Quantitative Imaging in Medicine and Surgery最新文献

{"title":"Enhancing pediatric abdominal pain diagnosis: the role of ultrasound layered scanning technique.","authors":"Feng-Yan Zhang, Fu-Jian Wang, Zhi-Fang Wang, Xiao-Qing Qi","doi":"10.21037/qims-24-1855","DOIUrl":"10.21037/qims-24-1855","url":null,"abstract":"<p><strong>Background: </strong>Pediatric abdominal pain is a common yet diagnostically challenging symptom, particularly in young children who struggle to articulate their discomfort. With obesity increasingly affecting ultrasound accuracy, this study aimed to find the cause of pediatric abdominal pain by seeking new approaches and methods in ultrasound examination, especially in the application among obese or overweight pediatric patients.</p><p><strong>Methods: </strong>A retrospective analysis was conducted on pediatric patients hospitalized between July 2016 and November 2017 for abdominal pain. Patients were categorized into normal weight, overweight, and obese groups. Conventional and layer-by-layer scanning methods were used by attending physicians to examine abdominal organs, including the liver, gallbladder, spleen, pancreas, kidneys, and bladder. An abdominal probe was employed for rapid screening, followed by a high-frequency probe for detailed three-layer scanning. Ultrasound images were analyzed alongside the children's symptoms and physical signs to provide diagnostic insights.</p><p><strong>Results: </strong>When comparing the conventional and stratified screening groups, several key differences were noted. The stratified group had higher detection rates for mesenteric lymphadenopathy (100% <i>vs.</i> 86%) and peritonitis (94% <i>vs.</i> 27%). Improved detection in the stratified group was due to the identification of peritoneal thickening, leading to higher detection rates for mesenteric fat inflammation (100% <i>vs.</i> 46%), appendicitis (94% <i>vs.</i> 63%), and urachal inflammation (100% <i>vs.</i> 0%). Detection rates for substantial lesions, such as gallstones and ovarian torsion, were similar in both groups (100%). The stratified group also showed significantly better detection of gastrointestinal conditions like gastroenteritis (97% <i>vs.</i> 32%), inguinal hernia (100% <i>vs.</i> 0%), and intestinal ascariasis (100% <i>vs.</i> 47%). Differences in detection rates were observed when abdominal fat layer thickness was between 0.8 and 1.7 cm, with more significant differences when thickness exceeded 1.7 cm.</p><p><strong>Conclusions: </strong>Real-time ultrasound with stratified screening effectively detects abdominal and pelvic masses, solid organ lesions, and bowel wall thickening, improving disease detection in children, including individuals with increased body mass index. This method is valuable and recommended for wider use.</p>","PeriodicalId":54267,"journal":{"name":"Quantitative Imaging in Medicine and Surgery","volume":"15 5","pages":"4039-4046"},"PeriodicalIF":2.9,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12084742/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144095454","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":"FCFDiff-Net: full-conditional feature diffusion embedded network for 3D brain tumor segmentation.","authors":"Xiaosheng Wu, Qingyi Hou, Zhaozhao Xu, Chaosheng Tang, Shuihua Wang, Junding Sun, Yudong Zhang","doi":"10.21037/qims-24-2300","DOIUrl":"10.21037/qims-24-2300","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Brain tumor segmentation (BraTS) plays a critical role in medical imaging for early diagnosis and treatment planning. Recently, diffusion models have provided new insights into image segmentation, achieving significant success due to their ability to model nonlinearities. However, existing methods still face challenges, such as false negatives and false positives, caused by image blurring and noise interference, which remain major obstacles. This study aimed to develop a novel neural network approach to address these challenges in three-dimensional (3D) BraTS.