PDF. While the assumption of a Gaussian distribution is common, other PDFs may be more appropriate depending on the context. In this study, we investigate the influence of input PDF choice on output uncertainty from non-deterministic finite element simulations of a nitinol medical device. We first characterize the geometry, material properties, and the experimental test conditions. We then perform a screening study to determine the relative importance of each parameter on our primary quantity of interest (QOI), peak strain amplitude. Next we perform three UQ studies, each using one of three different PDF types: Gaussian, gamma, and uniform. By sampling the input parameter PDFs using a Latin hypercube approach, we perform non-deterministic simulations to predict the output distributions. Our results show that use of uniform distributions yields output predictions with the largest variance and is thus the most conservative choice unless infrequent events are of interest. In contrast, we show that for conservative predictions of extreme events, a better choice is to use Gaussian or gamma distributions with asymptotic tails of finite probability that are neglected when using uniform distributions.</p>","PeriodicalId":54322,"journal":{"name":"Cardiovascular Engineering and Technology","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147846037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing Non-invasive Fractional Flow Reserve Estimation with Machine Learning-Enhanced 1D Hemodynamic Modeling.","authors":"Cyrus Tanade, Japneet Kaur Mavi, Guinevere Ferreira, Sam Schwaller, Amanda Randles","doi":"10.1007/s13239-026-00836-y","DOIUrl":"https://doi.org/10.1007/s13239-026-00836-y","url":null,"abstract":"<p><p>Patient-specific computational models exhibit strong concordance with invasively measured fractional flow reserve (FFR)-the clinical gold standard for diagnosing coronary ischemia. However, current modeling techniques frequently rely on computationally intensive assumptions such as pulsatile flow dynamics and often fail to optimally leverage patient-specific clinical data that is routinely available, limiting their practical clinical adoption. In this study, we propose a hybrid coronary angiography-based approach that reduces computational complexity through simplified steady-state flow assumptions, while simultaneously better leveraging available clinical information. Specifically, we integrate physics-based modeling with a machine learning (ML) feedback loop designed to refine and improve FFR predictions. We evaluated this hybrid framework using a retrospective two-center cohort comprising 132 patients with 132 coronary lesions. Our results demonstrate that steady-state models effectively capture essential hemodynamic patterns, closely matching pulsatile model predictions. The ML refinement step enhances diagnostic accuracy, yielding a sensitivity of 83.3%, specificity of 100.0%, positive predictive value of 100.0%, negative predictive value of 88.2%, and overall precision of 92.6%. By effectively combining efficient computational modeling with targeted ML-driven refinements, our approach represents a robust, clinically viable solution for accurate patient-specific FFR estimation.</p>","PeriodicalId":54322,"journal":{"name":"Cardiovascular Engineering and Technology","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147789382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mathematical Modeling of Photoplethysmography: Model Assessment and Validation.","authors":"Adrien Lefieux, Justine Daraize, Fabien Vergnet, Marina Vidrascu, Marie Willemet, Aniss Bendjoudi, Damiano Lombardi, Miguel A Fernández","doi":"10.1007/s13239-026-00826-0","DOIUrl":"https://doi.org/10.1007/s13239-026-00826-0","url":null,"abstract":"<p><strong>Purpose: </strong>Photoplethysmography (PPG) is a well-known technique employed to assess optically perfused bio-tissue volume changes. A PPG apparatus consists of a light emitter and a receptor. The analysis of the received light is used to infer properties of the illuminated tissues. This paper aims at presenting a novel distributed mathematical model for PPG signals, which combines a poroelastic model of tissue perfusion with a diffusion model for light absorption and scattering.</p><p><strong>Methods: </strong>We assume that the tissues undergo small deformations, allowing for a linear poroelastic description of perfusion. Since many PPG devices are applied to the fingertips (due to the rich vascularization in that area) we model the system specifically on the finger, with arterial blood pulse pressure serving as the primary perfusion driver. The numerical discretization of the governing equations is carried out using the finite element method. After calibration of the model, 216,000 simulations are performed with varying parameters (quasi-Monte Carlo approach). The aggregated results for two key biomarkers, AC/DC PPG amplitude ratio and pulse pressure, are compared against experimental PPG and pulse pressure measurements obtained from 20 volunteers.