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Impact of slice thickness on CACS calculation with virtual non-contrast in photon-counting CT. 层厚对光子计数CT虚无对比度下CACS计算的影响。
IF 3.2 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2026-01-20 DOI: 10.1186/s12880-026-02162-0
Qiuju Hu, Huixin Zhang, Bangjun Guo, Dongsheng Jin, Meirong Sun, Jiliang Chen, Song Luo, Yane Zhao, Guang-Ming Lu

Background: This study aims to investigate the feasibility of coronary artery calcium scoring (CACS) calculating from PureCalcium virtual non-iodine algorithm on photon-counting detector CT (PCD-CT) and the potential impact of different section thickness, level of virtual monoenergetic images (VMIs), and quantum iterative reconstruction (QIR) on the accuracy of CACS quantification.

Materials and methods: A total of 123 patients who underwent coronary CT angiography on PCD-CT with a separate true non-contrast CACS (CACSTNC) scan were prospectively included. Agatston scores were calculated from the PureCalcium algorithm (CACSPC) using a section thickness of 3 mm-1.5 mm, different VMI (55-75 kilo-electron volt (keV)) and QIR (strength 1,4) levels, respectively. CACSTNC at 70 keV and QIR 2 were used as reference standards. Differences in CACS of different reconstructions section thicknesses, various keV levels, and QIR strength were compared using the Wilcoxon rank sum test with Bonferroni correction. The intraclass correlation coefficients (ICCs) and Bland-Altman analysis were conducted to assessed the agreement. The agreement of plaque burden groups (based on CACS) at different reconstruction parameters was evaluated using weighted Cohen kappa.

Results: At all investigated section thickness, VMI, and QIR levels, the CACSPC were strongly correlated with CACSTNC (ICC: 0.94-0.98, P < 0.001 for all). There were no statistical differences in CACS between CACSPC at 3 mm section thickness, 60/65 keV (QIR1/4), and at 1.5 mm section thickness with 55 keV (QIR1/4), compared with CACSTNC. The smallest CACS bias was observed at a 1.5 mm section thickness, 55 keV, QIR 1, with mean bias of 2.4; LoA (IQR: -182.7, 187.4). CACSPC correctly identified 105 of 123 participants (85.4%) into the corresponding plaque burden group using CACSTNC as the referent standard (excellent agreement, κ = 0.904).

Conclusion: CACS derived from the PureCalcium algorithm with optimized reconstruction parameters shows excellent correlation with true non-contrast scans derived values. Thus, it is may possible to use the PureCalcium virtual non-iodine algorithm to replace the true non-contrast scans for CACS quantification, without additional radiation dose exposure.

