Objective To investigate the effect of ClearInfinity deep learning reconstruction algorithm on image quality and radiation dose of abdominal computed tomography angiography(CTA)at low kV and low contrast medium.Methods One hundred patients who underwent abdominal CTA were selected and randomly divided into group A and group B.Group A:tube voltage 70 kV,con-trast medium 30-35 mL,divided into A1 and A2 subgroups according to reconstruction algorithm,group A1 50%ClearInfinity,group A2 50%ClearView iterative algorithm;group B:tube voltage 100 kV,contrast medium 60-70 mL,50%ClearView.CT values and standard deviation(SD)values of region of interest(ROI)of abdominal aorta,proper hepatic artery,superior mesenteric artery,renal artery and common iliac artery were evaluated objectively,while signal-to-noise ratio(SNR)and contrast-to-noise ratio(CNR)were calculated;subjective scores were evaluated by two physicians;radiation doses of groups A and B were analyzed.Results Volume CT dose index(CTDIvol),dose length product(DLP)and effective dose(ED)in group A were significantly lower than those in group B(P<0.05),subjective scores in group A1 and group B were higher than those in group A2(P<0.05),and there was no difference between group A1 and group B(P>0.05).Compared with group A1,SNR and CNR of all vessels in group A2 were significantly decreased.CT values of abdominal aorta and common iliac artery,CNR of common iliac artery and supe-rior mesenteric artery in group B were significantly increased,SNR of renal artery was significantly decreased(P<0.05).Conclusion ClearInfinity deep learning reconstruction algorithm combined with 70 kV scanning technology can obtain better abdom-inal CTA image quality,and effectively reduce the radiation dose and contrast medium of patients,which has high clinical application value.

Objective To explore the characteristic changes in white matter microstructure in Parkinson's disease(PD)patients via automated fiber quantification(AFQ)technology,providing a basis for the identification and diagnosis of PD,and to analyze the feasibility of combining the AFQ method with support vector machine(SVM)in the diagnosis of PD.Methods Forty patients with primary PD(PD group)and 20 healthy controls(HC)(HC group)were prospectively selected.The AFQ technology was applied for white matter fiber tract analysis.Statistical analyses were performed using FSL(v6.0)software and SPSS 27.0 software.Independent-sample t-tests were conducted for comparisons between groups in AFQ analysis.The AFQ method was used to analyze the relationship between diffusion tensor imaging(DTI)parameters and Montreal Cognitive Assessment(MoCA)scores.Results(1)The results of AFQ analysis revealed that compared with the HC group,the PD group exhibited significantly lower fractional anisotropy(FA)values in the right cingulum bundle,left cingulum bundle hippocampus,and left uncinate fasciculus,with no differences in the FA values of the remaining 17 fiber tracts.Moreover,PD group demonstrated higher mean diffusivity(MD)values in the left cingulum bundle,left cingulum bundle hippocampus,left inferior frontal occipital fasciculus,left inferior longitudinal fasciculus,left superior longitudinal fasciculus,and left uncinate fasciculus.These differences were statistically significant(P<0.05),while no significant differences were found in the MD values of the remaining 14 fiber tracts.Furthermore,the MD values of the left inferior frontal occipital fasciculus,and left inferior longitudinal fasciculus were negatively correlated with the MoCA scores.(2)The classification results of SVM showed that the best results were achieved when combining the differential nodes of FA and MD as classification features,with an area under the curve(AUC)of 0.922,an accuracy of 84.81%,a sensitivity of 87.50%,and a specificity of 82.05%.Conclusion The DTI parameters in PD patients can serve as potential biomarkers for diagnosis.The AFQ methods provides an effective approach for detecting alterations white matter tract integrity,offering important insights for the identification and diagnosis of PD.The best results are achieved when combining the differential nodes of FA and MD as classification features.

Objective To investigate the effect of ClearInfinity deep learning reconstruction algorithm on image quality and radiation dose of abdominal computed tomography angiography(CTA)at low kV and low contrast medium.Methods One hundred patients who underwent abdominal CTA were selected and randomly divided into group A and group B.Group A:tube voltage 70 kV,con-trast medium 30-35 mL,divided into A1 and A2 subgroups according to reconstruction algorithm,group A1 50%ClearInfinity,group A2 50%ClearView iterative algorithm;group B:tube voltage 100 kV,contrast medium 60-70 mL,50%ClearView.CT values and standard deviation(SD)values of region of interest(ROI)of abdominal aorta,proper hepatic artery,superior mesenteric artery,renal artery and common iliac artery were evaluated objectively,while signal-to-noise ratio(SNR)and contrast-to-noise ratio(CNR)were calculated;subjective scores were evaluated by two physicians;radiation doses of groups A and B were analyzed.Results Volume CT dose index(CTDIvol),dose length product(DLP)and effective dose(ED)in group A were significantly lower than those in group B(P<0.05),subjective scores in group A1 and group B were higher than those in group A2(P<0.05),and there was no difference between group A1 and group B(P>0.05).Compared with group A1,SNR and CNR of all vessels in group A2 were significantly decreased.CT values of abdominal aorta and common iliac artery,CNR of common iliac artery and supe-rior mesenteric artery in group B were significantly increased,SNR of renal artery was significantly decreased(P<0.05).Conclusion ClearInfinity deep learning reconstruction algorithm combined with 70 kV scanning technology can obtain better abdom-inal CTA image quality,and effectively reduce the radiation dose and contrast medium of patients,which has high clinical application value.

Objective To explore the characteristic changes in white matter microstructure in Parkinson's disease(PD)patients via automated fiber quantification(AFQ)technology,providing a basis for the identification and diagnosis of PD,and to analyze the feasibility of combining the AFQ method with support vector machine(SVM)in the diagnosis of PD.Methods Forty patients with primary PD(PD group)and 20 healthy controls(HC)(HC group)were prospectively selected.The AFQ technology was applied for white matter fiber tract analysis.Statistical analyses were performed using FSL(v6.0)software and SPSS 27.0 software.Independent-sample t-tests were conducted for comparisons between groups in AFQ analysis.The AFQ method was used to analyze the relationship between diffusion tensor imaging(DTI)parameters and Montreal Cognitive Assessment(MoCA)scores.Results(1)The results of AFQ analysis revealed that compared with the HC group,the PD group exhibited significantly lower fractional anisotropy(FA)values in the right cingulum bundle,left cingulum bundle hippocampus,and left uncinate fasciculus,with no differences in the FA values of the remaining 17 fiber tracts.Moreover,PD group demonstrated higher mean diffusivity(MD)values in the left cingulum bundle,left cingulum bundle hippocampus,left inferior frontal occipital fasciculus,left inferior longitudinal fasciculus,left superior longitudinal fasciculus,and left uncinate fasciculus.These differences were statistically significant(P<0.05),while no significant differences were found in the MD values of the remaining 14 fiber tracts.Furthermore,the MD values of the left inferior frontal occipital fasciculus,and left inferior longitudinal fasciculus were negatively correlated with the MoCA scores.(2)The classification results of SVM showed that the best results were achieved when combining the differential nodes of FA and MD as classification features,with an area under the curve(AUC)of 0.922,an accuracy of 84.81%,a sensitivity of 87.50%,and a specificity of 82.05%.Conclusion The DTI parameters in PD patients can serve as potential biomarkers for diagnosis.The AFQ methods provides an effective approach for detecting alterations white matter tract integrity,offering important insights for the identification and diagnosis of PD.The best results are achieved when combining the differential nodes of FA and MD as classification features.