</p><p><strong>Results: </strong>Within the prescribed parameter ranges, numerical simulations successfully reproduce the AC/DC PPG amplitude biomarker in both the red and infrared wavelengths, with a few outliers observed for the green. Furthermore, in the vicinity of the combined red and infrared measured biomarkers, there are simulated biomarkers whose corresponding pulse pressures closely match the mapped-to-finger measured pulse pressure, with a difference of less than <math><mrow><mn>1</mn> <mspace></mspace> <mi>mmHg</mi></mrow> </math> .</p><p><strong>Conclusion: </strong>This work demonstrates the relevance of the proposed mathematical model for simulating PPG signals and highlights its potential for estimating tissue perfusion parameters, such as arterial pulse pressure.</p>","PeriodicalId":54322,"journal":{"name":"Cardiovascular Engineering and Technology","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147730662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Personalized Deep Networks for Enhanced ECG Segmentation.","authors":"Maylon Pereira Folli, Gabriel Tozatto Zago, Stephanie Rezende Alvarenga Moulin Mares, Rodrigo Varejão Andreão","doi":"10.1007/s13239-026-00835-z","DOIUrl":"https://doi.org/10.1007/s13239-026-00835-z","url":null,"abstract":"<p><p>Electrocardiography (ECG) plays a vital role in the diagnosis of cardiovascular diseases by analyzing the electrical activity of the heart. ECG semantic segmentation is a subfield focused on sample-wise delineation of ECG waveforms by assigning a physiological label to each time sample, enabling explicit estimation of clinically meaningful onset and offset boundaries. Recent advancements in deep learning have significantly improved ECG classification accuracy; however, the same has not yet been observed in automatic ECG segmentation. Existing models often lack explainability and adaptability to patient-specific variations, thereby reducing their generalizability. This study proposes a personalized deep neural network approach for enhanced ECG processing. This method incorporates convolutional neural networks (CNNs) and bidirectional long short-term memory (BiLSTM) networks, incorporating an attention mechanism to refine segmentation accuracy. A novel loss function is introduced to ensure smoother temporal transitions and better classification accuracy. The model was evaluated using the QT Database, demonstrating substantial improvements in P-wave and QRS delineation and in T-wave offset localization segmentation when fine-tuned for individual patients when fine-tuned and evaluated on held-out data from the same patient, demonstrating the benefit of intra-patient adaptation. Our results indicate that personalization improves delineation accuracy for challenging waveforms (notably P and T waves), supporting the potential of deep learning to better capture patient-specific morphology and providing a stronger basis for waveform-level, clinically interpretable ECG analysis.</p>","PeriodicalId":54322,"journal":{"name":"Cardiovascular Engineering and Technology","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147730731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mind the Gap: Human and AI Uncertainties in Cardiac MRI Segmentation.","authors":"Qiao Lin, Xin Chen, Chao Chen, Nikesh Jathanna, Peter P Swoboda, Shahnaz Jamil-Copley, Jonathan M Garibaldi","doi":"10.1007/s13239-026-00832-2","DOIUrl":"https://doi.org/10.1007/s13239-026-00832-2","url":null,"abstract":"<p><strong>Purpose: </strong>This study conducts quantitative and qualitative analyses to investigate the relationship between human-annotated and AI-derived uncertainty in cardiac MRI segmentation, aiming to enhance the reliability of AI-based cardiac MRI segmentation models and foster better human-AI collaboration.</p><p><strong>Methods: </strong>The CMRI dataset used in the experiments consists of 483 scans, each with two types of labels: an annotated segmentation mask and an uncertainty score per CMRI slice, both provided by clinicians. First, the AI-derived uncertainty estimated by the fuzzy-based algorithm is utilized to indicate the quality of segmentation. Multiple levels of uncertainties are derived from the method, including class-wise, slice-wise, subject-wise, etc. Subsequently, they are compared to the human-annotated uncertainty scores. Finally, qualitative analyses are conducted with clinicians to investigate all uncertainty measures potentially impacting real clinical applications.</p><p><strong>Results: </strong>Experimental results show a strong inverse correlation between AI-derived uncertainty and Dice score, a standard metric for segmentation quality, indicating that lower uncertainty predicts higher segmentation quality. Additionally, it is found that human-annotated uncertainty coincides with AI-derived uncertainty for some anatomical structures (e.g., papillary muscle). However, high human-annotated uncertainty does not necessarily correlate with low AI segmentation quality, and there is no obvious association between human-annotated uncertainty and the size of the segmented structure. Concluded from qualitative analysis with clinicians, humans are better at utilizing prior knowledge (e.g. cardiac structural and contextual information) for uncertainty scoring, while the current AI method lacks this capability and is mainly data-driven for decision-making and uncertainty estimation.