Quantitative Imaging in Medicine and Surgery最新文献

{"title":"Case study of a 47-year-old long COVID patient diagnosed with Alzheimer's disease.","authors":"Yang Yao, Shichang Liu, Yiyao Zhang, Chang Liu, Jingsong Shi, Binbin Chen, Zhuo Yang, Tao Zhang, Zhanyong Li, Shan Gao","doi":"10.21037/qims-2025-125","DOIUrl":"10.21037/qims-2025-125","url":null,"abstract":"","PeriodicalId":54267,"journal":{"name":"Quantitative Imaging in Medicine and Surgery","volume":"15 7","pages":"6535-6540"},"PeriodicalIF":2.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12290769/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144735441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the incidence rate and imaging differential diagnosis of anterior mediastinal lesions: an 11-year retrospective study based on 2,626 cases.","authors":"Jiaqi Chen, Linlin Qi, Jianing Liu, Fenglan Li, Shulei Cui, Jianwei Wang","doi":"10.21037/qims-2025-13","DOIUrl":"10.21037/qims-2025-13","url":null,"abstract":"<p><strong>Background: </strong>There are many complex types of anterior mediastinal lesions, and preoperative differential diagnosis is difficult in clinical practice. This study aimed to explore the incidence of different anterior mediastinal lesions and their differential diagnosis based on clinical and imaging features.</p><p><strong>Methods: </strong>We examined the local incidence of anterior mediastinal lesions and their different types in 2,626 patients with anterior mediastinal lesions. Among them, we explored the diagnostic utility of clinical, radiological, and pathological characteristics of 1,809 patients with complete imaging data.</p><p><strong>Results: </strong>The incidence rate of anterior mediastinal lesions was about 0.4%. Thymic epithelial tumors (TETs) showed the highest incidence rate (56.1%), with most patients aged 50-60 years. Lymphoma was the second most common lesion (16.3%). Age, average diameter, boundaries, calcification, average computed tomography (CT) value, surrounding tissues invasion (vascular, pleural, and lung), pericardial effusion, mediastinal enlargement of lymph nodes, and distant metastasis were identified as statistically significant risk factors for distinguishing TETs, with the areas under the curve (AUCs) of the training and validation sets of 0.94 and 0.93, respectively. Average diameter, edges, boundaries, average CT value, surrounding tissue invasion, and mediastinal lymph node enlargement were risk stratification factors for TETs [AUC: 0.865, 95% confidence interval (CI): 0.842-0.888; sensitivity, 72.0%; specificity, 85.6%]. Other malignant tumors included lymphoma, germ cell tumors, hematolymphoid tumors, and mesenchymal tumors. Benign lesions included simple cysts, mature teratomas, mesenchymal tumors, thymic tissue/hyperplasia, giant lymph node hyperplasia, inflammation, and retrosternal goiters.</p><p><strong>Conclusions: </strong>We observed a low incidence rate of anterior mediastinal lesions. Age was associated with various types of anterior mediastinal lesions, with TETs showing the highest incidence. A systematic diagnostic approach for anterior mediastinal lesions can be developed based on the clinical and imaging features.</p>","PeriodicalId":54267,"journal":{"name":"Quantitative Imaging in Medicine and Surgery","volume":"15 7","pages":"6465-6485"},"PeriodicalIF":2.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12290781/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144735453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diabetic retinal vessel segmentation algorithm based on MA-DUNet.","authors":"Jian-Zhi Deng, Yan Yang, Yong-Ping Guo, Yan-Hua He, Kang-Cheng Chen, Yun-Chun Lu, Yue-Han Zhou, Bin Xiong","doi":"10.21037/qims-24-2267","DOIUrl":"10.21037/qims-24-2267","url":null,"abstract":"<p><strong>Background: </strong>Precise retinal vessel segmentation is crucial for the diagnosis of ocular diseases. This meticulous process can promptly capture retinal vessel abnormalities, which in turn can facilitate early diagnosis and timely treatment. However, retinal vessels exhibit complex branching patterns with different diameters and contrasts, making it difficult to segment tiny vessels and low-contrast areas, and identify subtle terminal branches. This study aimed to improve the accuracy of retinal vessel segmentation and ultimately improve the efficiency of clinical diagnosis.