Physical and Engineering Sciences in Medicine最新文献

{"title":"Dosimetric evaluation of synthetic kilo-voltage CT images generated from megavoltage CT for head and neck tomotherapy using a conditional GAN network.","authors":"Yazdan Choghazardi, Mohamad Bagher Tavakoli, Iraj Abedi, Mahnaz Roayaei, Simin Hemati, Ahmad Shanei","doi":"10.1007/s13246-025-01603-4","DOIUrl":"https://doi.org/10.1007/s13246-025-01603-4","url":null,"abstract":"<p><p>The lower image contrast of megavoltage computed tomography (MVCT), which corresponds to kilovoltage computed tomography (kVCT), can inhibit accurate dosimetric assessments. This study proposes a deep learning approach, specifically the pix2pix network, to generate high-quality synthetic kVCT (skVCT) images from MVCT data. The model was trained on a dataset of 25 paired patient images and evaluated on a test set of 15 paired images. We performed visual inspections to assess the quality of the generated skVCT images and calculated the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Dosimetric equivalence was evaluated by comparing the gamma pass rates of treatment plans derived from skVCT and kVCT images. Results showed that skVCT images exhibited significantly higher quality than MVCT images, with PSNR and SSIM values of 31.9 ± 1.1 dB and 94.8% ± 1.3%, respectively, compared to 26.8 ± 1.7 dB and 89.5% ± 1.5% for MVCT-to-kVCT comparisons. Furthermore, treatment plans based on skVCT images achieved excellent gamma pass rates of 99.78 ± 0.14% and 99.82 ± 0.20% for 2 mm/2% and 3 mm/3% criteria, respectively, comparable to those obtained from kVCT-based plans (99.70 ± 0.31% and 99.79 ± 1.32%). This study demonstrates the potential of pix2pix models for generating high-quality skVCT images, which could significantly enhance Adaptive Radiation Therapy (ART).</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144734067","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 comparison of two bolus types for radiotherapy following immediate breast reconstruction.","authors":"Kasia Bobrowski, Jonathon Lee","doi":"10.1007/s13246-025-01604-3","DOIUrl":"https://doi.org/10.1007/s13246-025-01604-3","url":null,"abstract":"<p><p>Immediate breast Reconstruction is increasing in use in Australia and accounts for almost 10% of breast cancer patients (Roder in Breast 22:1220-1225, 2013). Many treatments include a bolus to increase dose to the skin surface. Air gaps under bolus increase uncertainty in dosimetry and many bolus types are unable to conform to the shape of the breast or are not flexible throughout treatment if there is a swelling induced contour change. This study investigates the use of two bolus types that can be manufactured in house-wet combine and ThermoBolus. Wet combine is a material composed of several water soaked dressings. ThermoBolus is a product developed in-house that consists of thermoplastic encased in silicone. Plans using a volumetric arc therapy technique were created for each bolus and dosimetry performed with thermoluminescent detectors (TLDs) and EBT-3 film over three fractions. Wax was used to simulate swelling and allow analysis of the flexibility of the bolus materials. ThermoBolus had a range of agreement with calculation from -2 to 4% for film measurement and -5.6 to 1.0% for TLDs. Wet combine had a range of agreement with calculation from 1.6 to 10.5% for film measurement and -13.5 to 13.1% for TLDs. It showed consistent conformity and flexibility for all fractions and with induced contour but air gaps of 2-3 mm were observed between layers of the material. ThermoBolus and wet combine are able to conform to contour change without the introduction of large air gaps between the patient surface and bolus. ThermoBolus is reusable and can be remoulded if the patient undergoes significant contour change during the course of treatment. It is able to be modelled accurately by the treatment planning system. Wet combine shows inconsistency in manufacture and requires more than one bolus to be made over the course of treatment, reducing accuracy in modelling and dosimetry.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144734066","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":"Modelling single cell dosimetry and DNA damage of targeted alpha therapy using Monte-Carlo techniques.","authors":"Adam L Jolly, Andrew L Fielding","doi":"10.1007/s13246-025-01605-2","DOIUrl":"https://doi.org/10.1007/s13246-025-01605-2","url":null,"abstract":"<p><p>Targeted alpha therapy (TαT) employs alpha particle-emitting radioisotopes conjugated to tumour-specific carriers to precisely irradiate tumour cells. Monte-carlo techniques have been used to accurately simulate absorbed dose and DNA damage for the four promising TαT radionuclides, Actinium-225 (²²⁵Ac), Radium-223, (²²³Ra), Lead-212 (²¹²Pb) and Astatine-211, (²¹¹At). TOPAS and TOPAS-nBio, based on the Geant4 and Geant4-DNA monte-carlo codes respectively, were used to model the radioactive decay and alpha particle transport within a simplified spherical cell model. Four different sites within the cell model were used for the initial radionuclide distributions: the cell membrane layer, within the cytoplasm volume, on the nucleus surface, and within the nucleus volume. Results indicate higher absorbed doses to the nucleus per decay when radionuclides are initially located on the nucleus wall or within the nucleus volume. ²²⁵Ac and ²²³Ra, with longer decay chains and higher alpha yields, exhibit higher doses to the nucleus per decay compared to ²¹²Pb and ²¹¹At. Notably, ²¹¹At, particularly when initially distributed within the nucleus volume or at its surface, demonstrates high relative efficacy, indicated by the absorbed dose to the nucleus per decay and number of single and double-strand breaks. These findings suggest that tumour-specific molecules should ideally target the nucleus to optimize efficacy.