</p><p><strong>Conclusion: </strong>AI-derived uncertainty could be utilized as quality control for CMRI segmentation. Humans utilize structural and contextual information to formulate uncertainty, while AI models currently lack this capability.</p>","PeriodicalId":54322,"journal":{"name":"Cardiovascular Engineering and Technology","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147624833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ECG and PPG Signals-Based Premature Ventricular Contraction Detection Methods: A Review, Key Challenges, and Future Directions.","authors":"Shailesh Mohine, Nabasmita Phukan, M Sabarimalai Manikandan, Ram Bilas Pachori","doi":"10.1007/s13239-026-00819-z","DOIUrl":"10.1007/s13239-026-00819-z","url":null,"abstract":"<p><p>Premature ventricular contraction (PVC) is a common cardiac arrhythmia, and its timely and automated detection is crucial for preventing life-threatening cardiovascular events and reducing clinical workload in long-term monitoring. This paper reviews state-of-the-art PVC detection methods based on electrocardiogram (ECG) and photoplethysmogram (PPG) signals, employing signal processing techniques, traditional machine learning, and deep learning approaches. The existing methods are categorized into three groups: threshold-based or heuristic-based techniques, conventional machine learning models, and deep learning frameworks. Additionally, the paper provides an overview of ECG and PPG signal databases and the benchmark metrics used for performance evaluation. Given that most methods utilize R-peak and systolic peak detection during preprocessing, we also review various preprocessing techniques for detecting R-peaks in ECG signals and systolic peaks in PPG signals. Based on the performance and key contributions of existing PVC detection methods, we highlight major challenges and future directions, considering the presence of various noise sources in ECG and PPG signals-particularly under ambulatory and exercise conditions-and the resource constraints of wearable or portable long-term ECG and/or PPG monitoring devices.</p>","PeriodicalId":54322,"journal":{"name":"Cardiovascular Engineering and Technology","volume":" ","pages":"119-139"},"PeriodicalIF":1.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146115197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Multi-Surface Microelectrode Catheter for Targeted Energy Delivery in Cardiac Radiofrequency Ablation: A Proof of Concept Based on Computational Simulation.","authors":"Yuqi Wu, Xingkai Ji, Tong Ren, Xiaomei Wu, Shengjie Yan","doi":"10.1007/s13239-025-00818-6","DOIUrl":"10.1007/s13239-025-00818-6","url":null,"abstract":"<p><strong>Purpose: </strong>Inefficient energy delivery to blood remains a primary challenge in cardiac radiofrequency ablation (RFA), limiting procedural efficacy. This paper introduces and computationally validates a novel multi-surface microelectrode catheter (MSMC) designed to enhance targeted energy delivery and improve overall procedural efficiency.</p><p><strong>Methods: </strong>A 3D multiphysics computational model coupling electrical, thermal, fluid-dynamic, and mechanical fields was developed to simulate RFA. The performance of the MSMC was systematically compared against a traditional catheter by analyzing its energy distribution and thermal lesion characteristics under both standard (10-18 W vs. 30 W) and high-power short-duration (25 W vs. 60 W) protocols, assessing the impact of varying catheter angles (vertical, 45°, and parallel), and exploring its potential for real-time lesion monitoring via impedance analysis.</p><p><strong>Results: </strong>The MSMC directed over 75% of its energy to the myocardium, a threefold improvement over the traditional catheter (~22%), allowing the creation of comparable lesions with 40% less power. The design demonstrated high stability across different orientations. Furthermore, analysis of its impedance characteristics via Cole-Cole plots revealed a greater sensitivity for real-time lesion monitoring compared to the traditional catheter.</p><p><strong>Conclusions: </strong>The MSMC's design, which synergizes a multi-surface electrode structure with a contact-based discharge strategy, enables more efficient and predictable lesion formation. This computational proof-of-concept study confirms its potential to improve the safety, efficacy, and real-time control of RFA procedures, offering a promising pathway for the development of next-generation therapeutic devices.</p>","PeriodicalId":54322,"journal":{"name":"Cardiovascular Engineering and Technology","volume":" ","pages":"206-222"},"PeriodicalIF":1.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145968053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}