Physical and Engineering Sciences in Medicine最新文献

{"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}

{"title":"Evaluation of a digital bismuth germanium oxide PET/CT system according to the Japanese brain tumor phantom test for ¹⁸F-fluciclovine imaging.","authors":"Shohei Fukai, Hiromitsu Daisaki, Honoka Yoshida, Naoki Shimada, Kazuki Motegi, Atsushi Osawa, Takashi Terauchi","doi":"10.1007/s13246-025-01608-z","DOIUrl":"https://doi.org/10.1007/s13246-025-01608-z","url":null,"abstract":"<p><p>The Omni Legend (GE Healthcare), equipped with a digital bismuth germanium oxide PET/CT system, has been recently developed. However, the performance of the Omni Legend without a time-of-flight (TOF) system for ¹⁸F-fluciclovine imaging is still unclear. Therefore, this study evaluated the image quality of the Omni Legend according to the Japanese brain tumor phantom test (JBT) criteria, and assessed its potential use for ¹⁸F-fluciclovine imaging. This study followed the JBT procedures. A brain tumor phantom, which includes six hot spheres of different diameters, was filled with an ¹⁸F-fluorodeoxyglucose solution with a radioactivity concentration ratio of 3 (spheres):1 (background). PET scanning was performed using the Omni Legend with a 30-min list mode acquisition. The PET data were reconstructed using an ordered subset expectation maximization (OSEM), an OSEM with point spread function (OSEM + PSF), and a Bayesian penalized likelihood (BPL) under standard clinical parameters. The image quality was evaluated using the JBT criteria, including contrast for a 7.5-mm sphere, recovery coefficient (RC) for a 10.0-mm sphere, standardized uptake value of total background (SUV_TOT), and detectability for a 7.5-mm sphere. The contrast, RC, and SUV_TOT were 25.1%, 0.70, and 1.00, respectively in OSEM; 25.8%, 0.80, and 0.99 in OSEM + PSF; and 33.8%, 0.93, and 0.99 in BPL. The 7.5-mm sphere was detected by all three methods. All of the JBT criteria were satisfied, regardless of the PET image reconstruction methods. This study demonstrated that the Omni Legend without TOF satisfies all JBT criteria and has the potential to provide high-quality images in ¹⁸F-fluciclovine imaging.</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":"144692085","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":"Radiation exposure of young patients with abdominal neuroblastoma from therapeutic and imaging procedures: a phantom study.","authors":"Michalis Mazonakis, John Stratakis, Efrossyni Lyraraki, John Damilakis","doi":"10.1007/s13246-025-01601-6","DOIUrl":"https://doi.org/10.1007/s13246-025-01601-6","url":null,"abstract":"<p><p>This study calculated the radiation dose to young patients with high-risk abdominal neuroblastoma from therapeutic and imaging procedures. Computational XCAT phantoms representing typical patients aged 5-15 years were used. Intensity modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT) plans were generated with 6 MV photons for a planning target volume (PTV) on the left and right abdominal side. Dose-volume-histograms from the plans were used to find the average dose (D_av) to critical normal abdominal and thoracic organs. The imaging dose to these organs and PTV was calculated by simulating kV cone-beam computed tomography (CBCT) scanning for patient setup before radiotherapy. Different CBCT protocols were simulated with Monte Carlo methods. The IMRT and VMAT plans provided similar PTV coverage and organ sparing. For a 21.6 Gy target dose, the D_av of the abdominal organs from the treatment plans was 3.6-19.6 Gy and that of thoracic organs was 0.1-2.3 Gy. Daily CBCT scans on 15-year-old patients with the standard adult protocol gave total PTV and organ doses of 95.3-485.3 mGy. The doses from the modified standard protocol for 5- and 10-year-old patients were 74.2-159.6 mGy. The dose calculations with a specially designed CBCT protocol for patients up to 10 years were 6.0-27.8 mGy. The total imaging dose to the PTV was up to 2.2% of the delivered therapeutic dose. The replacement of the modified adult CBCT protocol with a special protocol solely defined for children reduced the radiation dose to target and normal organs by more than five times.</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":"144691989","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":"Supervised versus unsupervised GAN for pseudo-CT synthesis in brain MR-guided radiotherapy.","authors":"Milad Zeinali Kermani, Mohamad Bagher Tavakoli, Amir Khorasani, Iraj Abedi, Vahid Sadeghi, Alireza Amouheidari","doi":"10.1007/s13246-025-01606-1","DOIUrl":"https://doi.org/10.1007/s13246-025-01606-1","url":null,"abstract":"<p><p>Radiotherapy is a crucial treatment for brain tumor malignancies. To address the limitations of CT-based treatment planning, recent research has explored MR-only radiotherapy, requiring precise MR-to-CT synthesis. This study compares two deep learning approaches, supervised (Pix2Pix) and unsupervised (CycleGAN), for generating pseudo-CT (pCT) images from T1- and T2-weighted MR sequences. 