Biomedical Physics & Engineering Express最新文献

{"title":"Cardiac and respiratory signal separation in electrical impedance tomography (EIT).","authors":"M N Hidayah, M R Baidillah, S Suharyana","doi":"10.1088/2057-1976/aea425","DOIUrl":"10.1088/2057-1976/aea425","url":null,"abstract":"<p><p>Electrical impedance tomography (EIT) is a promising non-invasive imaging modality for respiratory and cardiac monitoring. However, separating cardiac signals from the dominant respiratory component remains challenging because both physiological processes are simultaneously embedded in the measured impedance signals. This study investigates the influence of EIT measurement patterns on cardiac-respiratory signal separation using four methods: continuous wavelet transform, empirical mode decomposition, independent component analysis, and principal component analysis (PCA). Numerical simulations were performed in EIDORS using a realistic thoracic geometry reconstructed from the POPI dataset with a GREIT-based image reconstruction framework. Four measurement configurations were evaluated: adjacent-adjacent, adjacent-Skip 4, Skip 4-adjacent, and Skip 4-Skip 4. Signal separation performance was assessed using the Cross Correlation (CC) between the extracted and reference signals, while the normalized root mean square error was additionally used to evaluate the reconstructed cardiac waveform. Among all evaluated methods, PCA consistently achieved the highest waveform similarity, reaching a Lung CC of 98.12% and a Heart CC of 99.56% under the Skip 4-Skip 4 measurement pattern. Visual inspection of the reconstructed images further demonstrated clearer separation of respiratory and cardiac conductivity distributions using this configuration. These results indicate that combining PCA with a wide electrode measurement pattern improves cardiac-respiratory signal separation in simulated EIT measurements.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148910548","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":"HARU-Net: hybrid attention residual U-Net for edge-preserving denoising in cone-beam computed tomography.","authors":"Khuram Naveed, Ruben Pauwels","doi":"10.1088/2057-1976/ae9c46","DOIUrl":"10.1088/2057-1976/ae9c46","url":null,"abstract":"<p><p><i>Objectives.</i>Cone-beam computed tomography (CBCT) is widely used in dental and maxillofacial imaging, but low-dose acquisition introduces strong, spatially varying noise that degrades soft-tissue visibility and obscures fine anatomical structures. Classical denoising methods struggle to suppress noise in CBCT while preserving edges. Although deep learning (DL)-based approaches offer high-fidelity restoration, their use in CBCT denoising is limited by the scarcity of high-resolution CBCT data for supervised training. This study aims to develop an efficient DL framework for high-quality CBCT denoising that effectively suppresses noise while preserving fine anatomical structures and maintaining computational efficiency for practical clinical deployment.<i>Approach.</i>To achieve this objective, we propose a novel Hybrid Attention Residual U-Net (HARU-Net) for high-quality denoising of CBCT data, trained on a cadaver dataset of human hemimandibles acquired using a high-resolution protocol of the 3D Accuitomo 170 (J. Morita, Kyoto, Japan) CBCT system. The novel contribution of this approach is the integration of three complementary architectural components: (i) a hybrid attention transformer block embedded within each skip connection to selectively emphasize salient anatomical features, (ii) a residual hybrid attention transformer group at the bottleneck to strengthen global contextual modeling and long-range feature interactions, and (iii) residual learning convolutional blocks to facilitate deeper, more stable feature extraction throughout the network.<i>Results.</i>HARU-Netconsistently outperforms state-of-the-art methods achieving the highest peak signal-to-noise ratio (37.52 dB), the second-highest SSIM (0.9557), and the lowest GMSD (0.1084). Compared with transformer-based methods, the proposed network achieves superior denoising performance while maintaining substantially lower computational complexity.<i>Conclusion.</i>The proposed HARU-Net provides an effective balance between noise suppression, anatomical structure preservation, and computational efficiency. These characteristics make it a promising and practical solution for improving image quality and supporting reliable diagnosis in low-dose CBCT imaging.