Physical and Engineering Sciences in Medicine最新文献

{"title":"Towards ultra-low-dose CT for detecting pulmonary nodules using DenseNet.","authors":"Ching-Ching Yang","doi":"10.1007/s13246-025-01520-6","DOIUrl":"10.1007/s13246-025-01520-6","url":null,"abstract":"<p><p>Low-radiation techniques should be used to detect and follow lung nodules on CT images, but reducing radiation dose to ultra-low-dose CT with submilliSievert dose level would drastically impede image quality and sensitivity for nodule detection. This study investigated the feasibility of using DenseNet to suppress image noise in ultra-low-dose CT for lung cancer screening. DenseNet was trained using input-label pairs from 1, 2, 4, and 6 patients. After training, the model was tested with chest CT from 14 patients that were not used in training process. Seven patients have solid nodules and 7 patients have subsolid nodules. Root mean square error (RMSE) and peak signal-to-noise ratio (PSNR) were calculated to quantify the difference between reference and test images. The contrast-to-noise ratio (CNR) between lung nodule and lung parenchyma was calculated to evaluate the target detectability of chest CT. Subjective image quality assessment was performed using 4-point ranking scale to evaluate the visual quality of CT images perceived by end user. Substantial improvements in RMSE and PSNR were observed after denoising. The lung nodules in denoised images could be distinguished more easily in comparison with those in the original ultra-low-dose CT, which is supported by the CNRs and subjective image quality scores. The comparison of intensity profiles for lung nodules demonstrated that the image noise in ultra-low-dose CT could be suppressed effectively after denoising without causing edge blurring or variation in Hounsfield unit (HU) values. A two-sample t-test revealed no statistically significant differences between full-dose CT and denoised ultra-low-dose CT in the evaluation of lung nodules, lung parenchyma, paraspinal muscle, or vertebral body. Since the linear no-threshold model suggests that no amount of ionizing radiation is entirely risk-free, the quest for further dose reduction remains a consistently important focus in radiology. Overall, our findings suggest that DenseNet could be a viable approach for reducing image noise in ultra-low-dose CT scans used for lung cancer screening.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":"379-389"},"PeriodicalIF":2.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143383564","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":"Publisher Correction to: Sensitivity improvement of a deuterium-deuterium neutron generator based in vivo neutron activation analysis (IVNAA) system.","authors":"Song Yue, Sana Tabbassum, Elizabeth Helen Jaye, Cheryl A M Anderson, Linda H Nie","doi":"10.1007/s13246-024-01497-8","DOIUrl":"10.1007/s13246-024-01497-8","url":null,"abstract":"","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":"47"},"PeriodicalIF":2.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142630467","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":"Significance of gender, brain region and EEG band complexity analysis for Parkinson's disease classification using recurrence plots and machine learning algorithms.","authors":"Divya Sasidharan, V Sowmya, E A Gopalakrishnan","doi":"10.1007/s13246-025-01521-5","DOIUrl":"10.1007/s13246-025-01521-5","url":null,"abstract":"<p><p>Parkinson Disease (PD) is a complex neurological disorder attributed by loss of neurons generating dopamine in the SN per compacta. Electroencephalogram (EEG) plays an important role in diagnosing PD as it offers a non-invasive continuous assessment of the disease progression and reflects these complex patterns. This study focuses on the non-linear analysis of resting state EEG signals in PD, with a gender-specific, brain region-specific, and EEG band-specific approach, utilizing recurrence plots (RPs) and machine learning (ML) algorithms for classification. For this an open EEG dataset consisting of 14 PD and 14 healthy (HC) subjects is utilized. Recurrence plots and cross-recurrence plots (CRPs) were constructed for each frequency band and brain region, extracting complexity measures such as determinism (DET) and entropy (ENT). The interpretability of the ML model decisions is investigated using explainability technique. The scattered distribution of points