</p><p><strong>Methods: </strong>In this study, we proposed the Multi-modal Attention Deformable U-shaped Network (MA-DUNet) to enhance retinal vessel segmentation performance. This model was designed to enhance segmentation accuracy through three key strategies. First, it incorporates atrous multi-scale (AMS) convolutions in the encoding stage to enhance the perception of information across different scales, thereby improving the extraction and representation of image features. Second, a gated channel transformation (GCT) attention mechanism is introduced between the encoder and decoder to improve feature transmission and extract crucial information. This mechanism dynamically adjusts the relationships among feature channels, emphasizing important task-related features while suppressing irrelevant ones. Third, a Multi-Modal Attention Fusion Block (MAFB), which combines the Multi-Modal Fusion Block (MFB) and GCT attention mechanism, is used during the decoding process to optimize information usage and enhance segmentation performance.</p><p><strong>Results: </strong>Images from three public datasets [i.e., digital retinal images for vessel extraction (DRIVE), structured analysis of the retina (STARE), and Child Heart and Health Study in England Database 1 (CHASE-DB1)] were analyzed. In terms of blood vessel segmentation, the model had accuracy values of 95.72%, 96.68%, and 96.38%, and area under the receiver operating characteristic (ROC) curve values of 98.10%, 98.89%, and 98.35% for the three datasets, respectively. Additionally, when validated using data from hospital collaborations, our model outperformed other comparative models in terms of the segmentation results.</p><p><strong>Conclusions: </strong>Our proposed MA-DUNet model provides more accurate segmentation of the fine terminal branches of vessels, effectively eliminating blurred boundaries and fragmentation in the segmentation outcomes, resulting in clearer vascular segmentation results.</p>","PeriodicalId":54267,"journal":{"name":"Quantitative Imaging in Medicine and Surgery","volume":"15 6","pages":"5258-5275"},"PeriodicalIF":2.9,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12209626/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144555702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnosis of thyroid nodules using ultrasound images based on deep learning features: online dynamic nomogram and gradient-weighted class activation mapping.","authors":"Wenwu Lu, Di Zhang, Wang Zhou, Wei Wei, Xin Wu, Wenbo Ding, Chaoxue Zhang","doi":"10.21037/qims-2025-159","DOIUrl":"10.21037/qims-2025-159","url":null,"abstract":"<p><strong>Background: </strong>The high incidence of thyroid nodules (TNs) necessitates accurate and effective differentiation between benign and malignant cases to avoid overtreatment, which is crucial for both patients and doctors. The aim of this study was to develop and visualize an integrated model to enhance the diagnostic capability of young radiologists in evaluating TNs.</p><p><strong>Methods: </strong>A retrospective collection of 1,501 ultrasound (US) images of TNs were randomly divided into training and validation sets. An independent test set comprised 541 patients from The First Affiliated Hospital of Anhui Medical University and the Affiliated Hospital of Integration Chinese and Western Medicine with Nanjing University of Traditional Chinese Medicine. We fine-tuned five ImageNet-pretrained deep learning (DL) models via transfer learning (TL) on US images to generate prediction scores and construct the final model. Gradient-weighted class activation mapping (Grad-CAM) was employed to highlight sensitive areas of the US images that contribute to nodule classification. Additionally, a comprehensive model was established utilizing both US image features and DL features, and an online dynamic nomogram was subsequently created for practical application. Models were compared and evaluated for discrimination, calibration, and effectiveness, to ascertain whether the DL model can improve radiologists' diagnosis of TNs.</p><p><strong>Results: </strong>The DL model demonstrated superior performance compared to the US model, with area under the receiver operating characteristic (ROC) curve (AUC) values of 0.875 and 0.787 on the test set, respectively. When combined into a comprehensive diagnostic model, a significant improvement was observed (test set AUC: 0.907). The Net Reclassification Index (NRI) results indicate that the heat maps and scores output by the DL model help to improve radiologists' classification accuracy in distinguishing between benign and malignant nodules.</p><p><strong>Conclusions: </strong>The integrated model based on US image features and DL features demonstrates good diagnostic performance for distinguishing between benign and malignant TNs.