1.Analysis of correlative factors of diagnostic accuracy for CT-guided percutaneous biopsy of spine lesions
Zhenghua LIU ; Yonghong JIANG ; Qinpeng ZHAO ; Yuting ZHANG ; Benyin LIU ; Yali ZHAO
Journal of Practical Radiology 2016;32(8):1272-1274,1292
Objective To investigate the diagnostic accuracy factors for CT-guided percutaneous biopsy of spine lesions.Methods The clinical and pathological data of 128 patients who were undergone CT-guided percutaneous biopsy of spine lesions were collected.The multivariate stepwise Logistic retrospective study was performed to study the influence of the patient-related factors (sex,age),lesion-related factors (location,bone destruction characteristics,with or without necrosis,with or without calcification),and procedure factors (punc-ture target spot,tissue specimen size)on the diagnostic accuracy.Results The diagnostic accuracy rate of CT-guided percutaneous biopsy of spine lesions was 86.7% (1 1 1/128 ).By multi-factor analysis,bone destruction characteristics (OR = 3.428,P = 0.038 ),with or without necrosis (OR=0.1 93,P =0.012),with or without calcification (OR=0.266,P =0.036),tissue specimen size (OR=0.200, P =0.01 5)were incorporated into the regression equation of the diagnostic accuracy.Conclusion CT-guided percutaneous biopsy of spine lesions has a high diagnostic accuracy.Bone destruction characteristics,with or without necrosis,with or without calcification, tissue specimen size are the independent factors.
2.Applicationvalueofanewgenerationmodel-basediterativereconstructioninchestCTscan
Xiujuan ZUO ; Yonghong JIANG ; Zhenghua LIU ; Yuting ZHANG ; Benyin LIU ; Yaning LI ; Yaqing DUAN
Journal of Practical Radiology 2019;35(7):1143-1147
Objective ToinvestigatetheimpactofCTimagequalityforfilteringbackprojection(FBP),conventionalmodel-based iterativereconstruction(MBIRC)andnewgeneration model-basediterativereconstruction (MBIRN)onchest.Methods Thirtypatientswith chestCTscanwerecollected.FBP,MBIRCandMBIRN wereusedtoreconstructtheimage.Objectivequality[standarddeviation(SD) valueoftheROI,SNR],thenoisereductionrateandSNRimprovementrateofMBIRCand MBIRN withrespecttoFBP werecom-paredacrossthethreeimages.Atthesametime,tworadiologistsusedtheblind methodtoevaluatetheintrapulmonarystructurein thelungalgorithm FBP,MBIRC,MBIRN,andthemediastinalstructure (5-pointsystem)inthestandardalgorithmsFBP,MBIRC, MBIRN.Results ComparedwithFBP,theimagemusclenoisesofMBIRCand MBIRN were76.71% and86.06%lowerthanFBP,respectively, andthefatnoiseswere66.91% and78.18%lowerthanFBP,respectively.Thedifferencewasstatisticallysignificant(P<0.05).The imageSNRofMBIRCandMBIRN were74.12% and84.97% higherthanthatoftheFBPgroup,respectively.ThefatSNRwere65.63% and 76.02% higherthanthatoftheFBPgroup (P<0.05).Thethreealgorithmsshowedstatisticallysignificantdifferencesinsubjective noise,intrapulmonaryvascular,bronchialresolution,mediastinalbloodvessels,andlymphnodes.MBIRN hadthelowestsubjective noise,andthehighestSNR,mediastinalstructure,andintrapulmonaryvesselsandbronchi.Conclusion Comparedwith MBIRC and FBPwithnormaldosechestCTscan,MBIRN cansignificantlyreducethenoiseofchestCTscanimages,improveSNR,and more clearlyshowthedetailsofthescanrangeandlesionedgefeatures.