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We propose a novel full-conditional feature diffusion embedded network (FCFDiff-Net) for 3D BraTS. This model enhances segmentation accuracy and robustness, particularly in noisy or ambiguous regions. This model introduces the full-conditional feature embedding (FCFE) module and employs a more comprehensive conditional embedding approach, fully integrating feature information from the original image into the diffusion model. It establishes an effective connection between the decoder side of the denoising network and the encoder side of the diffusion model, thereby improving the model's ability to capture the tumor target region and its boundaries. To further optimize performance and minimize discrepancies between conditional features and the denoising module, we introduce the multi-head attention residual fusion (MHARF) module. The MHARF module integrates features from the FCFE with noisy features generated during the denoising process. Using multi-head attention aligns semantic and noise information refining the segmentation results. This fusion enhances segmentation accuracy and stability by reducing noise impact and ensuring greater consistency across tumor regions.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The BraTS 2020 and BraTS 2021 datasets served as the primary training and evaluation datasets. The proposed architecture was assessed using metrics such as Dice similarity coefficient (DSC), Hausdorff distance at the 95th percentile (HD95), specificity, and false positive rate (FPR). For the BraTS 2020 dataset, the DSC scores for the whole tumor (WT), tumor core (TC), and enhancing tumor (ET) were 0.916, 0.860, and 0.786, respectively. The HD95 values were 1.917, 2.571, and 2.581 mm, whereas specificity values were 0.998, 0.999, and 0.999, and FPR values were 0.002, 0.001, and 0.001, respectively. On the BraTS 2021 dataset, the DSC scores for the same regions were 0.926, 0.903, and 0.869, with HD95 values of 2.156, 1.834, and 1.583 mm, respectively. Specificity and FPR values were 0.999 across the board, and FPR values were consistently low at 0.001. These results demonstrate the model's excellent performance across the three regions.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The proposed FCFDiff-Net provides an efficient and robust solution for 3D BraTS, outperforming existing models in terms of accuracy and robustness. Future work will","PeriodicalId":54267,"journal":{"name":"Quantitative Imaging in Medicine and Surgery","volume":"15 5","pages":"4217-4234"},"PeriodicalIF":2.9,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12084721/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144095611","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":"Global trends and developments in pulmonary magnetic resonance imaging research: a bibliometric analysis of the past decade.","authors":"Ting Wu, Linyu Wu, Yufan Chen, Jun Wu, Chen Gao, Maosheng Xu","doi":"10.21037/qims-24-2205","DOIUrl":"10.21037/qims-24-2205","url":null,"abstract":"<p><strong>Background: </strong>Pulmonary magnetic resonance imaging (MRI) has the advantage of nonionizing radiation and multiparameter imaging of structure and function, facilitating its clinical use in a variety of pulmonary diseases. This study aimed to identify the research trends and emerging topics in pulmonary MRI by conducting a comprehensive bibliometric analysis of the field over the past decade.</p><p><strong>Methods: </strong>A search of the Web of Science Core Collection database was conducted with the words \"lung\" and \"MRI\" for literature published from 2014 to 2023. The data were further analyzed with R and CiteSpace software in terms of annual publications and citations, collaborative networks (countries, institutions, and authors), source's local impact, keyword clustering, and burst analysis.</p><p><strong>Results: </strong>A total of 1,839 publications related to pulmonary MRI have been published over the last decade, with a relatively slow growth trend. The top three journals in terms of total publications and citations were <i>Magnetic Resonance in Medicine</i>, <i>Journal of Magnetic Resonance Imaging</i>, and <i>Radiology</i>. The most productive country was the United States, and the countries with the strongest collaborative links were the United States and the United Kingdom. The most productive institutions and authors were Ruprecht Karls University Heidelberg (articles, n=309) and Wild JM (articles, n=86), respectively. Keyword cluster analysis identified five clusters: \"lung cancer\", \"magnetic resonance imaging\", \"lung MRI\", \"cystic fibrosis\", and \"congenital diaphragmatic hernia\". Keyword burst analysis showed that the keywords with the highest burst intensity in the first 5 years and the last 5 years were \"mice\" and \"standardization\", respectively.</p><p><strong>Conclusions: </strong>Over the past decade, research trends in pulmonary MRI have focused on lung cancer and cystic fibrosis as the dominant clinical diseases. Research has been centered on standardizing pulmonary MRI to promote its clinical application.