3270 paired T1- and T2-weighted MRI images were collected and registered with corresponding CT images. After preprocessing, a supervised pCT generative model was trained using the Pix2Pix framework, and an unsupervised generative network (CycleGAN) was also trained to enable a comparative assessment of pCT quality relative to the Pix2Pix model. To assess differences between pCT and reference CT images, three key metrics (SSIM, PSNR, and MAE) were used. Additionally, a dosimetric evaluation was performed on selected cases to assess clinical relevance. The average SSIM, PSNR, and MAE for Pix2Pix on T1 images were 0.964 ± 0.03, 32.812 ± 5.21, and 79.681 ± 9.52 HU, respectively. Statistical analysis revealed that Pix2Pix significantly outperformed CycleGAN in generating high-fidelity pCT images (p < 0.05). There was no notable difference in the effectiveness of T1-weighted versus T2-weighted MR images for generating pCT (p > 0.05). Dosimetric evaluation confirmed comparable dose distributions between pCT and reference CT, supporting clinical feasibility. Both supervised and unsupervised methods demonstrated the capability to generate accurate pCT images from conventional T1- and T2-weighted MR sequences. While supervised methods like Pix2Pix achieve higher accuracy, unsupervised approaches such as CycleGAN offer greater flexibility by eliminating the need for paired training data, making them suitable for applications where paired data is unavailable.</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":"144691990","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 distributed adaptive network framework for ERP-Based classification of multichannel EEG signals.","authors":"Fatemeh Afkhaminia, Mohammad Bagher Shamsollahi, Tahereh Bahraini","doi":"10.1007/s13246-025-01578-2","DOIUrl":"https://doi.org/10.1007/s13246-025-01578-2","url":null,"abstract":"<p><p>Understanding brain function is one of the most challenging areas in brain signal processing. This study introduces a novel framework for electroencephalography (EEG) signal classification based on distributed adaptive networks using diffusion strategy. Our approach models the brain as a multitask network, where EEG electrodes are considered as nodes of this network. The network parameters are dynamically optimized based on the data from the nodes and inter-node cooperation. The proposed framework, which comprises network modeling and diffusion-based adaptation using the adapt then combine (ATC) algorithm, has been validated on different types of data. Experimental results indicate that the proposed framework outperforms common methods in classifying EEG data based on event-related potential (ERP) pattern identification, particularly in scenarios where machine learning-based models struggle with limited data. Furthermore, its ability to adapt to the non-stationary and dynamic nature of EEG signals and its efficient real-time implementation make this approach ideal for brain-computer interface (BCI), cognitive neuroscience, and clinical applications.</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":"144692082","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":"Comment on \"Subclinical tremor differentiation using long short-term memory networks\".","authors":"Hinpetch Daungsupawong, Viroj Wiwanitkit","doi":"10.1007/s13246-025-01559-5","DOIUrl":"https://doi.org/10.1007/s13246-025-01559-5","url":null,"abstract":"","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":"144692084","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":"Hybrid deep-CNN and Bi-LSTM model with attention mechanism for enhanced ECG-based heart disease diagnosis.","authors":"Gaurav Kumar, Neeraj Varshney","doi":"10.1007/s13246-025-01612-3","DOIUrl":"https://doi.org/10.1007/s13246-025-01612-3","url":null,"abstract":"<p><p>According to the World Health Organization (WHO), 17.9 million people die yearly from cardiovascular Diseases (CVDs), including heart attacks. Cardiovascular diseases, including heart attack, kill 32% of people globally. Current approaches struggle with electrocardiogram (ECG) signal variability, causing diagnosing errors. The adoption of automated and accurate models for heart disease detection is lacking since conventional methods rely on human analysis, which is time-consuming and error-prone. This work covers the crucial topic of heart disease diagnosis, especially ECG data analysis for cardiovascular disease detection. The integration of the Deep-Convolutional Neural Network (Deep-CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) model with an Attention Mechanism enhances the accuracy and reliability of heart disease categorisation. The Deep-CNN component efficiently extracts features from capture spatial linkages, while the Bi-LSTM layers handle temporal dependencies to identify patient health patterns over time. The model is evaluated on 303 patient records with 14 clinical characteristics from the University of California, Irvine (UCI) Cleveland Heart Disease dataset. The suggested technique has 97.23% accuracy, 97.72% recall, precision, and 96.90% F1 score. These findings show that the proposed architecture improves diagnostic performance more than boosting ensemble approaches and hybrid models.</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":"144692086","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 Monaco 6.2.2 in complex radiotherapy across matched LINACs: improved MLC modelling and dose accuracy with virtual source model 2.0.","authors":"Luis Muñoz, Peter McLoone, Peter Metcalfe, Anatoly B Rosenfeld, Giordano Biasi","doi":"10.1007/s13246-025-01602-5","DOIUrl":"https://doi.org/10.1007/s13246-025-01602-5","url":null,"abstract":"<p><p>This study assesses the updated Monaco TPS virtual source model (VSM) 2.0, which removes multileaf collimator (MLC) and jaw characterization as editable factors from the MLC geometry section within Monaco. The focus is on the impact of changes to stereotactic radiotherapy (SRT) cases for spinal and intracranial treatments for two beam matched linear accelerators. A validated custom VSM 1.6 model optimized for SRT was compared with the Elekta Accelerated Go Live 6 MV flattening filter-free (FFF) and VSM 2.0. Evaluations included measured MLC characteristics with a high-resolution detector, measured output factors (OPF), ion chamber fields in the thorax phantom, and recalculations of clinically relevant SRT cases. VSM 2.0 improves MLC modelling. Ion chamber measurements for IAEA TD1583 measurements were found to be within expected tolerances. Gamma pass rates for two matched LINACs evidenced improvement at 1%, 1 mm and 10% threshold for single and multi-SRS brain and SABR Spine treatments. VSM 2.0 represents a meaningful advancement in beam modelling within a Monte Carlo-based TPS environment, offering improved dosimetric performance and operational simplicity. Commercially available detectors were used to demonstrate that VSM 2.0 enhances agility MLC modelling, supporting more precise SRT and SABR delivery for matched LINACs. Removing configurable dependencies from the beam model will result in more consistent high quality beam models, an improves workflows for commissioning of the Monaco TPS.