</p>","PeriodicalId":54267,"journal":{"name":"Quantitative Imaging in Medicine and Surgery","volume":"15 6","pages":"5689-5702"},"PeriodicalIF":2.9,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12209616/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144555704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnostic value of different contrast-enhanced ultrasound (CEUS) methods for sentinel lymph node metastasis in patients with breast neoplasms: a meta-analysis and indirect comparison.","authors":"Xiao-Wu Yuan, Cun-Li Cao, Wen-Xiao Li, Ji-Xue Hou, Si-Rui Wang, Li-Nan Shi, Pei-Shan Zhu, Jin-Li Wang, Ya-Qian Deng, Ze-Lin Xu, Jun Li","doi":"10.21037/qims-24-317","DOIUrl":"10.21037/qims-24-317","url":null,"abstract":"<p><strong>Background: </strong>Breast cancer is the most common malignant tumor in female patients. It is important to evaluate axillary lymph node metastasis before surgery to select the most appropriate treatment and evaluate the prognosis of breast cancer patients. In breast cancer, the sentinel lymph node (SLN) is the first lymph node to which tumor cells metastasize, and thus can indicate the status of the axillary lymph nodes. This meta-analysis sought to quantify the performance of contrast medium via intravenous injection or via four-point subcutaneous injection (i.e., into the skin around the areola at 3, 6, 9 and 12 o'clock) to evaluate which method had the best diagnostic performance in the diagnosis of SLN metastasis of breast cancer, provide a more accurate non-invasive assessment of axillary staging in clinical breast cancer patients, and improve patient outcomes.</p><p><strong>Methods: </strong>The PubMed, Web of Science, Embase, OVID, and Cochrane Library databases were used to evaluate the value of the two injection methods in diagnosing SLN metastasis in breast cancer patients. In total, 17 articles (with 19 datasets) met the inclusion criteria of the study and were included in the meta‑analysis. All the analyses were conducted using Stata 14.0 software. The summary statistics, including the sensitivity (SEN), specificity (SPE), positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic score, diagnostic odds ratio (DOR), and area under the receiver operating characteristic (ROC) curve, were calculated to assess the diagnostic value of the two methods. This study was registered with the International Prospective Register of Systematic Reviews (PROSPERO) (No. CRD42023475494).</p><p><strong>Results: </strong>The four-point subcutaneous injection method around the areola had a relative SEN of 1.26 [95% confidence interval (CI): 0.88-1.81], a relative SPE of 0.95 (95% CI: 0.88-1.00), a relative PLR of 0.76 (95% CI: 0.34-1.71), a relative NLR of 0.44 (95% CI: 0.17-1.16), a relative diagnostic score of 1.15 (95% CI: 0.84-1.58), and a relative DOR of 1.61 (95% CI: 0.44-5.90). The missed diagnosis rate of the four-point subcutaneous injection was 12%, and the area under the ROC curve was 0.94 (95% CI: 0.91-0.96); while the missed diagnosis rate of the intravenous injection was 42%, and the area under the ROC curve was 0.94 (95% CI: 0.92-0.96).</p><p><strong>Conclusions: </strong>Compared to the intravenous injection, the four-point subcutaneous injection around the areola improved the SEN of contrast-enhanced ultrasound in the diagnosis of breast cancer SLN metastasis and reduced the rate of missed diagnosis, was better able to diagnose SLN status, and provided more accurate axillary lymph node staging for breast cancer patients, and thus could help to improve patient prognosis.</p>","PeriodicalId":54267,"journal":{"name":"Quantitative Imaging in Medicine and Surgery","volume":"15 6","pages":"5660-5673"},"PeriodicalIF":2.9,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12209659/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144555742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A case presentation of pleomorphic carcinoma of the lung presenting as a small nodule with early cavitary change and rapid progression.","authors":"Jae Wook Lee, Jang Gyu Cha, Eun Suk Koh","doi":"10.21037/qims-2024-2475","DOIUrl":"10.21037/qims-2024-2475","url":null,"abstract":"","PeriodicalId":54267,"journal":{"name":"Quantitative Imaging in Medicine and Surgery","volume":"15 6","pages":"5921-5927"},"PeriodicalIF":2.9,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12209606/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144555753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MamTrans: magnetic resonance imaging segmentation algorithm for high-grade gliomas and brain meningiomas integrating attention mechanisms and state-space models.","authors":"Cheng Lv, Xu-Jun Shu, Jun Qiu, Zi-Cheng Xiong, Jing-Bo Ye, Shang-Bo Li, Sheng-Bo Chen, Hong Rao","doi":"10.21037/qims-24-2180","DOIUrl":"10.21037/qims-24-2180","url":null,"abstract":"<p><strong>Background: </strong>Meningiomas and