in RPs of male PD individuals reflects the complex and dynamic nature of abnormal brain function. Also, CRPs confirms the enhanced effect of Beta Gamma synchronization during PD in the Parietal region. Low DET and high ENT corresponds to the complex non-linear characteristics of EEG signals and brain neuronal circuits during PD condition in male subjects. The extracted recurrence features served as inputs to the ML models, which achieved high classification performance, across all the scenarios. This study demonstrates the potential of recurrence plot-based complexity analysis combined with machine learning for the gender-specific, region-specific, and band-specific assessment of EEG signals during resting state in Parkinson's disease.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":"391-407"},"PeriodicalIF":2.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143048411","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":"Performance improvements of virtual monoenergetic images in photon-counting detector CT compared with dual source dual-energy CT: Fourier-based assessment.","authors":"Hiroki Kawashima, Katsuhiro Ichikawa, Ryoichi Yoshida, Takuto Katayama, Makoto Arimoto, Jun Kataoka, Hiroji Nagata, Satoshi Kobayashi","doi":"10.1007/s13246-024-01499-6","DOIUrl":"10.1007/s13246-024-01499-6","url":null,"abstract":"<p><p>To confirm the performance improvement of virtual monoenergetic images (VMIs) for iodine contrast tasks in a clinical photon-counting detector CT (PCD CT) using Fourier-based assessment, compared with those in the latest-generation dual-source dual-energy CT (DECT). A water-filled bath with a diameter of 300 mm, which contains rod-shaped phantoms equivalent to diluted iodine (2 and 12 mg/mL), was scanned using PCD CT and DECT at 15, 7.5, and 3 mGy. VMIs were generated without any iterative reconstruction algorithm. Task transfer function (TTF), noise power spectrum (NPS), and slice sensitivity profile were evaluated for VMIs at 70 and 40 keV. The detectability index (d') and the squared system performance function (SPF²) calculated by TTF²/NPS were compared. At 40 keV, the d' values of PCD CT were higher (percentage increase of 25.7-39.9%) than those of DECT, whereas at 70 keV, the difference was rather small. The SPF² values at 40 keV of PCD CT grew notably higher than those of DECT as the spatial frequency increased. The higher SPF² values endorsed the lower image noise and the sharper edge of the rod phantom as observed. The d' and SPF² in VMIs at 40 keV of PCD CT were notably higher than those of DECT, which endorsed the clinical advantages of PCD CT that had been previously reported in various studies.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":"143-153"},"PeriodicalIF":2.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11996975/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142808260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improvement of plan quality in whole-breast radiation following BCS using feasibility DVH by less-experienced planners.","authors":"Yun Zhang, Yuling Huang, Mingming Luo, Xingxing Yuan, Xiaoping Wang, Changfei Gong","doi":"10.1007/s13246-024-01493-y","DOIUrl":"10.1007/s13246-024-01493-y","url":null,"abstract":"<p><p>Variability in plan quality of radiotherapy is commonly attributed to the planner's skill rather than technological parameters. While experienced planners can set reasonable parameters before optimization, less experienced planners face challenges. This study aimed to assess the quality of volumetric-modulated arc therapy (VMAT) in patients with left-sided breast cancer following breast-conserving surgery. Twenty-eight patients requiring whole-breast irradiation were randomly selected for inclusion. Each patient underwent two VMAT treatment plans: one optimized by an experienced planner (VMAT-EXP group) and the other designed by a less experienced planner using feasibility dose-volume histogram (FDVH) parameters from PlanIQ (VMAT-FDVH group). Both plans aimed to deliver a prescription dose of 50 Gy in 25 fractions to the planning target volume (PTV). Dosimetry parameters for the PTV and organs at risk (OARs) were compared between the two groups. Both the VMAT-EXP and VMAT-FDVH groups met the clinical plan goals for PTV and OARs. VMAT-FDVH demonstrated a PTV coverage and homogeneity comparable to those of VMAT-EXP. Compared to VMAT-EXP plans, VMAT-FDVH plans resulted