In this paper, methods are proposed and validated to determine low and high thresholds to segment out gray matter and white matter for MR images of different pulse sequences of human brain. First, a two-dimensional re...In this paper, methods are proposed and validated to determine low and high thresholds to segment out gray matter and white matter for MR images of different pulse sequences of human brain. First, a two-dimensional reference image is determined to represent the intensity characteristics of the original three-dimensional data. Then a region of interest of the reference image is determined where brain tissues are present. The non-supervised fuzzy c-means clustering is employed to determine: the threshold for obtaining head mask, the low threshold for T2-weighted and PD-weighted images, and the high threshold for T1-weighted, SPGR and FLAIR images. Supervised range-constrained thresholding is employed to determine the low threshold for T1-weighted, SPGR and FLAIR images. Thresholding based on pairs of boundary pixels is proposed to determine the high threshold for T2-and PD-weighted images. Quantification against public data sets with various noise and inhomogeneity levels shows that the proposed methods can yield segmentation robust to noise and intensity inhomogeneity. Qualitatively the proposed methods work well with real clinical data.展开更多
Objective To qualitatively assess the diagnostic performance of dynamic contrast enhancement(DCE),diffusionweighted imaging(DWI),and T2-weighted imaging(T2WI),alone or in combination,in the evaluation of breast cancer...Objective To qualitatively assess the diagnostic performance of dynamic contrast enhancement(DCE),diffusionweighted imaging(DWI),and T2-weighted imaging(T2WI),alone or in combination,in the evaluation of breast cancer.Methods We retrospectively reviewed the records of 394 consecutive patients with pathologically confirmed breast lesions who had undergone 3-T magnetic resonance imaging(MRI).The morphological characteristics of breast lesions were evaluated using DCE,DWI,and T2WI based on BI-RADS lexicon descriptors by trained radiologists.Patients were categorized into mass and non-mass groups based on MRI characteristics of the lesions,and the differences between benign and malignant lesions in each group were compared.Clinical prediction models for breast cancer diagnosis were constructed using logistic regression analysis.Diagnostic efficacies were compared using the area under the receiver operating characteristic curve(AUC)and DeLong test.Results For mass-like lesions,all the morphological parameters significantly differentiated benign and malignant lesions on consensus DCE,DWI,and T2WI(P<0.05).The combined method(DCE+DWI+T2WI)had a higher AUC(0.865)than any of the individual modality(DCE:0.786;DWI:0.793;T2WI:0.809)(P<0.05).For non-mass-like lesions,DWI signal intensity was a significant predictor of malignancy(P=0.036),but the model using DWI alone had a low AUC(0.669).Conclusion Morphological assessment using the combination of DCE,DWI,and T2WI provides better diagnostic value in differentiating benign and malignant breast mass-like lesions than assessment with only one of the modalities.展开更多
