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基于卷积神经网络的低剂量CT图像处理方法.docx

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基于卷积神经网络的低剂量CT图像处理方法 Title:Low-DoseCTImageProcessingMethodBasedonConvolutionalNeuralNetworks Abstract: Low-doseCTimaginghasbecomeapopulartechniqueduetoitsreducedradiationexposuretopatients.However,low-doseCTimagesoftensufferfromdegradedimagequality,includingnoiseandartifacts,whichcanaffecttheaccuracyofdiagnosis.Thispaperproposesanovelimageprocessingmethodbasedonconvolutionalneuralnetworks(CNNs)toenhancethequalityoflow-doseCTimages.Theeffectivenessoftheproposedmethodisdemonstratedthroughexperimentalresults,showingsignificantimprovementsinimagequalityanddiagnosticaccuracy. 1.Introduction Computedtomography(CT)imagingplaysacrucialroleinthediagnosisandmonitoringofvariousdiseases.However,theusageofCTimagingcomeswithconcernsregardingthepotentialrisksassociatedwithradiationexposure.Low-doseCTimagingaimstoreducetheradiationdosewhilemaintainingdiagnosticimagequality.However,thereductioninradiationdoseoftenresultsindegradedimagequality,limitingtheefficacyofdiagnosis.Toaddressthischallenge,thispaperproposesamethodbasedonconvolutionalneuralnetworksforlow-doseCTimageprocessing. 2.RelatedWork Previousstudieshaveexploredvariousmethodsforlow-doseCTimageprocessing,includingiterativereconstructionalgorithms,statisticalmethods,andmachinelearningapproaches.Whilethesemethodshaveachievedcertainimprovements,theyoftensufferfromlimitationssuchashighcomputationalcomplexityorinsufficientabilitytocaptureintricateimagefeatures.Convolutionalneuralnetworks,atypeofdeeplearningalgorithm,haveshowngreatpotentialinvariouscomputervisiontasks,includingimagerestorationandenhancement.ThismotivatestheuseofCNNsforlow-doseCTimageprocessing. 3.Methodology Theproposedmethodconsistsofseveralkeysteps.First,adatasetofpairedlow-doseandstandard-doseCTimagesiscollectedfortrainingtheCNNmodel.Thepairedimagesareusedtoestablishamappingrelationshipbetweenlow-doseandstandard-doseimages,enablingtheCNNtolearntheimagefeaturesassociatedwithhighimagequality.TheCNNarchitectureisdesignedtohavemultipleconvolutionallayers,followed