in a significant reduction in the mean ipsilateral lung dose, with an average decrease of 0.9 Gy (8.5 Gy vs. 7.6 Gy, P < 0.001). The V5Gy and V20Gy of the ipsilateral lung were also reduced by 3.2% and 1.8%, respectively. Minor differences were observed in the heart, contralateral lung, breast, and liver. Personalized objectives derived from the feasibility DVH tool facilitated the generation of acceptable VMAT plans. Less experienced planners achieved lower doses to the ipsilateral lung while maintaining adequate target coverage and homogeneity. These findings suggest the potential for the effective use of VMAT in in patients with left-sided breast cancer following breast-conserving surgery, especially when guided by feasibility DVH parameters.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":"103-110"},"PeriodicalIF":2.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11996942/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142606834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"<ArticleTitle xmlns:ns0=\"http://www.w3.org/1998/Math/MathML\">Re-evaluation of <ns0:math><ns0:mmultiscripts><ns0:mrow /> <ns0:mrow /> <ns0:mn>18</ns0:mn></ns0:mmultiscripts> </ns0:math> F-FDG absorbed and effective dose in adult and pediatric phantoms using DoseCalcs Monte Carlo platform: a validation study.","authors":"Tarik El Ghalbzouri, Tarek El Bardouni, Jaafar El Bakkali, Otman El Hajjaji, Hicham Satti, Assia Arectout, Maryam Hadouachi, Randa Yerru","doi":"10.1007/s13246-024-01492-z","DOIUrl":"10.1007/s13246-024-01492-z","url":null,"abstract":"<p><p>Positron emission tomography (PET) using <math><mmultiscripts><mrow></mrow> <mrow></mrow> <mn>18</mn></mmultiscripts> </math> F-FDG is a well-known modality for the diagnosis of various diseases in patients of different ages, sexes, and states of health, which implies that internal radiation dosimetry is highly desired for different phantom anatomies. In this study, we validate \"DoseCalcs,\" a new Monte Carlo platform that combines personalized internal dosimetry calculations with Monte Carlo simulations. To achieve that, we used the specific absorbed fraction (SAF) calculated by DoseCalcs and those from ICRP publication 133 to estimate the absorbed dose per injected activity (AD/IA) and effective dose per injected activity (ED/IA) for <math><mmultiscripts><mrow></mrow> <mrow></mrow> <mn>18</mn></mmultiscripts> </math> F-FDG. The investigation focused on various voxelized phantoms representing different age groups, including adult male and female, and pediatric phantoms of various ages, from newborn to 15 years old. Using the DoseCalcs Monte Carlo platform, we have simulated the emission of <math><mmultiscripts><mrow></mrow> <mrow></mrow> <mn>18</mn></mmultiscripts> </math> F-FDG positrons based on the energy spectrum provided in ICRP publication 107. The results demonstrated the impact of anatomical differences and different organ/tissue compositions on radiation absorption, with significant variations in the AD/IA across different phantoms. Interestingly, organs/tissues near the emission source showed higher AD/IA, highlighting the anatomical dependence on the phantom. When our results were compared to established reference data, especially from ICRP128, most organs/tissues had good agreement. Still, some cases have shown differences. This shows how important it is to use accurate radionuclide data and biokinetic modeling in internal dosimetry calculations. Furthermore, we compared AD/IA and ED/IA values calculated in newborns by DoseCalcs with those derived from alternative codes, MCNP and EGSnrc. While the results generally exhibited consistency, subtle variations underscored the influence of biokinetics modeling choices and computational methodologies. Overall, this research contributes valuable insights into the precision of internal dosimetry calculations using \"DoseCalcs-Gui\" by providing one platform for Monte Carlo simulation and personalized internal dosimetry in nuclear medicine. The DoseCalcs platform is free for research and available for download at www.github.com/TarikEl/DoseCalcs-Gui .</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":"87-102"},"PeriodicalIF":2.