</p>","PeriodicalId":54267,"journal":{"name":"Quantitative Imaging in Medicine and Surgery","volume":"15 5","pages":"4431-4444"},"PeriodicalIF":2.9,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12082580/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144095613","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":"Multidisciplinary treatment of internal iliac pseudoaneurysm-sigmoid fistula: a case description.","authors":"Peng Wang, Xiaojun Mo, Chao Wang, Xin Wang, Hailong Yun, Yao Pan, Feizhou Du","doi":"10.21037/qims-24-1701","DOIUrl":"10.21037/qims-24-1701","url":null,"abstract":"","PeriodicalId":54267,"journal":{"name":"Quantitative Imaging in Medicine and Surgery","volume":"15 5","pages":"4888-4893"},"PeriodicalIF":2.9,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12082601/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144095652","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":"Performance of S-MAR in metal artifact reduction for intracranial aneurysm patients after endovascular embolization: a cross-sectional spectral computed tomography study.","authors":"Hanpei Zheng, Xinli Zhang, Shen Gui, Ming Yang, Jing Wang","doi":"10.21037/qims-2024-2640","DOIUrl":"10.21037/qims-2024-2640","url":null,"abstract":"<p><strong>Background: </strong>Metal artifacts caused by endovascular coils in intracranial aneurysms pose challenges in the accurate assessment of adjacent vessels. This study evaluated the performance of S-MAR, a novel post-processing technology that combines the advantages of three traditional techniques; that is, virtual monochromatic imaging (VMI) of spectral computed tomography (CT), metal artifact reduction (MAR), and subtraction algorithm. This study aimed to investigate its performance under different conditions and validate its accuracy.</p><p><strong>Methods: </strong>In total, 94 patients who had undergone cerebral computed tomography angiography (CTA) with dual-layer detector spectral CT were enrolled in this retrospective study. A total of 106 coils were found, and 67 patients had accompanying stents. Fifty patients underwent both digital subtraction angiography (DSA) and CTA within 6 months. Conventional CTA images, VMI [range, 40-110 kilo electron volt (keV)], and virtual non-enhanced images were generated and then post-processed to S-MAR, using the MAR technique and a subtraction algorithm. The contrast-to-noise ratio (CNR) and background noise were calculated. The maximum diameter, minimum diameter, and mean diameter of the adjacent vessels were measured. The coil artifact (CA) score was qualitatively assessed by two radiologists independently.</p><p><strong>Results: </strong>Compared to conventional images, S-MAR (40-70 keV) had significantly reduced metal artifacts, improved CNR, and lower CA scores (P<0.001). In S-MAR, a range of 60-70 keV is more suitable than 40-50 keV for coils with a diameter >8 mm. S-MAR also provides more accurate luminal quantitative measurements (maximum, minimum and mean diameter) and shows good consistency with DSA [intraclass correlation coefficients: 0.845 (0.783, 0.884), 0.947(0.876, 0.954), and 0.956 (0.875, 0.962)].</p><p><strong>Conclusions: </strong>S-MAR enhances vessel visualization and measurement accuracy. Our findings support its use in clinical practice for evaluating intracranial aneurysms post-embolization.</p>","PeriodicalId":54267,"journal":{"name":"Quantitative Imaging in Medicine and Surgery","volume":"15 5","pages":"4235-4246"},"PeriodicalIF":2.9,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12084747/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144095675","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":"Brain white matter microstructural alterations in patients with diabetic retinopathy: an automated fiber-tract quantification study.","authors":"Tian-Ye Xu, Yan-Hong Feng, Zhong-Ru Sun, Liang He, Jin-Hua Chen, Wei-Zhong Tian, Hong-Xia Zhang, Meng Zhu, Jian-Guo Xia","doi":"10.21037/qims-24-1440","DOIUrl":"10.21037/qims-24-1440","url":null,"abstract":"<p><strong>Background: </strong>Cognitive decline may occur in patients with diabetic retinopathy (DR), yet the mechanism underlying the relationship between cognitive decline and DR remains unclear. This study applied an automated fiber-tract quantification (AFQ) technique based on diffusion tensor imaging (DTI) to identify alterations in specific segments of brain white matter fiber tracts in patients with DR, and analyze their correlation with cognitive test scores and clinical biochemical indicators.