背景:本研究旨在探讨PureCalcium虚拟无碘算法在光子计数检测器CT (PCD-CT)上计算冠状动脉钙评分(CACS)的可行性,以及不同切片厚度、虚拟单能图像(VMIs)水平和量子迭代重建(QIR)对CACS量化准确性的潜在影响。材料和方法:前瞻性纳入123例在PCD-CT上进行冠状动脉CT血管造影并单独进行真非对比CACS (CACSTNC)扫描的患者。采用purecalum算法(CACSPC)计算Agatston评分,切片厚度为3 mm-1.5 mm, VMI(55-75千电子伏(keV))和QIR(强度1,4)水平分别为不同。以70 keV的CACSTNC和QIR 2作为参考标准。采用Bonferroni校正的Wilcoxon秩和检验比较不同重建截面厚度、不同keV水平和QIR强度的CACS差异。采用类内相关系数(ICCs)和Bland-Altman分析来评估一致性。采用加权Cohen kappa法评价不同重建参数下斑块负担组(基于CACS)的一致性。结果:与CACSTNC相比,在所有被调查的切片厚度、VMI和QIR水平上,CACSPC与CACSTNC (ICC: 0.94-0.98, 3 mm切片厚度60/65 keV (QIR1/4)和1.5 mm切片厚度55 keV (QIR1/4)时的P PC密切相关。截面厚度为1.5 mm, 55 keV, QIR为1,平均偏差为2.4;LoA (IQR: -182.7, 187.4)。以CACSTNC为参照标准,CACSPC正确地将123名参与者中的105名(85.4%)识别到相应的斑块负担组(一致性极好,κ = 0.904)。结论:经优化重建参数的purecalum算法得到的CACS与非对比扫描真实值具有良好的相关性。因此,有可能使用purecalum虚拟无碘算法来代替真正的非对比扫描进行CACS定量,而无需额外的辐射剂量暴露。
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引用次数: 0
Ensemble learning strategy-based 18 F-FDG PET/CT metabolic habitats radiomics for predicting EGFR mutation and prognosis in LA-NSCLC: a multi-center study. 基于集成学习策略的18 F-FDG PET/CT代谢栖息地放射组学预测LA-NSCLC中EGFR突变和预后:一项多中心研究
IF 3.2 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2026-01-19 DOI: 10.1186/s12880-026-02163-z
Yu Ji, Jiaqi Wang, Yaru Wang, Juntao Zhang, Zhengjun Dai, Yong Cui, Jingsong Zheng, Dexin Yu
{"title":"Ensemble learning strategy-based 18 F-FDG PET/CT metabolic habitats radiomics for predicting EGFR mutation and prognosis in LA-NSCLC: a multi-center study.","authors":"Yu Ji, Jiaqi Wang, Yaru Wang, Juntao Zhang, Zhengjun Dai, Yong Cui, Jingsong Zheng, Dexin Yu","doi":"10.1186/s12880-026-02163-z","DOIUrl":"10.1186/s12880-026-02163-z","url":null,"abstract":"","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":"88"},"PeriodicalIF":3.2,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12895722/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146003185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Feasibility of neurite oriented diffusion and density imaging in thigh skeletal muscle of volunteers with knee pain: relationship with proton density fat fraction: a cross-sectional study. 膝关节疼痛志愿者大腿骨骼肌神经突定向扩散和密度成像的可行性:与质子密度脂肪分数的关系:一项横断面研究。
IF 3.2 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2026-01-17 DOI: 10.1186/s12880-025-02127-9
Yiou Wang, Ziru Qiu, Juan Liu, Yanjun Chen, Xinru Zhang, Ruoxing Liao, Dong Han, Xinyuan Zhang, Xiaodong Zhang

Objectives: To evaluate the microstructure changes in thigh skeletal muscles of volunteers with knee pain and to explore their relationship with the proton density fat fraction (PDFF), utilizing neurite oriented diffusion and density imaging (NODDI).

Materials and methods: In this prospective study, we collected NODDI and mDIXON-quant images from the bilateral thigh skeletal muscles of 66 asymptomatic and 24 knee pain volunteers. To optimally match the raw data based on these variables and create asymptomatic group (HC, n = 24) and knee pain group (KP, n = 24), the MatchIt package was utilized.

Statistical tests: For the comparison of normally distributed data between the HC and KP groups, t-tests were utilized, while the Wilcoxon rank-sum test was applied for non-normally distributed data. Pearson coefficient was used to analyze the correlation between microstructure parameters of thigh skeletal muscle and PDFF.

Results: Statistically significant differences were observed in MD value of the left hamstrings between the HC and KP groups (p = 0.030), as well as in the FA value of the right quadriceps femoris (p = 0.026). Among volunteers experiencing knee pain, the V-intra value of the right hamstrings and the FA value of the right quadriceps femoris demonstrated a moderate positive correlation with PDFF (r = 0.661, p < 0.001; r = 0.724, p < 0.001).

Conclusion: Microstructure differences in thigh skeletal myofibrils were detected in volunteers with knee pain compared to asymptomatic volunteers, and were more closely related to intramuscular fat infiltration.

