Abstract Heavy metals in water can be deposited on graphite flakes, which can be used as an enrichment method for laser-induced breakdown spectroscopy （LIBS） and is studied in this paper. The graphite samples were p...Abstract Heavy metals in water can be deposited on graphite flakes, which can be used as an enrichment method for laser-induced breakdown spectroscopy （LIBS） and is studied in this paper. The graphite samples were prepared with an automatic device, which was composed of a loading and unloading module, a quantitatively adding solution module, a rapid heating and drying module and a precise rotating module. The experimental results showed that the sample preparation methods had no significant effect on sample distribution and the LIBS signal accumulated in 20 pulses was stable and repeatable. With an increasing amount of the sample solution on the graphite flake, the peak intensity at Cu I 324.75 nm accorded with the exponential function with a correlation coefficient of 0.9963 and the background intensity remained unchanged. The limit of detection （LOD） was calculated through linear fitting of the peak intensity versus the concentration. The LOD decreased rapidly with an increasing amount of sample solution until the amount exceeded 20 mL and the correlation coefficient of exponential function fitting was 0.991. The LOD of Pb, Ni, Cd, Cr and Zn after evaporating different amounts of sample solution on the graphite flakes was measured and the variation tendency of their LOD with sample solution amounts was similar to the tendency for Cu. The experimental data and conclusions could provide a reference for automatic sample preparation and heavy metal in situ detection.展开更多
The morphological quantification of the proximal tibia of the knee joint is important in knee replacement.Accurate knowledge of these parameters provides the basis for design of the tibial prosthesis and its fixation....The morphological quantification of the proximal tibia of the knee joint is important in knee replacement.Accurate knowledge of these parameters provides the basis for design of the tibial prosthesis and its fixation.Ideally,a prosthesis that is suitable for the morphological characteristics of Chinese knees is needed.In this paper,a deep learning automatic network framework is designed to achieve automatic segmentation and automatic quantitative analysis of magnetic resonance images of the tibia.An enhanced feature fusion network structure is designed,including high and low-level feature fusion path modules to create accurate segmentation of the tibia.A new method of extracting feature points and lines from outline contours of the proximal tibia is designed to automatically calculate six clinical morphological linear parameters of the tibia in real-time.The final result is an automatic visualisation of the tibial contour and automated extraction of tibial morphometric parameters.Validation of the results from our system against a gold standard obtained by manual processing by expert clinicians showed the Dice coefficient to be 0.97,the accuracy to be 0.98,and the correlation coefficients for all six morphological parameters of the automatic quantification of the tibia are above 0.96.The gender-specific study found that the values of the proximal tibial linear parameters of internal and external tibial diameter,anterior and posterior diameter,lateral plateau length,lateral plateau width,medial plateau length,and medial plateau width in male patients are significantly greater than in female patients(all P values<0.01).The results enrich the use of deep learning in medicine,providing orthopaedic specialists with a valuable and intelligent quantitative tool that can assess the progression and changes in osteoarthritis of the knee joint.展开更多
基金supported by National Natural Science Foundation of China(No.60908018)National High Technology Research and Development Program of China(No.2013AA065502)Anhui Province Outstanding Youth Science Fund of China(No.1108085J19)
文摘Abstract Heavy metals in water can be deposited on graphite flakes, which can be used as an enrichment method for laser-induced breakdown spectroscopy （LIBS） and is studied in this paper. The graphite samples were prepared with an automatic device, which was composed of a loading and unloading module, a quantitatively adding solution module, a rapid heating and drying module and a precise rotating module. The experimental results showed that the sample preparation methods had no significant effect on sample distribution and the LIBS signal accumulated in 20 pulses was stable and repeatable. With an increasing amount of the sample solution on the graphite flake, the peak intensity at Cu I 324.75 nm accorded with the exponential function with a correlation coefficient of 0.9963 and the background intensity remained unchanged. The limit of detection （LOD） was calculated through linear fitting of the peak intensity versus the concentration. The LOD decreased rapidly with an increasing amount of sample solution until the amount exceeded 20 mL and the correlation coefficient of exponential function fitting was 0.991. The LOD of Pb, Ni, Cd, Cr and Zn after evaporating different amounts of sample solution on the graphite flakes was measured and the variation tendency of their LOD with sample solution amounts was similar to the tendency for Cu. The experimental data and conclusions could provide a reference for automatic sample preparation and heavy metal in situ detection.
基金National Natural Science Foundation of China(Project Nos.11772214 and 11972243)supported by the Shanxi Huajin Orthopaedic Public Foundation.
文摘The morphological quantification of the proximal tibia of the knee joint is important in knee replacement.Accurate knowledge of these parameters provides the basis for design of the tibial prosthesis and its fixation.Ideally,a prosthesis that is suitable for the morphological characteristics of Chinese knees is needed.In this paper,a deep learning automatic network framework is designed to achieve automatic segmentation and automatic quantitative analysis of magnetic resonance images of the tibia.An enhanced feature fusion network structure is designed,including high and low-level feature fusion path modules to create accurate segmentation of the tibia.A new method of extracting feature points and lines from outline contours of the proximal tibia is designed to automatically calculate six clinical morphological linear parameters of the tibia in real-time.The final result is an automatic visualisation of the tibial contour and automated extraction of tibial morphometric parameters.Validation of the results from our system against a gold standard obtained by manual processing by expert clinicians showed the Dice coefficient to be 0.97,the accuracy to be 0.98,and the correlation coefficients for all six morphological parameters of the automatic quantification of the tibia are above 0.96.The gender-specific study found that the values of the proximal tibial linear parameters of internal and external tibial diameter,anterior and posterior diameter,lateral plateau length,lateral plateau width,medial plateau length,and medial plateau width in male patients are significantly greater than in female patients(all P values<0.01).The results enrich the use of deep learning in medicine,providing orthopaedic specialists with a valuable and intelligent quantitative tool that can assess the progression and changes in osteoarthritis of the knee joint.
文摘目的比较三种自动勾画软件(Pinnacle 9.10、LinkingMed和Manteia)勾画上腹部危及器官(OAR)的准确性。方法选取了26例上腹部肿瘤患者,由一名资深的临床医师手动勾画OAR(肝脏、脊髓、双肾、胰腺和胃),并采用三种软件对其进行自动勾画。以手动勾画为金标准,计算并比较三种自动勾画结果的质心偏差(Center of Mass Deviation,DC)、Dice相似性系数(Dice Similarity Coefficient,DSC)、Hausdorff距离(Hausdorff Distance,HD)、包容性指数(Inclusive Index,IncI)和敏感性指数(Sensitivity Index,SI)。采用单因素方差分析评价各项指标的统计学差异,同时比较了三种软件勾画结果的准确性。结果肝、双肾和脊髓的DC、DSC、HD、IncI和SI,LinkingMed和Manteia组与Pinnacle组有统计学差异(P<0.05),inkingMed和Manteia组的勾画效果优于Pinnacle组。对于胃和胰腺的结果,Manteia组优于Lingkingmed组,除了IncI,均有统计学差异。结论对于肝、肾和脊髓,这三种软件均有较好的勾画效果,LinkingMed和Manteia的勾画效果更优于Pinnacle软件。对于胃和胰腺,Manteia的勾画效果优于LinkingMed。