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144734069","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":"Evaluating the impact of view position in X-ray imaging for the classification of lung diseases.","authors":"Aya Hage Chehade, Nassib Abdallah, Jean-Marie Marion, Mohamad Oueidat, Pierre Chauvet","doi":"10.1007/s13246-025-01579-1","DOIUrl":"https://doi.org/10.1007/s13246-025-01579-1","url":null,"abstract":"<p><p>Clinical information associated with chest X-ray images, such as view position, patient age and gender, plays a crucial role in image interpretation, as it influences the visibility of anatomical structures and pathologies. However, most classification models using the ChestX-ray14 dataset relied solely on image data, disregarding the impact of these clinical variables. This study aims to investigate which clinical variable affects image characteristics and assess its impact on classification performance. To explore the relationships between clinical variables and image characteristics, unsupervised clustering was applied to group images based on their similarities. Afterwards, a statistical analysis was then conducted on each cluster to examine their clinical composition, by analyzing the distribution of age, gender, and view position. An attention-based CNN model was developed separately for each value of the clinical variable with the greatest influence on image characteristics to assess its impact on lung disease classification. The analysis identified view position as the most influential variable affecting image characteristics. Accounting for this, the proposed approach achieved a weighted area under the curve (AUC) of 0.8176 for pneumonia classification, surpassing the base model (without considering view position) by 1.65% and outperforming previous studies by 6.76%. Furthermore, it demonstrated improved performance across all 14 diseases in the ChestX-ray14 dataset. The findings highlight the importance of considering view position when developing classification models for chest X-ray analysis. Accounting for this characteristic allows for more precise disease identification, demonstrating potential for broader clinical application in lung disease evaluation.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144734068","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":"An open-source tool for converting 3D mesh volumes into synthetic DICOM CT images for medical physics research.","authors":"Michael John James Douglass","doi":"10.1007/s13246-025-01599-x","DOIUrl":"https://doi.org/10.1007/s13246-025-01599-x","url":null,"abstract":"<p><p>Access to medical imaging data is crucial for research, training, and treatment planning in medical imaging and radiation therapy. However, ethical constraints and time-consuming approval processes often limit the availability of such data for research. This study introduces DICOMator, an open-source Blender add-on designed to address this challenge by enabling the creation of synthetic CT datasets from 3D mesh objects. DICOMator aims to provide researchers and medical professionals with a flexible tool for generating customisable and semi-realistic synthetic CT data, including 4D CT datasets from user defined static or animated 3D mesh objects. The add-on leverages Blender's powerful 3D modelling environment, utilising its mesh manipulation, animation and rendering capabilities to create synthetic data ranging from simple phantoms to accurate anatomical models. DICOMator incorporates various features to simulate common CT imaging artefacts, bridging the gap between 3D modelling and medical imaging. DICOMator voxelises 3D mesh objects, assigns appropriate Hounsfield Unit values, and applies artefact simulations. These simulations include detector noise, metal artefacts and partial volume effects. By incorporating these artefacts, DICOMator produces synthetic CT data that more closely resembles real CT scans. The resulting data is then exported in DICOM format, ensuring compatibility with existing medical imaging workflows and treatment planning systems. To demonstrate DICOMator's capabilities, three synthetic CT datasets were created: a simple lung phantom to illustrate basic functionality, a more realistic cranial CT scan to demonstrate dose calculations and CT image registration on synthetic data in treatment planning systems. Finally, a thoracic 4D CT scan featuring multiple breathing phases was created to demonstrate the dynamic imaging capabilities and the quantitative accuracy of the synthetic datasets. These examples were chosen to highlight DICOMator's versatility in generating diverse and complex synthetic CT data suitable for various research and educational purposes, from basic quality assurance to advanced motion management studies. DICOMator offers a promising solution to the limitations of patient CT data availability in medical physics research. By providing a user-friendly interface for creating customisable synthetic datasets from 3D meshes, it has the potential to accelerate research, validate treatment planning tools such as deformable image registration, and enhance educational resources in the field of radiation oncology medical physics. Future developments may include incorporation of other imaging modalities, such as MRI or PET, further expanding its utility in multi-modal imaging research.