目的:利用神经突定向扩散和密度成像(NODDI)技术评价膝关节疼痛患者大腿骨骼肌的微结构变化,探讨其与质子密度脂肪分数(PDFF)的关系。材料和方法:在这项前瞻性研究中,我们收集了66名无症状和24名膝关节疼痛志愿者的双侧大腿骨骼肌的NODDI和mdixon -定量图像。为了根据这些变量对原始数据进行最佳匹配,并创建无症状组(HC, n = 24)和膝关节疼痛组(KP, n = 24),使用MatchIt包。统计检验:HC组与KP组正态分布资料比较采用t检验,非正态分布资料比较采用Wilcoxon秩和检验。采用Pearson系数分析大腿骨骼肌微结构参数与PDFF的相关性。结果:HC组与KP组左腘绳肌MD值、右股四头肌FA值比较,差异均有统计学意义(p = 0.030)。在有膝关节疼痛的志愿者中,右腿筋的V-intra值和右股四头肌的FA值与PDFF呈中度正相关(r = 0.661, p < 0.001; r = 0.724, p < 0.001)。结论:有膝关节疼痛的志愿者与无症状的志愿者相比,大腿骨骼肌原纤维的微结构存在差异,且与肌内脂肪浸润的关系更为密切。
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引用次数: 0
COVID-19 infection during the Omicron wave changed carotid structure compared with uninfected controls: a longitudinal study. 与未感染的对照组相比,欧米克隆波期间COVID-19感染改变了颈动脉结构:一项纵向研究。
IF 3.2 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2026-01-17 DOI: 10.1186/s12880-026-02169-7
Lianbi Zhao, Yang Qu, Liang Zhang, Dan Xue, Jing Huang, Fenghui Ma, Bin Zhang, Lantian Wang, Yunyou Duan, Ke Dong, Lijun Yuan, Changyang Xing
{"title":"COVID-19 infection during the Omicron wave changed carotid structure compared with uninfected controls: a longitudinal study.","authors":"Lianbi Zhao, Yang Qu, Liang Zhang, Dan Xue, Jing Huang, Fenghui Ma, Bin Zhang, Lantian Wang, Yunyou Duan, Ke Dong, Lijun Yuan, Changyang Xing","doi":"10.1186/s12880-026-02169-7","DOIUrl":"10.1186/s12880-026-02169-7","url":null,"abstract":"","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":"85"},"PeriodicalIF":3.2,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12895908/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145994185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Investigate the quantification accuracy of small lesions in oncological 18F-FDG PET/CT using a deep progressive learning reconstruction method. 应用深度渐进式学习重建方法研究肿瘤18F-FDG PET/CT小病变的定量准确性。
IF 3.2 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2026-01-17 DOI: 10.1186/s12880-026-02166-w
Lei Xu, Rui Yang, Ru-Shuai Li, Ren-Cong Liu, Qing-le Meng, Feng Wang
{"title":"Investigate the quantification accuracy of small lesions in oncological <sup>18</sup>F-FDG PET/CT using a deep progressive learning reconstruction method.","authors":"Lei Xu, Rui Yang, Ru-Shuai Li, Ren-Cong Liu, Qing-le Meng, Feng Wang","doi":"10.1186/s12880-026-02166-w","DOIUrl":"10.1186/s12880-026-02166-w","url":null,"abstract":"","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":"84"},"PeriodicalIF":3.2,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12895979/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145994201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
MRI-Based peritumoral radiomics for predicting recurrence risk in ER+/HER2- breast cancer. 基于mri的肿瘤周围放射组学预测ER+/HER2-乳腺癌复发风险。
IF 3.2 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2026-01-17 DOI: 10.1186/s12880-026-02153-1
Yang Chen, Liang You, Yan Huang, Lizhi Xie, Qin Xiao, Tianwen Xie, Ling Zhang, Rong Li, Qifeng Wang, Yingshi Sun, Wei Tang, Yajia Gu, Weijun Peng
{"title":"MRI-Based peritumoral radiomics for predicting recurrence risk in ER+/HER2- breast cancer.","authors":"Yang Chen, Liang You, Yan Huang, Lizhi Xie, Qin Xiao, Tianwen Xie, Ling Zhang, Rong Li, Qifeng Wang, Yingshi Sun, Wei Tang, Yajia Gu, Weijun Peng","doi":"10.1186/s12880-026-02153-1","DOIUrl":"10.1186/s12880-026-02153-1","url":null,"abstract":"","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":"83"},"PeriodicalIF":3.2,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12896102/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145994175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Physiological distribution and dosimetry of Al18F-NOTA-Pentixafor in humans. al18f - nota - pentxafor在人体内的生理分布和剂量测定。