</p><p><strong>Methods: </strong>A total of 19 patients with DR and 20 age-, sex-, and education-matched healthy controls (HCs) were included. Clinical and imaging data were prospectively collected. The AFQ technique was applied to track the whole brain white matter fiber tracts of each participant, and each fiber tract was segmented into 100 equidistant nodes. The fractional anisotropy (FA), mean diffusion (MD), axial diffusion (AD), and radial diffusion in 100 nodes of each fiber tract were calculated and compared between the two groups. Partial correlation analysis was performed to analyze the correlation between altered DTI metrics in segments of the fiber tracts and cognitive test scores, as well as clinical biochemical indicators in patients with DR.</p><p><strong>Results: </strong>Compared with the HC group, the DR group showed significantly reduced FA values in nodes 81-100, increased MD values in nodes 39-50, and reduced AD values in nodes 91-100 of the left cingulum cingulate (CGC) [P<0.05, false discovery rate (FDR) corrected], they also showed increased AD values in the left superior longitudinal fasciculus (SLF; nodes 1-23, 37-50, and 66-99), and the right SLF (nodes 1-36 and 79-100) (P<0.05, FDR corrected). Correlation analysis revealed a positive correlation between the FA values in nodes 82-98 of the left CGC and Montreal Cognitive Assessment scores (MoCA scores, r=0.760, P<0.05/P=0.021), and a positive correlation between the AD values in nodes 37-41 in the left SLF and glycated hemoglobin A1c (HbA1c) levels (r=0.559, P<0.05/P=0.039).</p><p><strong>Conclusions: </strong>Our findings demonstrated alterations in the white matter fiber tracts at the point-wise level in patients with DR using AFQ analysis. These alterations may be associated with cognitive impairment in DR. The AFQ technique can accurately detect the damage to the integrity of the brain white matter fiber tracts in patients with DR, and have high clinical application value in the diagnosis and evaluation of DR, which can deepen our understanding of brain white matter microstructural abnormalities in patients with DR.</p>","PeriodicalId":54267,"journal":{"name":"Quantitative Imaging in Medicine and Surgery","volume":"15 5","pages":"3982-3992"},"PeriodicalIF":2.9,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12082598/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144095684","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":"Conventional ultrasound and high-frame-rate contrast-enhanced ultrasound characteristics of ovarian thecoma-fibroma groups.","authors":"Xiaochun Li, Huanchong Lu, Ruhui Zeng, Youping Wang, Shigao Chen, Shaoqi Chen","doi":"10.21037/qims-24-2200","DOIUrl":"10.21037/qims-24-2200","url":null,"abstract":"<p><strong>Background: </strong>Ovarian thecoma-fibroma groups (OTFGs) are uncommon sex cord-stromal neoplasms. Ultrasound (US) is one of the imaging modalities frequently used to evaluate pelvic masses. It has the advantages of safety, convenience, and noninvasive diagnosis. However, there are still shortcomings in accurately diagnosing OTFGs. The present study aimed to describe the conventional ultrasound and high-frame-rate contrast-enhanced ultrasound (HiFR-CEUS) characteristics of OTFGs and to compare the diagnostic efficacy of these 2 methods and their combination to improve the diagnosis of OTFGs.</p><p><strong>Methods: </strong>The study included 68 patients diagnosed with ovarian tumors with complete US images from January 2021 to December 2023. Based on pathology results, there were 35 OTFGs and 33 non-OTFGs. All patients underwent preoperative conventional US and HiFR-CEUS. A 3×2 Chi-squared test and paired Chi-squared test were used to compare the diagnostic concordance of the 3 methods to diagnose OTFGs. Sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) of these 3 methods were calculated to determine their efficacy for diagnosing OTFGs.</p><p><strong>Results: </strong>Among the 35 OTFGs, 77.1% tumors (27/35) showed hypoechogenicity in solid parts, with granular or linear hyperechogenicity. All tumors with recorded color Doppler signals (27/27) had no-to-sparse vascularization (color score 1-2). HiFR-CEUS showed typical linear perfusion in the OTFG tumors; 94.3% tumors (33/35) showed hypoenhancement at the peak intensity, as compared to the surrounding myometrium. Seven patients (7/35, 20.0%) had cystic lesions with no internal enhancement. The combination of conventional US and HiFR-CEUS showed the highest diagnostic efficacy for diagnosing OTFGs [sensitivity: 97%, specificity: 100%, accuracy: 99%, PPV: 100%, NPV: 97%, area under the curve (AUC): 0.99] as compared to conventional US (sensitivity: 23%, specificity: 100%, accuracy: 60%, PPV: 100%, NPV: 55%, AUC: 0.61) and HiFR-CEUS (sensitivity: 94%, specificity: 97%, accuracy: 96%, PPV: 97%, NPV: 94%, AUC: 0.96).