Biomedical Physics & Engineering Express最新文献

{"title":"Investigating the role of blood models in predicting rupture status of intracranial aneurysms.","authors":"Zonghan Lyu, Mostafa Rezaeitaleshmahalleh, Nan Mu, Jingfeng Jiang","doi":"10.1088/2057-1976/adcc34","DOIUrl":"https://doi.org/10.1088/2057-1976/adcc34","url":null,"abstract":"<p><p><i>Purpose</i>. Selecting patients with high-risk intracranial aneurysms (IAs) is of clinical importance. Recent work in machine learning-based (ML) predictive modeling has demonstrated that lesion-specific hemodynamics within IAs can be combined with other information to provide critical insights for assessing rupture risk. However, how the adoption of blood rheology models (i.e., Newtonian and Non-Newtonian blood models) may influence ML-based predictive modeling of IA rupture risk has not been investigated.<i>Methods and Materials.</i>In this study, we conducted transient CFD simulations using Newtonian and non-Newtonian rheology (Carreau-Yasuda [CY]) models on a large cohort of 'patient-specific' IA geometries (>100) under pulsatile flow conditions to investigate how each blood model may affect the characterization of the IAs' rupture status. Key hemodynamic parameters were analyzed and compared, including wall shear stress (WSS) and vortex-based parameters. In addition, velocity-informatics features extracted from the flow velocity were utilized to train a support vector machine (SVM) model for rupture status prediction.<i>Results.</i>Our findings demonstrate significant differences between the two models (i.e., Newtonian versus CY) regarding the WSS-related metrics. In contrast, the parameters derived from the flow vortices and velocity informatics agree. Similar to other studies, using a non-Newtonian CY model results in lower peak WSS and higher oscillatory shear index (OSI) values. Furthermore, integrating velocity informatics and machine learning achieved robust performance for both blood models (area under the curve [AUC] ˃0.85).<i>Conclusions.</i>Our preliminary study found that ML-based rupture status prediction derived from velocity informatics and geometrical parameters yielded comparable results despite differences observed in aneurysmal hemodynamics using two blood rheology models (i.e., Newtonian versus CY).</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":"11 3","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143969084","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":"Bald eagle-optimized transformer networks with temporal-spatial mid-level features for pancreatic tumor classification.","authors":"Manas Ranjan Mohanty, Pradeep Kumar Mallick, Debahuti Mishra","doi":"10.1088/2057-1976/adcac9","DOIUrl":"https://doi.org/10.1088/2057-1976/adcac9","url":null,"abstract":"<p><p>The classification and diagnosis of pancreatic tumors present significant challenges due to their inherent complexity and variability. Traditional methods often struggle to capture the dynamic nature of these tumors, highlighting the need for advanced techniques that improve precision and robustness. This study introduces a novel approach that combines temporal-spatial mid-level features (CTSF) with bald eagle search (BES) optimized transformer networks to enhance pancreatic tumor classification. By leveraging temporal-spatial features that encompass both spatial structure and temporal evolution, we employ the BES algorithm to optimize the vision transformer (ViT) and swin transformer (ST) models, significantly enhancing their capacity to process complex datasets. The study underscores the critical role of temporal features in pancreatic tumor classification, enabling the capture of changes over time to improve our understanding of tumor progression and treatment responses. Among the models evaluated-GRU, LSTM, and ViT-the ViTachieved superior performance, with accuracy rates of 94.44%, 89.44%, and 87.22% on the TCIA-Pancreas-CT, Decathlon Pancreas, and NIH-Pancreas-CT datasets, respectively. Spatial features extracted from ResNet50, VGG16, and ST were also essential, with the ST model attaining the highest accuracy of 95.00%, 95.56%, and 93.33% on the same datasets. The integration of temporal and spatial features within the CTSF model resulted in accuracy rates of 96.02%, 97.21%, and 95.06% for the TCIA-Pancreas-CT, Decathlon Pancreas, and NIH-Pancreas-CT datasets, respectively. Furthermore, optimization techniques, particularly hyperparameter tuning, further enhanced performance, with the BES-optimized model achieving the highest accuracy of 98.02%, 98.92%, and 98.89%. The superiority of the CTSF-BES approach was confirmed through the Friedman test and Bonferroni-Dunn test, while execution time analysis demonstrated a favourable balance between performance and efficiency.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":"11 3","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144061929","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":"Optimized glaucoma detection using HCCNN with PSO-driven hyperparameter tuning.","authors":"Latha G, Aruna Priya P","doi":"10.1088/2057-1976/adc9b7","DOIUrl":"10.1088/2057-1976/adc9b7","url":null,"abstract":"<p><p><i>Purpose</i>. This study is focused on creating an effective glaucoma detection system employing a Hybrid Centric Convolutional Neural Network (HCCNN) model. By using Particle Swarm Optimization (PSO), classification accuracy is increased and computing complexity is reduced. Modified U-Net is also used to segment the optic disc (OD) and optic cup (OC) regions of classified glaucoma images to determine the severity of glaucoma.