IF 3.2 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2026-01-17 DOI: 10.1186/s12880-026-02155-z
Xinyang Li, Xiao Jiang, Ying Kou, Yu He, Jingkai Yi, Dan Wang, Kailin Qi, Yingchun Li, Ping Wu, Yutang Yao, Hao Lu, Shirong Chen, Meng Zhao, Zhen Cao, Zhuzhong Cheng
{"title":"Physiological distribution and dosimetry of Al<sup>18</sup>F-NOTA-Pentixafor in humans.","authors":"Xinyang Li, Xiao Jiang, Ying Kou, Yu He, Jingkai Yi, Dan Wang, Kailin Qi, Yingchun Li, Ping Wu, Yutang Yao, Hao Lu, Shirong Chen, Meng Zhao, Zhen Cao, Zhuzhong Cheng","doi":"10.1186/s12880-026-02155-z","DOIUrl":"10.1186/s12880-026-02155-z","url":null,"abstract":"","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":"87"},"PeriodicalIF":3.2,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12895646/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145994248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Early prediction of pathologic complete response to neoadjuvant chemotherapy based on longitudinal total choline of MR spectroscopy in patients with breast cancer. 基于磁共振光谱纵向总胆碱对乳腺癌患者新辅助化疗病理完全反应的早期预测。
IF 3.2 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2026-01-16 DOI: 10.1186/s12880-026-02160-2
Shuling Liu, Xiaoxia Wang, Fujie Jiang, Sun Tang, Ying Cao, Lu Wang, Huifang Chen, Xiangfei Zeng, Yao Huang, Lan Li, Renzhi Zhang, Jiuquan Zhang
{"title":"Early prediction of pathologic complete response to neoadjuvant chemotherapy based on longitudinal total choline of MR spectroscopy in patients with breast cancer.","authors":"Shuling Liu, Xiaoxia Wang, Fujie Jiang, Sun Tang, Ying Cao, Lu Wang, Huifang Chen, Xiangfei Zeng, Yao Huang, Lan Li, Renzhi Zhang, Jiuquan Zhang","doi":"10.1186/s12880-026-02160-2","DOIUrl":"10.1186/s12880-026-02160-2","url":null,"abstract":"","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":"82"},"PeriodicalIF":3.2,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12896254/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145988107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Multiparametric MRI-based habitat analysis integrating deep learning and radiomics for predicting preoperative Ki-67 expression level in breast cancer. 结合深度学习和放射组学的多参数mri栖息地分析预测乳腺癌术前Ki-67表达水平。
IF 3.2 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2026-01-16 DOI: 10.1186/s12880-026-02151-3
Yuqian Wang, Yue Zhang, Zaiyi Liu, Yiming Xiong, Mifang Li, Lingyan Zhang, Zhenwei Shi
{"title":"Multiparametric MRI-based habitat analysis integrating deep learning and radiomics for predicting preoperative Ki-67 expression level in breast cancer.","authors":"Yuqian Wang, Yue Zhang, Zaiyi Liu, Yiming Xiong, Mifang Li, Lingyan Zhang, Zhenwei Shi","doi":"10.1186/s12880-026-02151-3","DOIUrl":"10.1186/s12880-026-02151-3","url":null,"abstract":"","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":"80"},"PeriodicalIF":3.2,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12892457/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145988045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Visceral adiposity as a key predictor of metabolic dysfunction-associated steatotic liver disease: an analytical cross-sectional study in a tertiary care hospital of Karachi, Pakistan. 内脏肥胖是代谢功能障碍相关脂肪变性肝病的关键预测因子:巴基斯坦卡拉奇一家三级保健医院的分析横断面研究。
IF 3.2 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2026-01-16 DOI: 10.1186/s12880-026-02159-9
Zainab Hussain, Abdur Rehman, Saira Samnani, Aysha Habib, Zafar Sajjad
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引用次数: 0
期刊
BMC Medical Imaging
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