Biomedical Physics & Engineering Express最新文献

{"title":"A multi-component polymeric bioink formulation developed using an in-house integrated bioextruder.","authors":"Siddhartha Dan, Gourav Kumar Sharma, Narendra Kumar, Mahesh Kumar Sah","doi":"10.1088/2057-1976/aeb12a","DOIUrl":"10.1088/2057-1976/aeb12a","url":null,"abstract":"<p><p>Advancements in 3D printing have revolutionized tissue engineering by enabling the fabrication of biomimetic scaffolds with greater precision and functionality. However, current bio-inks are often limited by suboptimal printability, insufficient mechanical stability, and a lack of comprehensive mechanistic understanding of structure-property-performance relationships. To address these challenges, the present study develops a novel multi-component bio-ink integrated with a custom bio-extrusion strategy to enhance scaffold fidelity and functional performance. The present study introduces a novel multi-component bio-ink formulation with polyvinyl alcohol (PVA), gelatin (Gel), sodium alginate (SA), and polyether ketone (PEK) that achieves a rare combination of optimal printability, mechanical robustness, and structural fidelity in 3D-printed constructs. The ink was prepared by dissolving in their respective solvents under controlled conditions. A customized bio-extruder head, integrated into a Creality Ender 3D printer, enabled precise ink deposition via a plunger-driven system with dynamic calibration for consistent material flow. This cost-effective modification enhances the accuracy of scaffold fabrication, offering new possibilities for regenerative medicine. Sixteen ink formulations were assessed based on their rheological behaviour, extrusion ability, layer adhesion, and mechanical performance. Composition 1, with an optimized balance of PVA (2.5%), Gel (0.5%), SA (0.25%), and PEK (0.5%), demonstrated superior printability, structural integrity, and mechanical stability, making it suitable for scaffold fabrication.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149126715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-driven motion compensation in HR-pQCT.","authors":"Paul Jürß, Tobias Knopp, Felix N von Brackel, Michael Amling, Björn Busse, Artyom Tsanda, Fynn Foerger, Martin Möddel, Marija Boberg","doi":"10.1088/2057-1976/aea857","DOIUrl":"10.1088/2057-1976/aea857","url":null,"abstract":"<p><p><i>Objective.</i>High-resolution peripheral quantitative computed tomography enables the assessment of bone mineral density and three-dimensional microstructure of peripheral limbs. Due to its long scanning time, it is especially susceptible to patient induced motion artifacts. The goal of this study is to compensate for such artifacts without requiring excessive computation time, while still avoiding the hallucination of erroneous structures.<i>Approach.</i>This work proposes a machine learning-based approach for the rapid estimation of rigid motion parameters directly from raw projection data. The model is trained on patient scans with motion simulated as a single, instantaneous jump. We demonstrate the performance and generalization of the method on simulated and experimentally acquired data, benchmarking our results against two recent baselines.<i>Main results.</i>The proposed method greatly reduced the amount of motion artifacts in both simulated and experimental data. On the simulated test data, the median structural similarity index measure (SSIM) for the reconstruction results could be improved from 0.62 to 0.88. Similarly, the SSIM for the experimental data improved from 0.58-0.71 to 0.74-0.78. At the tibia site that was used for training, motion artifacts were successfully reduced to such an extent that 92 % of previously inaccessible scans could be reconsidered for clinical evaluation. For the unseen radius site, a clinical recovery rate of 56 % could be achieved. Predicting the motion parameters for a measurement required less than 200 ms on consumer hardware.<i>Significance.</i>The proposed approach has the potential to enable the reuse of scans that were previously discarded for clinical evaluation while preventing generative hallucinations by design. Consequently, eliminating the need for repeated scans would reduce patient radiation exposure and clinical workflow overhead.