<i>Methods</i>. The proposed HCCNN model can extract features from fundus images that show signs of glaucoma. To improve the model performance, hyperparameters like dropout rate, learning rate, and the number of neurons in the dense layer are optimized using the PSO method. The PSO algorithm iteratively assesses and modifies these parameters to minimize classification error. The classified glaucoma image is subjected to channel separation to enhance the visibility of relevant features. This channel-separated image is segmented using the modified U-Net to delineate the OC and OD regions.<i>Results</i>. Experimental findings indicate that the PSO-HCCNN model attains classification accuracy of 94% and 97% on DRISHTI-GS and RIM-ONE datasets. Performance criteria including accuracy, sensitivity, specificity, and AUC are employed to assess the system's efficacy, demonstrating a notable enhancement in the early detection rates of glaucoma. To evaluate the segmentation performance, parameters such as the Dice coefficient, and Jaccard index are computed.<i>Conclusion</i>. The integration of PSO with the HCCNN model considerably enhances glaucoma detection from fundus images by optimizing essential parameters and accurate OD and OC segmentation, resulting in a robust and precise classification model. This method has potential uses in ophthalmology and may help physicians detect glaucoma early and accurately.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143802265","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":"The utilization of the k-means clustering for cancer cell detection and classification with serous effusion.","authors":"Safaa Al-Qaysi","doi":"10.1088/2057-1976/adca3e","DOIUrl":"10.1088/2057-1976/adca3e","url":null,"abstract":"<p><p>Cytological analysis of serous effusion specimens is essential for cancer diagnosis. In this work, we analyzed three-dimensional (3D) morphologic features by clustering to discriminate between malignant and nonmalignant cells in serous effusion specimens collected from 10 patients with pleural and peritoneal effusion accumulation symptoms. After the nuclei and mitochondria were fluorescently labeled, we obtained confocal image stack data and conducted 3D reconstruction and morphological feature parameter computation. Confocal images were segmented, interpolated, and reconstructed. Quantitative comparison across cell types has been made by 27 morphological features of volume and surface linked to the cell, nucleus, and mitochondria. We used an unsupervised machine learning method of<i>k-means</i>clustering to separate the cell distribution objectively and effectively in the 3D parameter space of the cell morphology features. The statistical significance of the differences was examined on morphological features among the three cell clusters. The clustering results were also analyzed against those of cytopathological examinations performed by collaborative pathologist on specimens collected from the same patients. These results showed that 3D morphologic features allow clustering of the effusion cells in the space of these parameters and may help produce new ways to quickly profile cells for cancer diagnosis in clinical settings. By incorporating these techniques into clinical practice, healthcare professionals may be able to more effectively detect and treat cancers in patients.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143810105","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":"A machine learning toolkit assisted approach for IMRT fluence map optimization: feasibility and advantages.","authors":"Xin Wu, Dongrong Yang, Yang Sheng, Qing-Rong Jackie Wu, Qiuwen Wu","doi":"10.1088/2057-1976/adcaca","DOIUrl":"https://doi.org/10.1088/2057-1976/adcaca","url":null,"abstract":"<p><p><i>Purpose</i>. Traditional machine learning (ML) and deep learning (DL) applications in treatment planning rely on complex model architectures and large, high-quality training datasets. However, they cannot fully replace the conventional optimization process. This study presents a novel application of ML in treatment planning where established ML/DL toolkits are directly applied to treatment plan optimization.<i>Materials and Methods</i>. A one-layer network was designed based on the dose deposition matrix and implemented in PyTorch's L-BFGS optimizer with GPU acceleration. The classical steepest descent optimizer was selected as a reference for comparison. Both optimizers utilized identical inputs and objective functions to ensure a fair comparison. DVH- and gEUD-based objectives were implemented in standard quadratic forms. Standard uniform and 1,000 random initializations were used to test optimizer's search ability under different starting conditions for prostate and head-and-neck cases.<i>Results</i>. The MLT-assisted framework demonstrated comparable or superior plan quality to classical optimization by achieving lower objective values, improved DVHs and capturing finer modulation details in fluence maps. For gEUD-based optimization, it effectively explored beam weight elevations that classical optimization could only reach with stricter convergence criteria and many more iterations. The quality differences primarily stemmed from convergence speed. The MLT-assisted framework required significantly fewer evaluations and iterations to achieve similar or better results. Optimization on random initial maps further demonstrated that it was more robust and less likely to be trapped. It does not require stricter convergence criteria or extended runs to reach high-quality optima, making it more efficient and reliable.