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148787919","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":"Fluoride-reinforced 50S8P sol-gel bioactive glass for potential enamel remineralisation.","authors":"Qichen Sun, Siti Noor Fazliah Mohd Noor, Siti Fatimah Samsurrijal, Nawal Radhiah Abdul Rahman","doi":"10.1088/2057-1976/ae9ffc","DOIUrl":"10.1088/2057-1976/ae9ffc","url":null,"abstract":"<p><p>Bioactive glasses (BGs) are widely used in various fields of dentistry, and reinforcing these BGs with fluoride is of particular interest for the remineralisation of dental hard tissue. In this study, fluoride-reinforced 50S8P BGs (BGF and mBGF) were prepared using the sol-gel method at different ageing temperatures. The BGs were manually ground into powder, sieved and characterised using thermogravimetric analysis (TGA) and differential scanning calorimetry ( (TGA-DSC), X-ray diffraction (XRD), X-ray Fluorescence (XRF), Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy with energy-dispersive spectroscopy (SEM-EDS). The fluoride-reinforced BG (BGF and mBGF) exhibited a greater mass loss based on TGA, whilst DSC analysis showed that the addition of fluoride reduced transition temperature, crystallisation temperature and melting temperature. XRD analysis revealed that fluoride incorporation led to the formation of Na₂Ca₂Si₃O₉and fluorinated phases such as Ca₅(PO₄)₃F, NaCa₂FSiO₄and CaSiO₂F₂. FTIR spectra indicated that fluorine (F) in the composition bonded with Si-O-Si (tetrahedral structure) functional groups. XRF and EDS results confirmed the presence of fluorine in BG and mBGF samples, with a higher fluoride content observed in the mBGF sample. After incubation in simulated body fluid, XRD, FTIR and SEM analyses demonstrated that all three samples (BG, BGF and mBGF) were capable of forming a hydroxyapatite layer. Furthermore, BGF and mBGF exhibited the formation of fluoroapatite, indicating enhanced remineralisation potential. Additionally, these BGs showed non-toxicity at low doses and were biocompatible with SaOS-2 cells based on MTT assay results.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":"12 5","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148981942","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":"Should breast/thoracic wall and lymph node volumes have the same margin in hypofractionated radiotherapy?","authors":"Marianne Falk, Mattias Hedman, Mimmi-Caroline Bolin, Jens Falk, Kristin Karlsson, Eva Onjukka, Bruno Sorcini, Giovanna Gagliardi","doi":"10.1088/2057-1976/ae9ffb","DOIUrl":"10.1088/2057-1976/ae9ffb","url":null,"abstract":"<p><p><i>Purpose.</i>The aim of this study was to investigate if the same planning target volume margins should be used for the breast/thoracic wall and lymph nodes when treated simultaneously with hypofractionated radiotherapy in deep inspiration breath-hold (DIBH) and free breathing (FB).<i>Methods.</i>For 80 patients (40 treated in FB and 40 in DIBH), nine anatomy sites were evaluated. Three were located in relation to the lymph node region and six in relation to the breast/thoracic wall. For three treatment fractions, a cone beam CT was acquired before and after the treatment and compared to the planning CT. Margins were calculated based on the inter- and intra-fractional uncertainties, ensuring 90% of the patients receiving 95% of the prescribed dose to the clinical target volume. Margin calculation was done for the full patient data set as well as for a subgroup of patients with set-up errors below the clinical action level, excluding patients with set-up errors larger than 7 mm.<i>Results.</i>For the full patient data set, margins of up to 14 mm were calculated. However, for patients with set-up errors below the clinical action level, the largest margins calculated for the lymph node region for treatments in DIBH were 8.5 mm, 8.7 mm and 6.6 mm in the vertical, longitudinal, and lateral directions respectively compared to 8.4 mm, 7.9 mm and 5.9 mm for treatments in FB. For the breast/thoracic wall, the largest margins were 5.9 mm, 6.4 mm and 5.5 mm for treatments in DIBH and 5.8 mm, 7.2 mm and 5.8 mm for treatments in FB in the vertical, longitudinal and lateral directions respectively.<i>Conclusion.</i>Treatments in DIBH and FB can use the same margins. However, the lymph node region requires a larger margin than the breast/thoracic wall region, due to greater anatomical mobility.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148849715","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":"Discrepancy between collapsed cone and Monte Carlo algorithms in lung inhomogeneity correction.","authors":"Amir Hossein Karimi, Indra J Das, Saeedeh Moharramkhani, Hossein Mirzaei, Marco Esposito, Ghazale Geraily","doi":"10.1088/2057-1976/aea1f9","DOIUrl":"10.1088/2057-1976/aea1f9","url":null,"abstract":"<p><p>Stereotactic body radiation therapy (SBRT) is a well-established standard of treatment for non-small cell lung cancer patients. However, SBRT is a double-edged sword and a high degree of accuracy is required for dose calculations. In the presence of inhomogeneity, it should be below 3% and depends strongly on the accuracy of the algorithm used for dose calculation in treatment planning system (TPS). The aim of this study is to evaluate the accuracy of collapsed cone (CC) algorithm for dose calculation in the presence of lung inhomogeneity compared to the Monte Carlo (MC) algorithm as the reference. The study conducted on a wide range of photon fields from 0.5 × 0.5 cm²to 10 × 10 cm². Monaco® TPS equipped with CC and MC algorithms in 6 MV, 10 MV, and 18 MV photons was employed and the inhomogeneity correction factor and PPDs were calculated inside an in-house inhomogeneous phantom. Finally, the results were compared between two algorithms. For 6 MV photons, gamma index pass rate (GIPR) with (2 mm, 3%) criteria in a 0.5 × 0.5 cm²field is only 71.3%. The CC algorithm will not be able to confidently reproduce the PDDs in this field. For 1 × 1 cm², 2 × 2 cm², and 3 × 3 cm²fields, the accuracy of the CC algorithm can increase to above 98%. For SBRT of small lung lesions, the results recommend increasing the minimum segment width to >0.5 cm in IMRT plans if CC algorithm is used. Independent of field size, the accuracy of CC algorithm decreases with increasing energy. For 10 and 18 MV photons, average GIPR under (2 mm, 3%) criteria was 97.9% and 92.0%, respectively, which are not of clinical interest.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148886182","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":"CORRIGENDUM: Retrieving of the melanosome fraction of human epidermis<i>in vivo</i>from skin chromatic coordinates (2026<i>Biomed. Phys. Eng. Express</i> 12 025078).","authors":"Freddy Narea-Jiménez, Alexis Vázquez-Villa, Cesar Arriaga-Arriaga, Alina Santillan-Guzmán, Anthony D Cho","doi":"10.1088/2057-1976/aea29c","DOIUrl":"10.1088/2057-1976/aea29c","url":null,"abstract":"","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":"12 5","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148944212","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":"Path-length-aware sinogram completion via direct moment enforcement for metal artifact reduction in X-ray CT.","authors":"Jong Hyun Seo, Sungwhan Kim","doi":"10.1088/2057-1976/aea1f8","DOIUrl":"10.1088/2057-1976/aea1f8","url":null,"abstract":"<p><p>High-density metallic objects often produce missing or severely corrupted projection data, resulting in strong streak artifacts in x-ray computed tomography (CT) images. In this paper, we propose a path-length-aware sinogram completion framework that combines interpolation in the mean attenuation domain with direct enforcement of low-order Helgason-Ludwig consistency conditions. The missing projections are first estimated using a Hermite-type interpolation that explicitly accounts for x-ray path length. Moment consistency is then restored through a probabilistic perturbation scheme based on compactly supported probability density functions, avoiding the need for solving a large-scale constrained optimization problem. Numerical experiments using both a simulated phantom and a dental CT model demonstrate that the proposed method generally achieves improved sinogram consistency and better image quality than linear interpolation-based metal artifact reduction (LI-MAR), normalized MAR (NMAR), and the TV-based iterative MAR method (TV-MAR), while preserving important anatomical structures. The proposed framework provides an efficient and physically motivated approach for MAR in x-ray CT.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148886270","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":"Evaluation of the physical and radiological properties of different Fricke gel formulations as water-equivalent materials and biological tissues.","authors":"Ângela M M Santos, Lucas F Araújo, Luíza R Costa, Natália R S Araújo, Rita C O Sebastião, Lucas Paixão, Ester M R Andrade, Bruno M Mendes, Amir Z Mesquita, Telma C F Fonseca","doi":"10.1088/2057-1976/ae9e3b","DOIUrl":"10.1088/2057-1976/ae9e3b","url":null,"abstract":"<p><p>Fricke gel dosimeters, also known as hydrogels, have emerged as attractive tools in clinical dosimetry due to the stringent requirements of quality assurance protocols and radiation protection programs. The performance of these materials strongly depends on the characteristics of the polymeric matrices that constitute them. Their ability to mimic reference materials, such as water and biological tissue, has been widely investigated for applications as simulators in radiotherapy procedures. This study aims to evaluate the physical and radiological properties of Fricke gel formulations prepared with different polymeric matrices: porcine skin gelatin (GEL), sodium carboxymethylcellulose (CMC), guar gum (GGU), and poly(vinyl alcohol) (PVA). Key-parameters, including the linear and mass attenuation coefficients (and), the mass energy-absorption (), the electronic stopping power (Stp_e), and the effective atomic number (), were determined using both experimental and computational methods. The physical investigations included the evaluation of thermal decomposition, determined by thermogravimetric analysis and kinetic analysis, incorporating isoconversional models based on multilayer perceptron (MLP) neural networks. The results obtained experimentally and through computational tools such as Phy-X, XCOM, TOPAS, EGSnrc, and ESTAR, for the different Fricke gel formulations, revealed that all gel formulations