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148981940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a Novel Non-Invasive Blood Pressure Measurement Technique Using Near-Infrared Spectroscopy and Deep Neural Networks.","authors":"Roya Kheyrkhah Shali, Seyed Kamaledin Setarehdan, Behjat Seifi","doi":"10.1088/2057-1976/aeb550","DOIUrl":"https://doi.org/10.1088/2057-1976/aeb550","url":null,"abstract":"<p><p>Non-invasive monitoring of blood pressure is essential for health management in daily life, particularly for elderly individuals and patients with autonomic dysfunction. However, conventional cuff-based sphygmomanometers are uncomfortable and provide discontinuous monitoring. In this study, we propose a novel non-invasive technique for blood pressure change assessment based on prefrontal near-infrared spectroscopy (NIRS) and deep neural networks. Unlike existing approaches that rely primarily on electrocardiography or photoplethysmography signals, the proposed method independently utilizes prefrontal NIRS signals to capture optical responses associated with postural changes. This enables early prediction of blood pressure changes following standing. NIRS is a non-invasive, real-time, portable, high temporal resolution, and cost-effective in comparison with other brain monitoring modalities. NIRS signals were collected from 40 healthy adults (aged 19-39 years) as test and training groups, 40 entirely new volunteers as an independent evaluation group, and during sit-to-stand transitions using a two-channel system, while reference blood pressure measurements were obtained using an automatic sphygmomanometer. An adaptive filtering framework was applied to suppress physiological noise, and multiple deep learning architectures were evaluated for regression analysis. A hybrid convolutional neural network-gated recurrent unit (CNN-GRU) model achieved the best performance, yielding root mean square errors of 1.95 mmHg and 1.75 mmHg for systolic and diastolic blood pressure, respectively. The results demonstrate that prefrontal NIRS combined with deep learning provides a reliable and accurate framework for non-invasive blood pressure change monitoring. This technique shows strong potential for wearable applications in elderly populations and individuals at risk of orthostatic hypotension, and it establishes a foundation for future clinical deployment.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149206513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of data augmentation techniques on deep transfer learning for heart sound classification.","authors":"Nour El Houda/ Gorine, Mohammed El Amine Moumene, Saliha Benkerdagh","doi":"10.1088/2057-1976/aeb4bf","DOIUrl":"https://doi.org/10.1088/2057-1976/aeb4bf","url":null,"abstract":"<p><p>Cardiovascular diseases remain one of the primary causes of death worldwide, highlight-&#xD;ing the importance of early detection. PhonoCardioGraphy (PCG) provides a non-invasive&#xD;approach to identify cardiac abnormalities. However, its effectiveness is limited by scarce&#xD;datasets and signal variability. Deep learning (DL) models have shown promising results,&#xD;but their performance strongly depends on the availability of sufficient training data. In&#xD;this study, PCG signals were converted into spectrogram representation, and four data&#xD;augmentation techniques were applied individually and in combination, followed by a sys-&#xD;tematic evaluation. A baseline convolutional neural network (CNN) and several transfer&#xD;learning models were trained on both original and augmented datasets. The results demon-&#xD;strate that combining augmentation techniques yields the best performance. The proposed&#xD;CNN achieved an accuracy of 94%, representing a 5.6% improvement over its performance&#xD;without data augmentation. In contrast, transfer learning such as VGG16, ResNet50, and&#xD;DenseNet121 showed improvements of 3.4%, 3.4%, and 2.2%, respectively. These findings&#xD;indicate that the proposed augmentation strategy effectively improves the classification per-&#xD;formance and generalization capability of deep learning models for heart sound classification.