<i>Conclusion</i>. This framework leverages ML toolkits in a novel way, enabling faster convergence, greater robustness and handling of complex constraints. As the first study of its kind, it establishes MLT-assisted optimization as a viable and effective alternative to classical methods.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":"11 3","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143975952","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":"A deep learning approach for quantifying CT perfusion parameters in stroke.","authors":"Wanning Zeng, Yang Li, Jeff L Zhang, Tong Chen, Ke Wu, Xiaopeng Zong","doi":"10.1088/2057-1976/adc9b6","DOIUrl":"10.1088/2057-1976/adc9b6","url":null,"abstract":"<p><p><i>Objective</i>. Computed tomography perfusion (CTP) imaging is widely used for assessing acute ischemic stroke. However, conventional methods for quantifying CTP images, such as singular value decomposition (SVD), often lead to oscillations in the estimated residue function and underestimation of tissue perfusion. In addition, the use of global arterial input function (AIF) potentially leads to erroneous parameter estimates. We aim to develop a method for accurately estimating physiological parameters from CTP images.<i>Approach</i>. We introduced a Transformer-based network to learn voxel-wise temporal features of CTP images. With global AIF and concentration time curve (CTC) of brain tissue as inputs, the network estimated local AIF and flow-scaled residue function. The derived parameters, including cerebral blood flow (CBF) and bolus arrival delay (BAD), were validated on both simulated and patient data (ISLES18 dataset), and were compared with multiple SVD-based methods, including standard SVD (sSVD), block-circulant SVD (cSVD) and oscillation-index SVD (oSVD).<i>Main results.</i>On data simulating multiple scenarios, local AIF estimated by the proposed method correlated with true AIF with a coefficient of 0.97 ± 0.04 (P < 0.001), estimated CBF with a mean error of 4.95 ml/100 g min^-1, and estimated BAD with a mean error of 0.51 s; the latter two errors were significantly lower than those of the SVD-based methods (P < 0.001). The CBF estimated by the SVD-based methods were underestimated by 10% ∼ 15%. For patient data, the CBF estimates of the proposed method were significantly higher than those of the sSVD method in both normally perfused and ischemic tissues, by 13.83 ml/100 g min^-1or 39.33% and 8.55 ml/100 g min^-1or 57.73% (P < 0.001), respectively, which was in agreement with the simulation results.<i>Significance</i>. The proposed method is capable of estimating local AIF and perfusion parameters from CTP images with high accuracy, potentially improving CTP's performance and efficiency in diagnosing and treating ischemic stroke.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143802262","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":"Evaluating the dosimetric and positioning accuracy of a deep learning based synthetic-CT model for liver radiotherapy treatment planning.","authors":"Lamyaa Aljaafari, Richard Speight, David L Buckley, Bashar Al-Qaisieh, Sebastian Andersson, David Bird","doi":"10.1088/2057-1976/adc818","DOIUrl":"10.1088/2057-1976/adc818","url":null,"abstract":"<p><p><i>Background and purpose.</i>An MRI-only workflow requires synthetic computed tomography (sCT) images to enable dose calculation. This study evaluated the dosimetric and patient positioning accuracy of deep learning-generated sCT for liver radiotherapy.<i>Methods and materials.</i>sCT images were generated for eleven patients using a CycleGAN algorithm. Clinical volumetric modulated arc treatment plans (VMAT) were calculated on CT and recalculated on sCT, and dose differences were assessed using dose volume histogram (DVH). For position verification, the sCT images were validated as reference images to 4D cone beam computed tomography (4D CBCT) by calculating the translational and rotational differences between sCT and CT registrations to 4D CBCT.<i>Results.</i>The mean dose differences for the planning target volume (PTV) and organs at risk (OAR) between the CT and sCT plans were 0.0% and < 0.5%, respectively. For positioning verification, the systematic translational and rotational differences were < 0.5 mm and < 0.5°, respectively in all directions.<i>Conclusion.</i>This is the first study to validate a sCT model for liver cancer in terms of both dosimetry and patient positioning, marking a significant step in demonstrating the feasibility of an MRI-only workflow for treating liver cancer. The generated sCTs showed dosimetric differences within clinically acceptable levels and were successfully used as reference images for treatment position verification. This CycleGAN model is accessible through the research version of a commercial vendor, with potential for development as a clinical solution.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143771277","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":"Portal dose image prediction using Monte Carlo generated transmission energy fluence maps of dynamic radiotherapy treatment plans: a deep learning approach.","authors":"Peter Andersson, Magnus Båth, Åsa Palm, Roumiana Chakarova","doi":"10.1088/2057-1976/adc73f","DOIUrl":"10.1088/2057-1976/adc73f","url":null,"abstract":"<p><p><i>Aims.