目的观察基于相位对比(PC)MRI颅内血流动力学参数预测急性高原反应(AMS)的价值。方法前瞻性招募72名健康青年志愿者,于平原地区采集平静呼吸及轻、中及重度瓦尔萨尔瓦动作(VM)下的颈内动脉(ICA)及颈内静脉(IJV)PC MRI并记录ICA及IJV血...目的观察基于相位对比(PC)MRI颅内血流动力学参数预测急性高原反应(AMS)的价值。方法前瞻性招募72名健康青年志愿者,于平原地区采集平静呼吸及轻、中及重度瓦尔萨尔瓦动作(VM)下的颈内动脉(ICA)及颈内静脉(IJV)PC MRI并记录ICA及IJV血流动力学参数;根据急进海拔4411 m的高原地区10 h后路易斯湖评分(LLS)结果划分AMS组(n=9)与无AMS组(n=63);采用单因素及多因素logistic回归分析筛选各状态下AMS的独立预测因素,构建单一及联合VM状态预测模型;绘制受试者工作特征曲线,计算曲线下面积(AUC),评估各模型预测效能。结果轻度VM下ICA搏动指数(PI ICA)、中度VM下IJV面积(S IJV)及重度VM下IJV阻力指数(RI IJV)均为AMS独立预测因素(P均<0.05)。联合VM状态模型(AUC=0.869)预测AMS的效能高于单一VM状态模型(AUC=0.698~0.738)。结论基于轻度VM PI ICA、中度VM S IJV及重度VM RI IJV构建的模型可有效预测AMS。展开更多
目的探讨动态对比增强磁共振成像(dynamic contrast-enhanced magnetic resonance imaging,DCE-MRI)参数对喉癌术后放疗短期预后的评估价值。方法本研究采用病例-对照研究设计方案,选取浙江省湖州市中心医院放射科2021年1月至2023年12...目的探讨动态对比增强磁共振成像(dynamic contrast-enhanced magnetic resonance imaging,DCE-MRI)参数对喉癌术后放疗短期预后的评估价值。方法本研究采用病例-对照研究设计方案,选取浙江省湖州市中心医院放射科2021年1月至2023年12月收治的127例喉癌患者为研究对象,患者接受不同肿瘤处理方式后进行根治性放疗。根据患者术后放疗肿瘤是否复发分为复发组(50例)和未复发组(77例),比较2组患者DCE-MRI参数[容量转移常数(volume transfer constant,Ktrans)、速率常数(rateconstant,Kep)、细胞外血管外间隙容积比(volumefractionofextracellular extravascular space,Ve)]及临床资料,采用多因素Logistic回归模型分析喉癌患者术后放疗短期预后的影响因素。采用受试者工作特征(receiver operating characteristic,ROC)及其曲线下面积(area under the curve,AUC)评估DCE-MRI参数对喉癌患者术后放疗短期预后的预测效能。结果复发组患者手术切缘阳性、术前病灶最大径≥4 cm、溃疡型病灶、N分期为N1~N3期、分期为晚期(Ⅲ、Ⅳ)占比均高于未复发组(P<0.05);复发组的Ktrans、Kep高于未复发组,Ve低于未复发组(P<0.05);多因素Logistic回归分析显示,手术切缘为阳性、N分期为N1~N3期、Ktrans、Kep是喉癌术后放疗短期预后的独立危险因素,Ve是保护因素(P<0.05);ROC结果显示,Ktrans、Kep、Ve三参数联合应用和五指标联合应用的Logistic回归模型诊断喉癌术后放疗短期预后的AUC(95%CI)分别为0.920(0.858~0.961)、0.923(0.862~0.963),三参数联合应用较单独应用的AUC明显提高(P<0.05),与五指标联合应用的AUC比较无差异。结论DCE-MRI参数Ktrans、Kep、Ve与喉癌术后放疗短期预后密切相关,Ktrans、Kep、Ve联合对喉癌患者术后放疗短期预后具有较好的预测效能。展开更多
文摘In this paper, methods are proposed and validated to determine low and high thresholds to segment out gray matter and white matter for MR images of different pulse sequences of human brain. First, a two-dimensional reference image is determined to represent the intensity characteristics of the original three-dimensional data. Then a region of interest of the reference image is determined where brain tissues are present. The non-supervised fuzzy c-means clustering is employed to determine: the threshold for obtaining head mask, the low threshold for T2-weighted and PD-weighted images, and the high threshold for T1-weighted, SPGR and FLAIR images. Supervised range-constrained thresholding is employed to determine the low threshold for T1-weighted, SPGR and FLAIR images. Thresholding based on pairs of boundary pixels is proposed to determine the high threshold for T2-and PD-weighted images. Quantification against public data sets with various noise and inhomogeneity levels shows that the proposed methods can yield segmentation robust to noise and intensity inhomogeneity. Qualitatively the proposed methods work well with real clinical data.