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144676209","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":"Evaluation of volumetric modulated arc therapy dose calculation plans based on kV-cone beam CT images for the breast cancer treatment.","authors":"Amine El Outmani, M Zerfaoui, I Hattal, Y Oulhouq, K Bahhous, A Rrhioua, D Bakari, M Hamal, A Moussa","doi":"10.1007/s13246-025-01592-4","DOIUrl":"https://doi.org/10.1007/s13246-025-01592-4","url":null,"abstract":"<p><p>The majority of treatment centers currently use Cone Beam Computed Tomography (CBCT) as an effective method of patient repositioning, opening up the prospect of using it in treatment plans calculation. The purpose of this task is to investigate the feasibility of utilizing kV-CBCT images as a potential alternative to CT scans for generating treatment plans during radiation therapy. The images are taken by the imaging system installed on a linear accelerator and implemented in a Treatment Planning System (TPS). This imaging system opens the possibility to proceed to dose calculation using a calibration curve that establishes a link between voxel attenuation in Hounsfield Units (HU) and electron densities relative to water (RED). Furthermore, a comparison of Volumetric Modulated Arc Therapy (VMAT) treatment plans on this imaging modality was done to the Computed Tomography (CT) imaging system using the CIRS phantom. The comparison is also performed the images of 10 patients with right breast cancer. VMAT plans from the two modalities were compared in terms of target coverage, normal tissue sparing, dose distribution parameters, and monitor units (MUs). Then, the gamma index test is employed by using the PTW Verisoft to compare the TPS calculated dose distribution for the two modalities. It is also used to compare the measured dose distribution performed by the Portal Dosimetry. Regarding the volume-dose parameters of the PTV and the Organs at risk (OARs), no differences were found between the VMAT plans of the two imaging modalities, CT and CBCT. Additionally, the gamma analysis results of the patients VMAT plans for 1%-1 mm, 2%-2 mm, 3%-3 mm, 4%-4 mm and 5%-5 mm showed that more than 84%, 90%, 92%, 94%, and 96%, respectively, of the points agreed upon between Eclipse calculated and measured dose distributions for the CT and CBCT VMAT plans. The good dosimetric agreement (gamma index reaches more than 92% for 3%-3 mm) between breast VMAT plans based on CT and CBCT images renders the latter an appealing verification tool and a substitute for CT images, if needed, for dose calculation. CBCT images are an effective option for direct dose calculation or to do adaptive radiotherapy in the case of a breast cancer.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144676127","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":"Action potential as a contributing factor in axonal transport: a numerical study.","authors":"AmirAli Saboorian, Bahman Vahidi","doi":"10.1007/s13246-025-01591-5","DOIUrl":"https://doi.org/10.1007/s13246-025-01591-5","url":null,"abstract":"<p><p>Despite various studies on axonal mechanics in recent years, the mechanisms and factors contributing to axonal transport are still not fully understood. In this study, the possible role of action potential (AP) propagation through neurites in axonal transport was explored by utilizing underlying physical principles through numerical simulation. A fluid-structure interaction model was used to simulate the physical behavior of the axon as action potential waves propagate. The axon and its membrane were modeled as a fluid-filled cylinder with elastic walls, where the action potential acts as a moving radial load on the axon. Utilizing computational fluid dynamics simulation and accounting for forces induced by the action potential led to the emergence of an intercellular fluid flow inside the axon, which was subsequently incorporated into current models of axonal transport in the literature. The convective intercellular fluid flow induced by the action potential acts as a mechanism for axonal transport, with velocities ranging from 2 to 17 mm per day, which is consistent with previously reported ranges for the slow axonal transport component. Additionally, by incorporating the effect of convective flow, it was shown that unidirectional transport, coupled with convective transport, can successfully describe the movement of larger cargos against their concentration gradients. The results demonstrated that for the squid giant axon and hippocampal neurites, the displacement pulse propagates almost simultaneously with the AP. Analyzing the interaction between action potential and axonal transport can lead to a better understanding of these phenomena.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144676207","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}