gliomas represent the most common benign and malignant brain tumors, where accurate segmentation is essential for clinical assessment and surgical planning. Although magnetic resonance imaging (MRI) serves as a crucial diagnostic tool, precise segmentation remains challenging due to significant morphological and structural variations between tumor types and surrounding complex soft tissues. While Mamba models demonstrate excellence in sequence processing and attention mechanisms show promising performance, both face limitations in feature extraction and computational efficiency, respectively. To address these challenges, we propose the MamTrans algorithm, which integrates state-space models (SSMs) with attention mechanisms to significantly improve computational efficiency while maintaining segmentation accuracy.</p><p><strong>Methods: </strong>This study utilized 418 cases of axial T1-weighted contrast-enhanced MRI data of brain tumors, comprising 177 cases of high-grade gliomas and 241 cases of meningiomas. To validate the findings, five-fold cross-validation was employed.</p><p><strong>Results: </strong>The newly algorithm MamTrans achieved promising segmentation results in the high-grade glioma segmentation experiment, with an intersection over union (IoU) of 88.12, a Dice similarity coefficient (DSC) of 89.23, and a Hausdorff distance (HD) of 12.67. In the meningioma segmentation experiment, its segmentation metrics were IoU of 90.26, DSC of 91.27, and HD of 15.14, on the external dataset, the model obtained IoU of 90.34, DSC of 91.25, and HD of 14.17, outperforming other segmentation models such as U-Net, DeepLab, and Attention U-Net.</p><p><strong>Conclusions: </strong>The research results demonstrate that the proposed MamTrans algorithm outperforms various segmentation models in the segmentation tasks of gliomas and meningiomas. Innovatively, this single algorithm achieves high-precision segmentation for two tumor types with remarkably different morphologies, while significantly reducing model complexity and computational overhead, exhibiting substantial clinical application value.</p>","PeriodicalId":54267,"journal":{"name":"Quantitative Imaging in Medicine and Surgery","volume":"15 6","pages":"5796-5810"},"PeriodicalIF":2.9,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12209605/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144555769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reversibility of diffuse magnetic resonance imaging following endovascular therapy in acute ischemic stroke: a systematic review and meta-analysis.","authors":"Wen-Feng Cao, Er-Ling Leng, Jun-Ling Wang, Yong-Liang Zhou, Shi-Min Liu, Ling-Feng Wu, Zheng-Bing Xiang, Wei Rao, Chao-Qun Luo, Wang-Wang Hong, Quan-Hong Chu, An Wen","doi":"10.21037/qims-2024-2885","DOIUrl":"10.21037/qims-2024-2885","url":null,"abstract":"<p><strong>Background: </strong>Diffusion-weighted imaging (DWI) can facilitate early stroke diagnosis. Recently, breakthroughs have been made in early intravascular interventions for acute ischemic stroke (AIS). However, the reversibility of DWI after interventions remains unclear. In this meta-analysis, we investigated the relationship between early endovascular therapy (EVT) and DWI reversal (DWI-R), and clinical outcomes were collected.</p><p><strong>Methods: </strong>Online databases (PubMed, Embase, Web of Science, Medline, and Cochrane Library) were searched for studies enrolling patients who underwent magnetic resonance imaging (MRI)-DWI sequence examination and EVT within 24 h of stroke onset, and follow-up DWI or fluid-attenuated inversion recovery (FLAIR) within 7 days of EVT. The time characteristics of DWI-R, clinical manifestations, imaging data, and clinical outcomes-up were collected and extracted to systematically evaluate DWI-R. Review Manager was used to evaluate the quality of the included studies, and Stata was used to perform the statistical analysis.</p><p><strong>Results: </strong>Initially, 515 studies were retrieved, of which 5 studies enrolling 1,226 subjects (n=643, 52.4% male) met the inclusion criteria. The pooled prevalence of DWI-R after EVT was 0.23 [95% confidence interval (CI): 0.17-0.28]. Early DWI-R was often transient. The apparent diffusion coefficient (ADC) was validated as a useful tool for predicting lesion survival. Complete reperfusion and shorter time interval from imaging to final reperfusion were independent predictors of DWI-R. DWI-R after EVT was associated with good functional outcomes in patients with stroke.