文摘Objective To qualitatively assess the diagnostic performance of dynamic contrast enhancement(DCE),diffusionweighted imaging(DWI),and T2-weighted imaging(T2WI),alone or in combination,in the evaluation of breast cancer.Methods We retrospectively reviewed the records of 394 consecutive patients with pathologically confirmed breast lesions who had undergone 3-T magnetic resonance imaging(MRI).The morphological characteristics of breast lesions were evaluated using DCE,DWI,and T2WI based on BI-RADS lexicon descriptors by trained radiologists.Patients were categorized into mass and non-mass groups based on MRI characteristics of the lesions,and the differences between benign and malignant lesions in each group were compared.Clinical prediction models for breast cancer diagnosis were constructed using logistic regression analysis.Diagnostic efficacies were compared using the area under the receiver operating characteristic curve(AUC)and DeLong test.Results For mass-like lesions,all the morphological parameters significantly differentiated benign and malignant lesions on consensus DCE,DWI,and T2WI(P<0.05).The combined method(DCE+DWI+T2WI)had a higher AUC(0.865)than any of the individual modality(DCE:0.786;DWI:0.793;T2WI:0.809)(P<0.05).For non-mass-like lesions,DWI signal intensity was a significant predictor of malignancy(P=0.036),but the model using DWI alone had a low AUC(0.669).Conclusion Morphological assessment using the combination of DCE,DWI,and T2WI provides better diagnostic value in differentiating benign and malignant breast mass-like lesions than assessment with only one of the modalities.
文摘目的观察基于相位对比(PC)MRI颅内血流动力学参数预测急性高原反应(AMS)的价值。方法前瞻性招募72名健康青年志愿者,于平原地区采集平静呼吸及轻、中及重度瓦尔萨尔瓦动作(VM)下的颈内动脉(ICA)及颈内静脉(IJV)PC MRI并记录ICA及IJV血流动力学参数;根据急进海拔4411 m的高原地区10 h后路易斯湖评分(LLS)结果划分AMS组(n=9)与无AMS组(n=63);采用单因素及多因素logistic回归分析筛选各状态下AMS的独立预测因素,构建单一及联合VM状态预测模型;绘制受试者工作特征曲线,计算曲线下面积(AUC),评估各模型预测效能。结果轻度VM下ICA搏动指数(PI ICA)、中度VM下IJV面积(S IJV)及重度VM下IJV阻力指数(RI IJV)均为AMS独立预测因素(P均<0.05)。联合VM状态模型(AUC=0.869)预测AMS的效能高于单一VM状态模型(AUC=0.698~0.738)。结论基于轻度VM PI ICA、中度VM S IJV及重度VM RI IJV构建的模型可有效预测AMS。
文摘目的探讨动态对比增强磁共振成像(dynamic contrast-enhanced magnetic resonance imaging,DCE-MRI)参数对喉癌术后放疗短期预后的评估价值。方法本研究采用病例-对照研究设计方案,选取浙江省湖州市中心医院放射科2021年1月至2023年12月收治的127例喉癌患者为研究对象,患者接受不同肿瘤处理方式后进行根治性放疗。根据患者术后放疗肿瘤是否复发分为复发组(50例)和未复发组(77例),比较2组患者DCE-MRI参数[容量转移常数(volume transfer constant,Ktrans)、速率常数(rateconstant,Kep)、细胞外血管外间隙容积比(volumefractionofextracellular extravascular space,Ve)]及临床资料,采用多因素Logistic回归模型分析喉癌患者术后放疗短期预后的影响因素。采用受试者工作特征(receiver operating characteristic,ROC)及其曲线下面积(area under the curve,AUC)评估DCE-MRI参数对喉癌患者术后放疗短期预后的预测效能。结果复发组患者手术切缘阳性、术前病灶最大径≥4 cm、溃疡型病灶、N分期为N1~N3期、分期为晚期(Ⅲ、Ⅳ)占比均高于未复发组(P<0.05);复发组的Ktrans、Kep高于未复发组,Ve低于未复发组(P<0.05);多因素Logistic回归分析显示,手术切缘为阳性、N分期为N1~N3期、Ktrans、Kep是喉癌术后放疗短期预后的独立危险因素,Ve是保护因素(P<0.05);ROC结果显示,Ktrans、Kep、Ve三参数联合应用和五指标联合应用的Logistic回归模型诊断喉癌术后放疗短期预后的AUC(95%CI)分别为0.920(0.858~0.961)、0.923(0.862~0.963),三参数联合应用较单独应用的AUC明显提高(P<0.05),与五指标联合应用的AUC比较无差异。结论DCE-MRI参数Ktrans、Kep、Ve与喉癌术后放疗短期预后密切相关,Ktrans、Kep、Ve联合对喉癌患者术后放疗短期预后具有较好的预测效能。