exhibit radiological parameters comparable to those of reference materials (water and biological tissue), in accordance with ICRU Report 44. Thermal analysis revealed distinct stability characteristics among the Fricke gel compositions. The GGU and PVA formulations exhibited greater thermal resistance compared with the GEL and CMC compositions. Kinetic analysis indicated that the average activation energy values for all formulations were below 40 kJ mol, suggesting that the thermal degradation processes initiate at relatively low temperatures. The application of MLP mathematical method proved valuable for accurately modeling the observed thermal kinetics. This study demonstrates that Fricke gel formulations (GEL, CMC, GGU, and PVA) effectively mimic the radiological response of biological tissue and water. Their performance in the field of radiotherapy is relevant for applications in three-dimensional dosimetry and radiological simulations due to their tissue-equivalent properties.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148816966","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":"Bayesian neural networks for uncertainty-aware tissue identification using bioimpedance-based frequency response similarity metrics.","authors":"Jacob Search, Sabino Zani, Brian P Mann","doi":"10.1088/2057-1976/aea167","DOIUrl":"10.1088/2057-1976/aea167","url":null,"abstract":"<p><p>This work sought to improve bioimpedance-based tissue identification model performance on an eight-tissue dataset by employing frequency response function similarity metrics (FRFSMs) as feature generators and Bayesian neural networks (BNNs) for accurate uncertainty estimations. Inspired by structural dynamics, FRFSMs were applied in a new context to generate features from mean baseline measurements, thereby extracting information of how unclassified measurements compare to previous ones. Additionally, BNNs were constructed to directly estimate model parameter uncertainty effects on classification uncertainty. Finally, a stacking ensemble technique combined base model outputs to train a meta-learner for improving performance. Models trained with similarity metric features achieved higher mean accuracies and better tissue specific1-scores than those trained with measurements. BNNs with temperature scaling reduced the expected calibration error of the standard feedforward networks by 19%-83%, indicating significant enhancement of uncertainty quantification. Ensembles achieved higher mean accuracies than base models, with maximum accuracies over 75%, and maintained the enhanced uncertainty quantification. The implementation of similarity metric inputs and BNNs for bioimpedance-based tissue identification offered a clear improvement in mean accuracy and uncertainty quantification over traditional models using measurements only. This marks an essential step towards enabling bioimpedance as a feasible sensing option for real-time tissue identification. Attaining dependable bioimpedance-based tissue identification will provide a foundation for new technologies due to economical and implementation advantages over competing methods while the added uncertainty-awareness makes it an excellent candidate for medical applications because it can provide additional context for outputs to users.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148879079","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":"Cross-layer semantic alignment and context enhancement network for medical image segmentation.","authors":"Bing Liu, Xinxin Sun, Geyi Zhan","doi":"10.1088/2057-1976/aea1f7","DOIUrl":"10.1088/2057-1976/aea1f7","url":null,"abstract":"<p><p>Medical image segmentation aims to accurately delineate organs, tissues, or lesion regions from complex medical images. However, existing hybrid models based on Transformers and convolutional neural networks still suffer from limitations in local detail modeling and cross-layer feature fusion, which often leads to blurred boundary information and loss of structural details. To address these issues, this paper proposes a Cross-layer Semantic Alignment and Context Enhancement Network for medical image segmentation. Specifically, a semantic enhancement module is introduced into the skip connections to achieve effective fusion of high-level semantic information and shallow spatial details through spatial-channel collaborative modeling and multi-scale context extraction (MCE). In addition, a lightweight boundary refinement mechanism is employed in the decoder stage to improve the recovery capability for complex boundary regions. Experiments conducted on the Synapse, ACDC, and GlaS datasets demonstrate that the proposed method outperforms mainstream approaches in terms of Dice, HD95, and Intersection over Union metrics, validating the effectiveness of the cross-layer semantic alignment mechanism for complex medical image segmentation tasks.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148879109","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}