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149191246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design of silica-core gold-shell nanoparticles for photothermal breast tumor therapy: a two-arm comparison of surface cooling with nanoparticle self-attenuation.","authors":"Amirhossein Tavangar","doi":"10.1088/2057-1976/aeb427","DOIUrl":"https://doi.org/10.1088/2057-1976/aeb427","url":null,"abstract":"<p><p>We assemble a three-layer forward model for gold nanoshell photothermal therapy, core-shell Mie absorption, Pennes bioheat transport, and an Arrhenius thermal dose, each benchmarked against an external reference, and use it to maximize necrosis depth subject to a skin-temperature cap. The design variables are core radius, shell thickness, and irradiance at 808 nm; number density is derived per geometry from a fixed delivered gold mass anchored to a measured in vivo loading. The Beer-Lambert source deposits heat from both nanoparticle and native tissue absorption and includes self-attenuation, so the deposition shape depends on the design; at fixed geometry, the rise is exact in the source amplitude, so envelope inversion pins the safety cap in closed form. The search is a two-arm comparison differing only&#xD;in the skin boundary condition. For near-surface loading under a common skin-temperature constraint, the no-cooling control admits no viable therapy (necrosis 0), while contact cooling reaches 15.6 mm at a 15 ◦C skin surface. As a secondary, boundary-dependent observation, at each arm's own safety limit the cooled optimum is 3.6 mm deeper than a control that reaches depth only by heating the skin to about 83 ◦C. The control becomes viable only once a nanoparticle-free skin cap exceeds about 5.6 mm, its surface still at the 45 ◦C cap; cooling remains the deeper arm at every cap thickness tested. Methodologically, omitting self-attenuation makes the necrosis objective appear geometry-independent, whereas resolving it makes particle geometry a design lever comparable to cooling, though the favored geometry is a model-dependent trend rather than a located optimum. The headline depths derive from a homogeneous constant-perfusion model with a ubiquitous nanoparticle distribution rather than a tumor-localized one; the cap sweep relaxes that distribution but not the constant perfusion. All depths are theoretical upper bounds, not clinical predictions.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149179054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ultrasonic wave propagation in cancellous bone using Voronoi tessellation and elastodynamic finite integration technique.","authors":"Fouzia Ariche, Grimes Morad, Abdelkrim Boukabou","doi":"10.1088/2057-1976/aeb426","DOIUrl":"https://doi.org/10.1088/2057-1976/aeb426","url":null,"abstract":"<p><p>This study investigates ultrasonic wave propagation in cancellous bone using a numerical framework that combines Voronoi tessellation with the Elastodynamic Finite Integration Technique (EFIT). The proposed approach enables the generation of controlled trabecular-like microstructures while accounting for variations in porosity, seed density, trabecular thickness, and structural discontinuities. A two-dimensional computational model is employed as an initial framework to analyze the influence of microstructural parameters on fast and slow ultrasonic wave propagation under controlled conditions. The results demonstrate that porosity and trabecular network continuity significantly affect wave behavior, with increasing porosity leading to reduced fast-wave amplitude and velocity, while the slow-wave amplitude increases and its velocity remains relatively stable. These trends are consistent with previously reported theoretical predictions and experimental observations of ultrasonic propagation in cancellous bone. The proposed Voronoi-EFIT framework provides a computational approach for studying the relationship between trabecular microstructure and ultrasonic response, offering a valuable basis for quantitative ultrasound modeling and future investigations involving three-dimensional bone architectures.