</p><p><strong>Conclusions: </strong>Most of the OTFGs showed characteristic linear perfusion in HiFR-CEUS. The combination of conventional US and HiFR-CEUS greatly improved the diagnosis rate of OTFGs. In summary, the combination of conventional US and HiFR-CEUS has significant value in the accurate diagnosis of OTFGs.</p>","PeriodicalId":54267,"journal":{"name":"Quantitative Imaging in Medicine and Surgery","volume":"15 5","pages":"3875-3890"},"PeriodicalIF":2.9,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12084734/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144095820","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 deep-learning model for predicting post-stroke cognitive impairment based on brain network damage.","authors":"Chen Bai, Yilin Leng, Haixing Xiao, Lei Li, Wenju Cui, Tan Li, Yuefang Dong, Jian Zheng, Xiuying Cai","doi":"10.21037/qims-24-2010","DOIUrl":"10.21037/qims-24-2010","url":null,"abstract":"<p><strong>Background: </strong>Post-stroke cognitive impairment (PSCI) is a common and severe complication following acute lacunar stroke (ALS). Due to the limitations of current assessment tools and imaging methods, the early diagnosis of PSCI within 3 months of ALS remaining challenging. This study aimed to develop an effective, reliable, and accurate deep-learning method to predict PSCI within 3 months of ALS.</p><p><strong>Methods: </strong>In total, 100 ALS patients were enrolled in the study, of whom 39 were diagnosed with PSCI and 61 were non-PSCI. First, we quantified three-dimensional (3D) gray-matter damage and white-matter tract disconnection, providing both regional damage (RD) and structural disconnection (SDC) higher-dimensional insights into brain network disruption. Second, we developed a novel deep-learning model based on ResNet18, integrating 3D RD, SDC, and diffusion-weighted imaging (DWI) to provide a comprehensive analysis of brain network integrity and predict PSCI. Finally, we compared the performance of our method with other methods, and visualized brain network damage associated with PSCI.</p><p><strong>Results: </strong>Our model showed strong predictive performance and had a mean accuracy (ACC) of 0.820±0.024, an area under the curve (AUC) of 0.795±0.068, a sensitivity (SEN) of 0.746±0.121, a specificity (SPE) of 0.869±0.044, and a F1-score (F1) of 0.760±0.050 in the five-fold cross-validation, outperforming existing models. In the PSCI patients, brain network damage significantly affected the salience, default mode, and somatic motor networks.</p><p><strong>Conclusions: </strong>This study not only established a model that can accurately predict PSCI, it also identified potential targets for symptom-based treatments, offering new insights into PSCI.</p>","PeriodicalId":54267,"journal":{"name":"Quantitative Imaging in Medicine and Surgery","volume":"15 5","pages":"3964-3981"},"PeriodicalIF":2.9,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12084723/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144095833","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":"Correlation analysis of organ doses with dose metrics for patients undergoing organ dose-modulated head CT examinations.","authors":"Mengting Wang, Tian Qin, Yihan Fan, Zongyu Xie, Baohui Liang","doi":"10.21037/qims-24-2061","DOIUrl":"10.21037/qims-24-2061","url":null,"abstract":"<p><strong>Background: </strong>The rapid advancement of computed tomography (CT) has greatly improved clinical diagnosis but has also introduced new challenges in radiation protection. This study aimed to evaluate the relationship between organ doses from Monte Carlo (MC) simulations and CT dose metrics for head CT exams with organ dose modulation (ODM), and to develop a simplified method for estimating individual organ doses.</p><p><strong>Methods: </strong>A CT source model including the X-ray energy spectrum, bowtie filter, fan beam shape, and rotational motion of the tube was constructed and validated. The modeling was divided into two different exposure regions based on the ODM technical principles: the 100° range on the anterior side of the skull (tube current reduction region) and the remaining 260° (tube current constant region). The source model was validated by comparing the error between the MC-simulated weighted CT dose index (CTDI_w) and the measured CTDI_w. A total of 40 patients were retrospectively collected, and each patient's voxelized head models were constructed and used for MC simulation to calculate organ doses. The global volume CTDI (CTDI_vol), regional CTDI_vol, size-specific dose estimate (SSDE), and organ-specific SSDE were derived based on the exposure (mAs) and water-equivalent diameters of each slice image. Linear regression fitting was used to explore the correlation between organ doses (including the brain, the eyeballs, the eye lens, and the salivary glands) and the four CT dose metrics mentioned above.