</i>This work aims to develop and investigate the feasibility of a hybrid model combining Monte Carlo (MC) simulations and deep learning (DL) to predict electronic portal imaging device (EPID) images based on MC-generated exit phase space energy fluence maps from dynamic radiotherapy treatment plans. Such predicted images can be used as reference images during<i>in vivo</i>dosimetry.<i>Materials and methods</i>. MC simulations involving a Varian True Beam linear accelerator model were performed using the EGSnrc code package. Two custom variants of the U-Net architecture were employed. The MLC dynamic chair sequence and 17 clinical treatment plans, spanning various cancer types and delivery methods, were used to acquire experimental data, and in the MC simulations. The proposed method was tested through 2D gamma index analysis, comparing predicted and measured EPID images.<i>Results</i>. Results showed gamma passing rates of 38.65%, 74.16% and 96.17% (minimum, median, maximum) for a simpler model variant and 52.72%, 80.61% and 96.80% for the more complex model variant.<i>Conclusion</i>. The study highlights the feasibility of integrating MC and DL methodologies for<i>in vivo</i>dosimetry quality assurance in complex radiotherapy delivery.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143751006","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":"Rapid dose prediction for lung CyberKnife radiotherapy plans utilizing a deep learning approach by incorporating dosimetric features delivered by noncoplanar beams.","authors":"Shengxiu Jiao, Honghao Xu, Jia Luo, Lin Lei, Peng Zhou","doi":"10.1088/2057-1976/adc697","DOIUrl":"10.1088/2057-1976/adc697","url":null,"abstract":"<p><p><i>Purpose</i>. The dose distribution of lung cancer patients treated with the CyberKnife (CK) system is influenced by various factors, including tumor location and the direction of CK beams. The objective of this study is to present a deep learning approach that integrates CK beam dose characteristics into CK planning dose calculations.<i>Methods</i>. The inputs utilized for the geometry and dosimetry method (GDM) include the patient's CT, the PTV structure, and multiple CK noncoplanar beam dose deposition features. The dose distributions were calculated using the Monte Carlo (MC) algorithm provided with the CK system and served as the ground truth dose label. Additionally, dose prediction was conducted through the geometry method (GM) for comparative analysis. The gamma pass rate<i>γ</i>(1 mm,1%),<i>γ</i>(2 mm,2%) and<i>γ</i>(3 mm,3%) were calculated between the predicted model and the MC method.<i>Results</i>. Compared to the GDM, the GM shows a significant dose difference from the MC approach in the low-dose region (<5 Gy) outside the target created by the various CK noncoplanar beams. The GDM increased the<i>γ</i>(1 mm, 1%) from 49.55% to 81.69%,<i>γ</i>(2 mm, 2%) from 73.24% to 98.11% and the<i>γ</i>(3 mm, 3%) from 81.69% to 99.37% when compared with the GM's results.<i>Conclusions</i>. This work proposed a deep learning dose calculation method by using patient geometry and dosimetry features in CK plans. The proposed method extends the geometric and dosimetric feature-driven deep learning dose calculation method to CK application scenarios, which has a great potential to accelerate the CK planning dose calculation and improve the planning efficiency.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143742101","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":"Timing performance evaluation of a dual-Axis rotational PET system according to NEMA NU 4-2008 standards: A simulation study.","authors":"P M C C Encarnação, P M M Correia, F M Ribeiro, J F C A Veloso","doi":"10.1088/2057-1976/adc5f5","DOIUrl":"10.1088/2057-1976/adc5f5","url":null,"abstract":"<p><p><i>Introduction:</i>Positron Emission Tomography (PET) imaging's diagnostic accuracy is dependent on the scanner design and image quality, which is affected by several factors including the coincidence timing window (CTW). NEMA NU 4-2008 procedures are commonly used to assess and compare PET systems performance, including dual rotation technologies like easyPET.3D, known for high-spatial resolution and reduced parallax contribution.<i>Aim:</i>This study aims to identify easyPET.3D's optimal performance based on NEMA standards. In addition, explores the impact of different CTWs on PET image quality by comparing simulated electronics capable of a 300 ps CTW with a 40 ns CTW.<i>Results:</i>When the data is filtered by a 40 ns CTW, a sub-millimetre resolution at the field-of-view (FoV) centre and a constant behaviour in the radial direction are achieved. The absolute sensitivity was 0.18% with a maximum value of 0.31%, for a 15 mm transverse FoV. The noise equivalent count rate peaked at 18 MBq with 249 cps. Recovery coefficients ranged from 17% to 90%, and spilled-over ratios were 0.32 (water) and 0.41 (air).<i>Conclusions:</i>A shorter 300 ps CTW primarily impacted PET dynamic range, allowing higher activity acquisitions, with no significant changes in resolution and sensitivity under NEMA test conditions. As for the image quality test, the 300 ps CTW images have less background, better SOR values, and similar RC values when comparing the 40 ns CTW.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143717891","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}