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144709487","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":"DGEAHorNet: high-order spatial interaction network with dual cross global efficient attention for medical image segmentation.","authors":"Haixin Peng, Xinjun An, Xue Chen, Zhenxiang Chen","doi":"10.1007/s13246-025-01583-5","DOIUrl":"https://doi.org/10.1007/s13246-025-01583-5","url":null,"abstract":"<p><p>Medical image segmentation is a complex and challenging task, which aims to accurately segment various structures or abnormal regions in medical images. However, obtaining accurate segmentation results is difficult because of the great uncertainty in the shape, location, and scale of the target region. To address these challenges, we propose a higher-order spatial interaction framework with dual cross global efficient attention (DGEAHorNet), which employs a neural network architecture based on recursive gate convolution to adequately extract multi-scale contextual information from images. Specifically, a Dual Cross-Attentions (DCA) is added to the skip connection that can effectively blend multi-stage encoder features and narrow the semantic gap. In the bottleneck stage, global channel spatial attention module (GCSAM) is used to extract image global information. To obtain better feature representation, we feed the output from the GCSAM into the multi-branch dense layer (SENetV2) for excitation. Furthermore, we adopt Depthwise Over-parameterized Convolutional Layer (DO-Conv) in order to replace the common convolutional layer in the input and output part of our network, then add Efficient Attention (EA) to diminish computational complexity and enhance our model's performance. For evaluating the effectiveness of our proposed DGEAHorNet, we conduct comprehensive experiments on four publicly-available datasets, and achieving 0.9320, 0.9337, 0.9312 and 0.7799 in Dice similarity coefficient on ISIC2018, ISIC2017, CVC-ClinicDB and HRF respectively. Our results show that DGEAHorNet has better performance compared with advanced methods. The code is publicly available at https://github.com/penghaixin/mymodel .</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144709488","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":"An approach for cancer outcomes modelling using a comprehensive synthetic dataset.","authors":"Lorna Tu, Hervé H F Choi, Haley Clark, Samantha A M Lloyd","doi":"10.1007/s13246-025-01594-2","DOIUrl":"https://doi.org/10.1007/s13246-025-01594-2","url":null,"abstract":"<p><p>Limited patient data availability presents a challenge for efficient machine learning (ML) model development. Recent studies have proposed methods to generate synthetic medical images but lack the corresponding prognostic information required for predicting outcomes. We present a cancer outcomes modelling approach that involves generating a comprehensive synthetic dataset which can accurately mimic a real dataset. A real public dataset containing computed tomography-based radiomic features and clinical information for 132 non-small cell lung cancer patients was used. A synthetic dataset of virtual patients was synthesized using a conditional tabular generative adversarial network. Models to predict two-year overall survival were trained on real or synthetic data using combinations of four feature selection methods (mutual information, ANOVA F-test, recursive feature elimination, random forest (RF) importance weights) and six ML algorithms (RF, k-nearest neighbours, logistic regression, support vector machine, XGBoost, Gaussian Naïve Bayes). Models were tested on withheld real data and externally validated. Real and synthetic datasets were similar, with an average one minus Kolmogorov-Smirnov test statistic of 0.871 for continuous features. Chi-square test confirmed agreement for discrete features (p < 0.001). XGBoost using RF importance-based features performed the most consistently for both datasets, with percent differences in balanced accuracy and area under the precision-recall curve of < 1.3%. Preliminary findings demonstrate the potential application of synthetic radiomic and clinical data augmentation for cancer outcomes modelling, although further validation with larger diverse datasets is crucial. While our approach was described in a lung context, it may be applied to other sites or endpoints.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144700047","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":"Impact of differences in computed tomography value-electron density/physical density conversion tables on calculate dose in low-density areas.","authors":"Mia Nomura, Shunsuke Goto, Mizuki Yoshioka, Yuiko Kato, Ayaka Tsunoda, Kunio Nishioka, Yoshinori Tanabe","doi":"10.1007/s13246-025-01611-4","DOIUrl":"https://doi.org/10.1007/s13246-025-01611-4","url":null,"abstract":"<p><p>In radiotherapy treatment planning, the extrapolation of computed tomography (CT) values for low-density areas without known materials may differ between CT scanners, resulting in different calculated doses. We evaluated the differences in the percentage depth dose (PDD) calculated using eight CT scanners. Heterogeneous virtual phantoms were created using LN-300 lung and - 900 HU. For the two types of virtual phantoms, the PDD on the central axis was calculated using five energies, two irradiation field sizes, and two calculation