</p><p><strong>Conclusions: </strong>High-signal areas on DWI were not static after AIS. EVT can reduce DWI expansion, facilitating DWI-R, which is closely associated with early neurological improvement and 90-day clinical outcomes. However, the enrolled studies had small sample sizes and showed significant heterogeneity in DWI-R rate. Factors related to DWI-R have not been comprehensively evaluated, and large-scale prospective clinical studies are required to provide a reference for treatment decision-making.</p>","PeriodicalId":54267,"journal":{"name":"Quantitative Imaging in Medicine and Surgery","volume":"15 6","pages":"5703-5718"},"PeriodicalIF":2.9,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12209663/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144555830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of iterative metal artifact reduction techniques on diagnostic performance in patients with dental artifacts on carotid computed tomography angiography.","authors":"Jun Xu, Honghao Wang, Chenxi Wang, Ruixin Yan, Yupeng Zhu, Chao Zhuang, Zhihui Xu, Yan Zhang, Ning Lang","doi":"10.21037/qims-2024-2651","DOIUrl":"10.21037/qims-2024-2651","url":null,"abstract":"<p><strong>Background: </strong>Metal artifacts (MAs) induced by dental prostheses in carotid computed tomography angiography (CTA) significantly impair diagnostic accuracy. This study aimed to assess the efficacy of the iterative metal artifact reduction (iMAR) technique in mitigating these artifacts.</p><p><strong>Methods: </strong>Eighty-one patients with suspected vascular disorders and dental prostheses who underwent CTA imaging were retrospectively included. The CTA images were reconstructed with and without iMAR (iMAR-CTA and non-iMAR-CTA) for evaluation. Additionally, 81 matched patients without dental prostheses who underwent CTA imaging (standard CTA) served as a reference group for objective image quality assessment. Objective image quality involving signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and artifact index (AI) were analyzed. Subjective image quality was evaluated using a five-point Likert scale. Diagnostic performance was assessed by examining luminal stenosis, calcification, and aneurysm, with digital subtraction angiography (DSA) as the reference standard. Intramodality and inter-radiologist agreements were calculated using the intraclass correlation coefficient (ICC).</p><p><strong>Results: </strong>Image quality score was significantly higher for iMAR-CTA images than non-iMAR-CTA images [radiologist 1, 5 (5-5) <i>vs.</i> 3 (2-3); radiologist 2, 5 (4-5) <i>vs.</i> 3 (3-3); radiologist 3, 5 (5-5) <i>vs.</i> 2 (2-3), all P<0.001]. There was no significant difference in scores between iMAR-CTA and normal CTA. In the objective analysis, iMAR-CTA exhibited higher SNR and CNR and lower AI compared to non-iMAR-CTA (P<0.001). Furthermore, the objective image quality of iMAR-CTA was comparable to that of standard CTA, with no statistically significant differences in SNR (P=0.324) or CNR (P=0.109). For diagnostic performance evaluation, iMAR-CTA exhibited good to excellent agreement with DSA for luminal stenosis and aneurysm (ICC, 0.859-0.946), exceeding the moderate to good agreement of non-iMAR-CTA (ICC, 0.583-0.777). Regarding luminal stenosis severity, iMAR-CTA had higher accuracy rates (90.63-93.75%; 58/64-60/64) than non-iMAR-CTA (57.81-65.63%; 37/64-42/64). In aneurysm detection, iMAR-CTA achieved higher accuracy rates (77.78-88.89%; 7/9-8/9) than non-iMAR-CTA (44.44-66.67%; 4/9-6/9). For luminal stenosis severity and calcification, iMAR-CTA demonstrated excellent agreement (ICC, 0.908-0.910), whereas non-iMAR-CTA exhibited moderate agreement (ICC, 0.694-0.747).</p><p><strong>Conclusions: </strong>iMAR effectively reduces MAs, achieving image quality comparable to standard CTA without artifacts, facilitating a more reliable evaluation of carotid artery disorders in patients with dental prostheses.</p>","PeriodicalId":54267,"journal":{"name":"Quantitative Imaging in Medicine and Surgery","volume":"15 6","pages":"5635-5646"},"PeriodicalIF":2.9,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12209623/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144555748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Left pulmonary sequestration with portal venous drainage: a case description of surgical planning using IQQA-3D reconstruction.","authors":"Wenjie Yuan, Hao Chen, Chun Chen","doi":"10.21037/qims-2024-2694","DOIUrl":"10.21037/qims-2024-2694","url":null,"abstract":"","PeriodicalId":54267,"journal":{"name":"Quantitative Imaging in Medicine and Surgery","volume":"15 6","pages":"5893-5899"},"PeriodicalIF":2.9,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12209703/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144555767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}