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142584449","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 new HCM heart sound classification method based on weighted bispectrum features.","authors":"Fang Yu, Huang Zhiyuan, Leng Hongxia, Dongbo Liu, Wang Weibo","doi":"10.1007/s13246-024-01506-w","DOIUrl":"10.1007/s13246-024-01506-w","url":null,"abstract":"<p><p>Hypertrophic cardiomyopathy (HCM), including obstructive HCM and non-obstructive HCM, can lead to sudden cardiac arrest in adolescents and athletes. Early diagnosis and treatment through auscultation of different types of HCM can prevent the occurrence of malignant events. However, it is challenging to distinguish the pathological information of HCM related to differential left ventricular outflow tract pressure gradients. To address this issue, a classification method based on weighted bispectrum features of heart sounds (HSs) is proposed for efficient and cost-effective HCM analysis. Preprocessing is first applied to remove background noise during HS acquisition. Then, the bispectrum contour map is calculated, and 56-dimensional features are extracted to represent the pathological information of HCM. Next, an adaptive threshold weighting mutual information method is proposed for feature selection and weighted fusion. Finally, the CNN-RF classifier model is built to automatically identify different types of HCM cases. A clinical dataset of normal and two types of HCM HSs is utilized for validation. The results show that the proposed method performs well, with a classification accuracy reaching 94.4%. It provides a reliable reference for HCM diagnosis in young patients in clinical settings.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":"207-220"},"PeriodicalIF":2.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143068837","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":"PET radiomics-based lymphovascular invasion prediction in lung cancer using multiple segmentation and multi-machine learning algorithms.","authors":"Seyyed Ali Hosseini, Ghasem Hajianfar, Pardis Ghaffarian, Milad Seyfi, Elahe Hosseini, Atlas Haddadi Aval, Stijn Servaes, Mauro Hanaoka, Pedro Rosa-Neto, Sanjeev Chawla, Habib Zaidi, Mohammad Reza Ay","doi":"10.1007/s13246-024-01475-0","DOIUrl":"10.1007/s13246-024-01475-0","url":null,"abstract":"<p><p>The current study aimed to predict lymphovascular invasion (LVI) using multiple machine learning algorithms and multi-segmentation positron emission tomography (PET) radiomics in non-small cell lung cancer (NSCLC) patients, offering new avenues for personalized treatment strategies and improving patient outcomes. One hundred and twenty-six patients with NSCLC were enrolled in this study. Various automated and semi-automated PET image segmentation methods were applied, including Local Active Contour (LAC), Fuzzy-C-mean (FCM), K-means (KM), Watershed, Region Growing (RG), and Iterative thresholding (IT) with different percentages of the threshold. One hundred five radiomic features were extracted from each region of interest (ROI). Multiple feature selection methods, including Minimum Redundancy Maximum Relevance (MRMR), Recursive Feature Elimination (RFE), and Boruta, and multiple classifiers, including Multilayer Perceptron (MLP), Logistic Regression (LR), XGBoost (XGB), Naive Bayes (NB), and Random Forest (RF), were employed. Synthetic Minority Oversampling Technique (SMOTE) was also used to determine if it boosts the area under the ROC curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE). Our results indicated that the combination of SMOTE, IT (with 45% threshold), RFE feature selection and LR classifier showed the best performance (AUC = 0.93, ACC = 0.84, SEN = 0.85, SPE = 0.84) followed by SMOTE, FCM segmentation, MRMR feature selection, and LR classifier (AUC = 0.92, ACC = 0.87, SEN = 1, SPE = 0.84). The highest ACC belonged to the IT segmentation (with 45 and 50% thresholds) alongside Boruta feature selection and the NB classifier without SMOTE (ACC = 0.9, AUC = 0.78 and 0.76, SEN = 0.7, and SPE = 0.94, respectively). Our results indicate that selection of appropriate segmentation method and machine learning algorithm may be helpful in successful prediction of LVI in patients with NSCLC with high accuracy using PET radiomics analysis.