[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117390-en":3,"doc-seo-117390-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117390,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Machine Learning-Based Objective Evaluation Model of CTPA Image Quality - A Multi-Center Study","A retrospective multi-center study develops an interpretable machine learning model for objective assessment of CT pulmonary angiography (CTPA) image quality. Data included 99 CTPA patients (March 2022–January 2023) plus two public datasets (FUMPE and CAD-PE), totaling 150 cases, randomly split 105 training and 45 testing (7:3). CT values and noise standard deviations were measured in 11 regions of interest, with two radiologists providing anonymous scores aggregated into MOS. Lasso and correlation-based feature selection retained three key features, and a random forest regression model achieved strong agreement with subjective quality.","International Journal of General Medicine downloaded from [https://www.dovepress.com/](https://www.dovepress.com/)  \nFor personal use only.  \nInternational Journal of General Medicine  \nOpen Access Full Text Article ORIGINAL RESEARCH  \nMachine Learning-Based Objective Evaluation Model of CTPA Image Quality: A Multi-Center Study  \nQihang Sun 1 , Zhongxiao Liu 1 , Tao Ding 1 , Changzhou Shi2 , Nailong Hou2 , Cunjie Sun 1  \n1Department of Medical Imaging, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, People’s Republic of China; 2School of Medical Imaging, Xuzhou Medical University, Xuzhou, People’s Republic of China  \nCorrespondence: Cunjie Sun, Affiliated Hospital of Xuzhou Medical University, No. 99 West Huaihai Road, Quanshan District, Xuzhou City, Jiangsu Province, 221006, People’s Republic of China, Tel +8618052268897, Email [cunjiesxyfy@163.com](cunjiesxyfy@163.com)  \n\n| Purpose: This study aims to develop a machine learning-based model for the objective assessment of CT pulmonary angiography (CTPA) image quality.\u003Cbr>Patients and Methods: A retrospective analysis was conducted using data from 99 patients who underwent CTPA between March 2022 and January 2023, alongside two public datasets, FUMPE (21 cases) and CAD-PE (30 cases) . In total, 150 cases from multiple centers were included in this analysis. The dataset was randomly split into a training set (105 cases) and a testing set (45 cases) in a 7:3 ratio. CT values and their standard deviations (SD) were measured in 11 specific regions of interest, and two radiologists independently assigned anonymous random scores to the images. The average of their subjective scores was used asthe target output for the model, which was the mean opinion score (MOS) for image quality. Feature selection was performed using the Lasso algorithm and Pearson correlation coefficient, and a random forest regression model was constructed. Model performance was evaluated using mean square error (MSE), coefficient of determination (R²), Pearson linear correlation coefficient (PLCC), Spearman rank correlation coefficient (SRCC), and Kendall rank correlation coefficient (KRCC) .\u003Cbr>Results: After feature selection, three key features were retained: main pulmonary artery CT value, ascending aorta CT value, and the difference in noise values between the left and right main pulmonary arteries. The random forest regression model constructed achieved MSE, R2_score, PLCC, SRCC, and KRCC values of 0.2001, 0.6695, 0.8682, 0.8694, 0.7363, respectively, on the testing set. Conclusion: This study successfully developed an interpretable machine learning-based model for the objective assessment of CTPA image quality. The model offers effective support for improving image quality control efficiency and precision. However, the limited sample size may affect the model’s generalizability, so it’s essential to conduct further research with larger datasets.\u003Cbr>Keywords: CT pulmonary angiography, machine learning, image quality, data interpretation |\n| --- |\n| Introduction\u003Cbr>Pulmonary embolism (PE) is a critical and potentially fatal condition caused by the blockage of pulmonary arteries or their branches, typically resulting from blood clots that often originate in deep vein thrombosis.1–3 Computed tomographic pulmonary angiography (CTPA) is currently the most commonly used and effective method for diagnosing PE, with PE typically appearing as a filling defect in CTPA images.4,5 However, the accuracy of CTPA in diagnosing PE can be compromised by several factors, including improper scan timing and the presence of artifacts, which may degrade image quality. Such degradation can negatively affect post-processing and hinder the accurate diagnosis of PE. Therefore, evaluating image quality is a critical aspect of quality control in imaging systems for PE diagnosis.6–8\u003Cbr>Currently, there are two main methods for distinguishing between artifacts and lesions: one is based on subjective image quality assessm","cbCaiuV3kw9pC2oj","https://ap.wps.com/l/cbCaiuV3kw9pC2oj","pdf",3862045,1,9,"English","en",105,"# Purpose\n# Patients and Methods\n## Data and datasets\n## Feature selection and model building\n# Results\n# Conclusion","[{\"question\":\"What is the purpose of the machine learning model in this study?\",\"answer\":\"To provide an objective and interpretable method for assessing CTPA image quality based on measurable imaging features rather than relying solely on subjective evaluation.\"},{\"question\":\"How were labels for image quality created for model training?\",\"answer\":\"Two radiologists assigned anonymous random scores, and their averaged scores were used as the target output, represented by the mean opinion score (MOS).\"},{\"question\":\"Which features were retained after feature selection and how did the model perform?\",\"answer\":\"Three features were kept: main pulmonary artery CT value, ascending aorta CT value, and the noise value difference between left and right main pulmonary arteries. On the testing set, the model reached MSE 0.2001, R² 0.6695, PLCC 0.8682, SRCC 0.8694, and KRCC 0.7363.\"}]","Machine Learning-Based Objective Evaluation Model of CTPA Image Quality - A Multi-Center Study | PDF",1785675590,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-based-objective-evaluation-model-of-ctpa-image-quality-a-multi-center-study","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-based-objective-evaluation-model-of-ctpa-image-quality-a-multi-center-study/117390/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the purpose of the machine learning model in this study?","Question",{"text":75,"@type":76},"To provide an objective and interpretable method for assessing CTPA image quality based on measurable imaging features rather than relying solely on subjective evaluation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were labels for image quality created for model training?",{"text":80,"@type":76},"Two radiologists assigned anonymous random scores, and their averaged scores were used as the target output, represented by the mean opinion score (MOS).",{"name":82,"@type":73,"acceptedAnswer":83},"Which features were retained after feature selection and how did the model perform?",{"text":84,"@type":76},"Three features were kept: main pulmonary artery CT value, ascending aorta CT value, and the noise value difference between left and right main pulmonary arteries. On the testing set, the model reached MSE 0.2001, R² 0.6695, PLCC 0.8682, SRCC 0.8694, and KRCC 0.7363.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]