</p><p><strong>Results: </strong>Comparison results for CTDI_w showed that the simulated source model error was within 5%, and the ODM model's error was below 0.05%. Organ doses correlated strongly with organ-specific SSDE (The R² between each organ dose and corresponding organ-specific SSDE were 0.92 for the brain, 0.91 for eyeballs, 0.90 for the eye lens, and 0.90 for the salivary gland). Estimation coefficients for estimating organ doses of the brain, eyeballs, eye lens, and salivary glands from organ-specific SSDE were 0.34, 0.59, 0.48, and 0.26, respectively, as a mean across all patients.</p><p><strong>Conclusions: </strong>There is a strong correlation between organ dose and organ-specific SSDE in ODM head CT examinations. However, activating the ODM results in significant differences in estimation coefficients for head CT exams with a fixed tube current, which provides a practical way to determine organ doses for individual patients undergoing head CT scans.</p>","PeriodicalId":54267,"journal":{"name":"Quantitative Imaging in Medicine and Surgery","volume":"15 5","pages":"3849-3860"},"PeriodicalIF":2.9,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12082609/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144095835","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":"Value of multi-modal ultrasound in evaluation of the accessory renal artery.","authors":"Ruijuan Liu, Youjing Sun, Yiyang Wang, Sijie Zhang, Junhong Ren","doi":"10.21037/qims-24-1117","DOIUrl":"https://doi.org/10.21037/qims-24-1117","url":null,"abstract":"<p><strong>Background: </strong>The evaluation of the accessory renal artery (ARA) holds clinical significance in the effective intervention of resistant hypertension and renal vascular-related surgical procedures. Multi-modal ultrasound is a non-invasive, secure, and real-time imaging modality, especially useful in patients with renal impairment. Nevertheless, few studies have focused on the value of multi-modal ultrasound in the assessment of the ARA. This study aimed to explore the diagnostic performances of multi-modal ultrasound in the assessment of the ARA.</p><p><strong>Methods: </strong>A retrospective data collection (clinical and imaging information) was conducted on patients who underwent renal artery conventional ultrasound and contrast-enhanced ultrasound (CEUS) examinations between August 2019 and November 2023 in Beijing Hospital. A total of 73 patients with a unilateral or bilateral ARA based on their computed tomography angiography (CTA) results were included. Compared with CTA results, the accuracy of multi-modal ultrasound for the assessment of the ARA was evaluated, and underlying reasons for misdiagnosis and missed diagnosis were analyzed.</p><p><strong>Results: </strong>Among the 73 patients (144 kidneys), CTA identified 85 ARAs, whereas multi-modal ultrasound detected 70 ARAs. Although multi-modal ultrasound failed to detect 15 ARAs, it did not result in any false-positive diagnoses. When CTA did not detect any ARAs in a kidney, multi-modal ultrasound also did not find any ARA. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of multi-modal ultrasound in diagnosing ARA were calculated as 82.4%, 100%, 100%, 81.0%, and 90.9%, respectively. The receiver operating characteristic (ROC) analysis demonstrated an area under the curve (AUC) of 0.906 (P<0.001). The consistency analysis yielded a kappa value of 0.806 (P<0.01). Comparisons were conducted between patients with detected ARAs and those with missed ARAs. The age and body mass index (BMI) between the two groups were found to be statistically significant (P<0.05).</p><p><strong>Conclusions: </strong>Multi-modal ultrasound, characterized by its non-invasive, safe, and reproducible nature, demonstrates a high level of diagnostic accuracy in detecting the ARA. Thus, multi-modal ultrasound holds promise as a valuable tool for evaluating the ARA.</p>","PeriodicalId":54267,"journal":{"name":"Quantitative Imaging in Medicine and Surgery","volume":"15 4","pages":"3575-3584"},"PeriodicalIF":2.9,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11994574/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144026772","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}