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":"1613-1625"},"PeriodicalIF":2.4,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11666702/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142120941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PET/CT-based 3D multi-class semantic segmentation of ovarian cancer and the stability of the extracted radiomics features.","authors":"Mohammad Hossein Sadeghi, Sedigheh Sina, Mehrosadat Alavi, Francesco Giammarile, Chai Hong Yeong","doi":"10.1007/s13246-024-01485-y","DOIUrl":"10.1007/s13246-024-01485-y","url":null,"abstract":"<p><p>Accurate segmentation of ovarian cancer (OC) lesions in PET/CT images is essential for effective disease management, yet manual segmentation for radiomics analysis is labor-intensive and time-consuming. This study introduces the application of a 3D U-Net deep learning model, leveraging advanced 3D networks, for multi-class semantic segmentation of OC in PET/CT images and assesses the stability of the extracted radiomics features. Utilizing a dataset of 3120 PET/CT images from 39 OC patients, the dataset was divided into training (70%), validation (15%), and test (15%) subsets to optimize and evaluate the model's performance. The 3D U-Net model, especially with a VGG16 backbone, achieved notable segmentation accuracy with a Dice score of 0.74, Precision of 0.76, and Recall of 0.78. Additionally, the study demonstrated high stability in radiomics features, with over 85% of PET and 84% of CT image features showing high intraclass correlation coefficients (ICCs > 0.8). These results underscore the potential of automated 3D U-Net-based segmentation to significantly enhance OC diagnosis and treatment planning. The reliability of the extracted radiomics features from automated segmentation supports its application in clinical decision-making and personalized medicine. This research marks a significant advancement in oncology diagnostics, providing a robust and efficient method for segmenting OC lesions in PET/CT images. By addressing the challenges of manual segmentation and demonstrating the effectiveness of 3D networks, this study contributes to the growing body of evidence supporting the application of artificial intelligence in improving diagnostic accuracy and patient outcomes in oncology.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":"1739-1749"},"PeriodicalIF":2.4,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142298927","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":"Unifying gamma passing rates in patient-specific QA for VMAT lung cancer treatment based on data assimilation.","authors":"Tomohiro Ono, Takanori Adachi, Hideaki Hirashima, Hiraku Iramina, Noriko Kishi, Yukinori Matsuo, Mitsuhiro Nakamura, Takashi Mizowaki","doi":"10.1007/s13246-024-01448-3","DOIUrl":"10.1007/s13246-024-01448-3","url":null,"abstract":"<p><p>This study aimed to identify systematic errors in measurement-, calculation-, and prediction-based patient-specific quality assurance (PSQA) methods for volumetric modulated arc therapy (VMAT) on lung cancer and to standardize the gamma passing rate (GPR) by considering systematic errors during data assimilation. This study included 150 patients with lung cancer who underwent VMAT. VMAT plans were generated using a collapsed-cone algorithm. For measurement-based PSQA, ArcCHECK was employed. For calculation-based PSQA, Acuros XB was used to recalculate the plans. In prediction-based PSQA, GPR was forecasted using a previously developed GPR prediction model. The representative GPR value was estimated using the least-squares method from the three PSQA methods for each original plan. The unified GPR was computed by adjusting the original GPR to account for systematic errors. The range of limits of agreement (LoA) were assessed for the original and unified GPRs based on the representative GPR using Bland-Altman plots. For GPR (3%/2 mm), original GPRs were 94.4 ± 3.5%, 98.6 ± 2.2% and 93.3 ± 3.4% for measurement-, calculation-, and prediction-based PSQA methods and the representative GPR was 95.5 ± 2.0%. Unified GPRs were 95.3 ± 2.8%, 95.4 ± 3.5% and 95.4 ± 3.1% for measurement-, calculation-, and prediction-based PSQA methods, respectively. The range of LoA decreased from 12.8% for the original GPR to 9.5% for the unified GPR across all three PSQA methods. The study evaluated unified GPRs that corrected for systematic errors. Proposing unified criteria for PSQA can enhance safety regardless of the methods used.</p>","PeriodicalId":48490,"journal":{"name":"Physical and Engineering Sciences in Medicine","volume":" ","pages":"1337-1348"},"PeriodicalIF":2.4,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141427990","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}