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149179272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"3D SE-ResUNet for accurate dose prediction in head and neck IMRT: a structure-specific ensemble approach.","authors":"Mohadeseh Zadnorouzi, Alireza Sadremomtaz","doi":"10.1088/2057-1976/aeadaa","DOIUrl":"10.1088/2057-1976/aeadaa","url":null,"abstract":"<p><p>Accurate three-dimensional dose prediction is important for automated treatment planning and radiotherapy in head and neck cancer. This study investigates an ensemble-based deep learning framework for dose prediction from computed tomography images and anatomical structures, with particular emphasis on structure-specific model weighting. A dataset of 340 head and neck cancer patients from the Open Knowledge-Based Planning dataset was used for model development and evaluation. Three three-dimensional convolutional neural network architectures, including U-Net, SE-UNet, and SE-ResUNet, were trained for dose prediction. Their predictions were first combined using a global weighted ensemble. To account for differences in prediction performance across anatomical structures, a structure-specific ensemble was subsequently developed. For each structure, the relative contribution of the three models was optimized independently on the validation set using an exhaustive grid search with weight increments of 0.1. Performance was evaluated using the Dose Score and dose-volume histograms (DVHs) Score. Among the individual models, SE-ResUNet achieved the lowest DVH Score (2.067 Gy), while the global weighted ensemble achieved a Dose Score of 2.77 Gy and a DVH Score of 2.068 Gy. The proposed structure-specific ensemble substantially reduced the DVH Score to 1.75 Gy, although its overall Dose Score increased slightly to 2.81 Gy. The proposed structure-specific ensemble provides an anatomy-aware strategy for combining complementary dose predictions from heterogeneous deep learning models. While it does not improve the overall Dose Score relative to the global weighted ensemble, it substantially improves the DVH Score and provides a more structure-aware characterization of dose prediction performance. The approach may therefore be useful for deep learning-based dose prediction in head and neck intensity-modulated radiation therapy.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149079542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dosimetric comparison of the anisotropic analytical algorithm and Acuros XB for single-fraction cranial stereotactic radiosurgery with a 6 MV flattening filter-free beam.","authors":"Kananan Utitsarn, Thepphithak Watthanasarn, Wirasinee Chaloemchawalit, Komkrit Krongkietlearts","doi":"10.1088/2057-1976/aeb0b1","DOIUrl":"10.1088/2057-1976/aeb0b1","url":null,"abstract":"<p><p>Stereotactic radiosurgery delivers an ablative single-fraction dose to small intracranial targets, and the reported dose depends on the calculation algorithm. This exploratory case series quantified the change in reported dosimetry when plans clinically finalised with the anisotropic analytical algorithm (AAA) were recalculated with Acuros XB (AXB) in dose-to-medium mode. Seven consecutive brain metastases in six patients, treated with 6 MV flattening filter-free volumetric modulated arc therapy, were recalculated on the same CT dataset using identical arc geometry, control points, multileaf collimator sequences and monitor units, without re-optimisation or renormalisation. Dose-volume histogram parameters were compared for the planning target volume (PTV), normal brain and organs at risk, and plan quality with the RTOG prescription-isodose-to-target-volume (PITV) ratio, the Paddick conformity index (PCI) and the gradient index (GI). Paired comparisons used exact two-sided Wilcoxon signed-rank tests and are interpreted as exploratory. AXB reported lower PTV dose metrics than AAA for the same deliverable plans, with mean D95% falling from 21.66 to 21.10 Gy, D98% from 21.19 to 20.58 Gy and Dmean from 24.58 to 24.13 Gy (all<i>p</i>= 0.016). The mean paired D98% difference was 3.00 ± 0.80% of the prescription dose. The PITV ratio fell from 1.19 to 1.10 and GI rose from 3.23 to 3.43 (both<i>p</i>= 0.016), whereas PCI did not differ (0.79 versus 0.82,<i>p</i>= 0.188). Normal-brain V12Gy and V10Gy were unchanged. Routine electronic portal imaging device dosimetry of the delivered plans gave a mean 2%/2 mm gamma passing rate of 98.5 ± 1.7%, documenting deliverability rather than validating either algorithm. These findings support careful review of target-dose metrics when an established AAA workflow is transitioned to AXB. The cohort is small and no independent reference dosimetry was available, so the study establishes neither clinical superiority nor an effect on tumour control or toxicity.