algorithms (the anisotropic analytical algorithm and Acuros XB). For the LN-300 lung, the maximum CT value difference between the eight CT scanners was 51 HU for an electron density (ED) of 0.29 and 8.8 HU for an extrapolated ED of 0.05. The LN-300 lung CT values showed little variation in the CT-ED/physical density data among CT scanners. The difference in the point depth for the PDD in the LN-300 lung between the CT scanners was < 0.5% for all energies and calculation algorithms. Using Acuros XB, the PDD at - 900 HU had a maximum difference between facilities of > 5%, and the dose difference corresponding to an LN-300 lung CT value difference of > 20 HU was > 1% at a field size of 2 × 2 cm². The study findings suggest that the calculated dose of low-density regions without known materials in the CT-ED conversion table introduces a risk of dose differences between facilities because of the calibration of the CT values, even when the same CT-ED phantom radiation treatment planning and treatment devices are used.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144692087","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":"Advanced bioimpedance analysis for infectious disease risk assessment via neural network classifiers.","authors":"Sergey Filist, Riad Taha Al-Kasasbeh, Tigran Gagikovich Gevorkyan, Osama M Al- Habahbeh, Olga Shatalova, Ahmad Telfah, Evgeny Starkov, Nikolay A Korenevskiy, Ashraf Shaqadan, Manafaddin Bashir Namazov, Ilyash Maksim, Marwan S Mousa","doi":"10.1007/s13246-025-01575-5","DOIUrl":"https://doi.org/10.1007/s13246-025-01575-5","url":null,"abstract":"<p><p>In this work, a neural network classification model based on multidimensional bioimpedance measurement to analyze biomaterial impedance in living systems was developed. The modified Voigt model was used to capture the structural elements as a bioimpedance model. Utilizing this model, extracted descriptors were used to train neural network classifiers. A multidimensional probing technique was employed to obtain biomaterial descriptors, and then Coles plots were generated. Iterative algorithms were applied to generate Voigt models that represent the biomaterial impedance. The model parameters were then utilized as descriptors for the trained classifiers. The developed hybrid classifiers employing these bioimpedance models and descriptor generation algorithms demonstrated their effectiveness in assessing the risk of infectious diseases and related complications. Sample of patients with viral pneumonia were investigated experimentally to evaluate the performance of the bioimpedance models. Bioimpedance analysis was performed by attaching an electrode belt to the patients' chests, and the results were used to generate Cole plots. This innovative approach in bioimpedance analysis has the potential to revolutionize the diagnosis and treatment of infectious diseases. By leveraging advanced technology and algorithms, we can improve the accuracy of infection risk assessment and mitigate its potential complications. This not only enhances patient outcomes but also aids in reducing the transmission of infectious diseases. Furthermore, a comparative analysis was conducted on a control sample of positive and negative cases of pneumonia using X-ray and bioimpedance methods. The bioimpedance method demonstrated an accuracy of 79%, surpassing the X-ray method by 77%.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144692083","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 LM-DRAMA parameters and non-local means filtering to improve small-lesion detectability in SiPM-based TOF breast PET.","authors":"Takuro Shiiba, Hana Katakami, Aiko Naito, Maki Takamura, Masanobu Ishiguro, Masanori Watanabe, Masaki Uno","doi":"10.1007/s13246-025-01598-y","DOIUrl":"https://doi.org/10.1007/s13246-025-01598-y","url":null,"abstract":"<p><p>This study aimed to optimize image reconstruction parameters for a dedicated time-of-flight (TOF) breast positron emission tomography (PET) system equipped with silicon photomultipliers (SiPMs) that maximize lesion detectability while minimizing image noise. A cylindrical phantom containing four hot spheres (3-10 mm diameter) was scanned at sphere-to-background ratios of 4:1, 6:1, and 8:1. All data were reconstructed using a 3D list-mode dynamic row-action maximum likelihood algorithm with β values of 10-200, followed by non-local means (NLM) filtering at intensities of 0.5-2.0 or no filtering. Image quality was evaluated using background coefficient of variation (COV_BG), contrast recovery coefficient (CRC), and detectability index (DI) for the 3 mm sphere. As β increased, CRC and DI improved, particularly for smaller spheres and higher SBRs; however, background noise also rose. Applying the NLM filter reduced COV_BG, especially when increasing the filter intensity from 0.5 to 1.0, although noise reduction gains plateaued at intensities above 1.0. Optimal trade-offs in lesion detectability and noise were observed at moderate β (50-100) with NLM intensities of 1.0-1.5, yielding higher CRC and DI without excessive background noise or blurring effects. A balanced approach to β and NLM filtering substantially enhances small-lesion visibility in SiPM-based TOF-dedicated breast PET imaging. These findings offer a practical framework for parameter selection, supporting better lesion detectability and advancing breast cancer diagnostics through more sensitive PET protocols.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144692088","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}