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149103999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Magneto-acousto-electrical tomography using frequency-response-compensated nonlinearly frequency-modulated stimulation.","authors":"Zhishen Sun, Jinfu Liu, Meijia Li, Zhizhuo Cheng, Jianfei Wang, Kebin Jia","doi":"10.1088/2057-1976/aeada7","DOIUrl":"10.1088/2057-1976/aeada7","url":null,"abstract":"<p><p><i>Objective</i>. Magneto-acousto-electrical tomography (MAET) based on nonlinearly frequency-modulated (NLFM) signal stimulation achieves large dynamic range of detection while sacrifices a little longitudinal resolution (LR). However, NLFM signal stimulation is also negatively affected by the uneven frequency response in MAET, which results in widening of the main lobe width after pulse compression and ultimately leads to the degradation of LR in the MAET images. To further improve the LR of the MAET using NLFM signal stimulation, an MAET method using frequency-response-compensated nonlinearly frequency-modulated (FRC-NLFM) signal stimulation is proposed.<i>Approach</i>. Two types of nonlinear frequency-time curves are generated using the window function method and the piecewise linear frequency modulation method. The cubic spline interpolation method is employed to fit the nonlinear frequency response curves, which are used to generate the FRC-NLFM stimulation signals. Four groups of MAET experiments using respectively pure agar phantom samples and an<i>in vitro</i>multi-layer pork tissue agar phantom sample are conducted. Comparisons in terms of sensitivity, LR and dynamic range of detection are made among MAET using respectively spike, FRC-LFM, FRC-NLFM, and purely NLFM stimulation methods.<i>Main results</i>. MAET using FRC-NLFM stimulation improves LR over MAET using purely NLFM excitation, and has higher sensitivity and larger dynamic range over MAET based on FRC-LFM.<i>Significance</i>. This method forwards coded-excitation-based MAET to become an imaging method with high sensitivity, high LR and large dynamic range, and paves another way for the clinical application of the MAET.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149079603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Low-duty-cycle pulsed ultrasound for ex vivo liver tissue ablation: thermal safety, lesion conformity, and vascular structure preservation.","authors":"Xiao Li, Ke Wu, Zhenxin Shen, Yankun Yan, Weihong Meng, Dongya Yang, Hongyan Duan","doi":"10.1088/2057-1976/aeb425","DOIUrl":"https://doi.org/10.1088/2057-1976/aeb425","url":null,"abstract":"<p><strong>Objective: </strong>To investigate the physical mechanisms and engineering efficacy of low-duty-cycle pulsed ultrasound (histotripsy) in overcoming the heat sink effect and preserving major hepatic vessels.Impact Statement: This study provides a highly conformal, non-thermal ablation strategy that eliminates blood flow cooling effects while maintaining vascular integrity, offering superior precision over traditional methods.</p><p><strong>Introduction: </strong>Conventional thermal ablation for hepatocellular carcinoma is limited by the \"heat sink effect,\" causing marginal residual tumors and risking irreversible damage to major blood vessels. Quantitative investigations defining the thermal safety boundaries of histotripsy to overcome these limitations are currently lacking.</p><p><strong>Methods: </strong>Combining simulation optimization and ex-vivo validation, an acoustic-thermal coupled model identified an optimal protocol (2.0 MHz, 37 MPa, 0.5% duty cycle). Ex-vivo porcine liver experiments compared this cavitation ablation head-to-head with continuous-wave thermal ablation near major vessels, assessing morphology, Conformity Index (CI), and vascular integrity.</p><p><strong>Results: </strong>Simulations showed a 0.5% duty cycle kept peak temperatures below the 60°C denaturation threshold. Ex-vivo, the cavitation group achieved homogeneous liquefaction, sharp boundaries, and preserved adjacent vessels, yielding a 96.8 ± 2.1% CI. The thermal group suffered from the cooling effect, showing irregular defects and a lower CI of 71.2 ± 15.6% (P < 0.01).</p><p><strong>Conclusion: </strong>Low-duty-cycle pulsed ultrasound provides a safe, highly conformal non-thermal ablation solution that successfully overcomes the heat sink effect and preserves critical vascular architecture.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149179189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}