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Concepts for tissue characterization in clinical practice
ABSTRACTS, ULTRASONIC
IMAGING AND TISSUE CHARACTERIZATION
SYMPOSIUM
ABSTRACTS Monday,
June
1. Tissue Parameters
3 I
1.1 SPECTRAL ESTIMATION IN TISSUE CHARACTERIZATION: AUTOREGRESSIVE MOVING-AVERAGE METHODS VS. CLASSICAL METHODS, K. A. Wear,’ R. F. Wagner,’ M. F. Insana,*andT. J. Hall’, ‘Centerfor DevicesandRadiologicalHealth,FDA, 12720TwinbrookPkwy, Rockville, MD 20857,andDepartmentof Radiology,Universityof Kansas MedicalCenter,KansasCity, KS, 66103. Spectralestimationis of fundamentalimportancein ultrasonictissuecharacterization.Spectra are commonlyestimatedfrom squaredmoduliof FFT’s of digitizedradiofrequencysignals.However, if the datacontaina considerable amountof noise,or possess an intrinsicallyrandomnature(as is the casewith speckle),then this methodcan exhibit a high degreeof variance. Autoregressivemovingaveragemethodsfor spectralestimationoffer an alternativeto classical(FFT based)methodsandhave beendemonstrated to be useful in many fields, includingradar, seismology,oil exploration,speech processing,Dopplerultrasound,radiography,andstockmarketforecasting. We havepreviously reportedon the efficacy of pure moving-averageandpure autoregressive spectralestimates on ultrasounddata,acquiredusinga clinicalscanneranda tissue-mimicking phantom. We concludedthat pure moving averagemodelswerepreferableto pure autoregressive modelsfor this application. In addition, we found that moving averagespectralestimateswere comparableto, but offeredno significantadvantage over, classicalmethodsin termsof bias,varianceandmeansquareerror. Wehaveexpandedthiswork to includecombinedautoregressive movingaveragespectralestimation. We haveevaluatedautoregressive movingaveragespectralestimates on phantomdatain termsof bias, variance,andmeansquareerror. In addition,we havedevelopeda theoreticalmodelwhich showsthe correspondence betweenpuremovingaverageandclassicalspectralestimates.This modeldelineates the conditionsunderwhichthe two spectralestimates are equivalent. Undertheseconditions,the classical biasvs. variancetrade-off curve representsa boundwhich cannotbe surpassed by the pure moving averageestimate.
CONCEPTSFOR TISSUE CHARACTERIZATION IN CLINICAL PRACTICE, M. Fein, I. Zuna, G. vanKaick andU R%th,Instituteof RadiologyandPathophysiologie, GermanCancerResearch Center,INF 280, 6900Heidelberg,FRG. Data acquisition,raw dataprocessing,parameterextractionandclassificationof the resultsare the components of a tissuecharacterizationsystem. While ultrasonicimaginggainsmore and more importancein clinical practice,tissuecharacterizationis still not well established.In this paper,the possibleconfigurations,online or offline systems,are discussedwith regard to clinical practice. Realizationandimplementation of the knowledgebasefor classificationarethe mainproblemsusinga parametricimagedescriptionfor tissuecharacterization.Our newly developedtissuecharacterization systemis presentedanddiscussed asonepossiblesolution. All stepsfor the configurationof a tissue characterizationsystemfor liver tissueare explained. The data acquisitionis realizedby a LeCroy digitizer or a video digitizer. All other systemcomponents are combinedin one menudriven program on a Unix workstation. On this system,the doctorhimselfcanreplaythe storedimageon the monitor, enterthe patientdataandchoosea regionof interestQOI). Thenthe selectedparameters arecalculated within the ROI. All necessarystatisticalanalysiscan alsobe donein this program:the selectionof a usefulparameterset, a discriminantanalysisandthe calculationof the classificator.First resultsof our liver studywill bepresented.Easyhandling,quickcalculationandthe displayof all relevantresultsare the basicadvantagesof this system. The amountof data for images(0.4 Mbyte per image)is one problemin manystudies.Therefore,selectionof the ROI andthe parametercalculationshouldbe done insidethe ultrasoundmachineor the systemshouldbe a part of an integratedsystemfor the storageof all kindsof imagesin onehospital. Nevertheless, the classificationof the resultsshouldbe carriedout 1.2
189
ABSTRACTS, ULTRASONIC IMAGING AND TISSUE CHARACTERIZATION
SYMPOSIUM
offline. Thus, objective image parameters can be combined with the subjective image analysis of the investigator. This combination of an objective and subjective image description is planned to achieve improved diagnosis. APPLICATION OF NEURAL NETS TO ULTRASOUND TISSUECHARACTERIZATION, J. S. Ostrem,A. D. ValdesandP. D. Edmonds,Bioengineering Research andElectromagnetic Sciences Laboratories,SRI International,Menlo Park, CA 94025. Measurements of ultrasoundspeedandfrequencydependence of attenuationcoefficient in human breastbiopsyspecimens werepreviouslyanalyzedby two differentstatisticaltechniques.Thegoalswere to discriminatenormalfrom pathologicaltissues andbenignfrom malignanttissue,if possible.128cases, comprising50 normal,56 benignand22 malignantwereanalyzedby classicaldiscriminantanalysisand classificationandregression trees(CART). While soundspeedwasconsistentlyidentifiedasthevariable with the greatestpower to discriminatenormalfrom pathologicaltissues,considerable difficulty was found in discriminatingbenignfrom malignanttissues.Nevertheless,CART appeared morepromising thandiscriminantanalysis. Another classificationapproachof interestis the neuralnetwork. Our data were presentedto a network in a fully-connected,feed-forwardconfigurationwith 6 inputs(the variables),2 hiddenlayers of 12 andeither6 or 9 units, respectively,and3 outputs(the categories).75% of the data(comprising thetrainingset)wereall correctly classifiedafter adjustment of the networkweightsby a backpropagation algorithmthat minimizedmean-square classification error. Amongthetestset,comprisingthe remaining 25% of thedata, 11112normal,11/15benign,and4/4 malignantwereclassifiedcorrectly. 1112normals was misclassified as benignand4/15 benignswere misclassified as normals. This performancewas superiorto that of the CART anddiscriminantanalyses. This work wassupportedby PHS-NIH-NC1grant CA34398 1.3
1.4 TISSUECHARACTERIZATION BY HIERARCHICAL CLUSTERINGTECHNIQUES,N. H. Wang,J. T. Sheu,andB. Ho, MichiganStateUniversity, UltrasoundResearch Laboratory,EastLansing, MI 48824. Most tissuecharacterizationobtainedfrom pulse-echoultrasoundis basedon the acoustic impedance differenceat tissue interfaces.Unfortunately,it hasbeenshownthat there is no significant variation of acousticimpedance betweennormalandcancerous tissues[11.Onthe otherhand,tumorand cancerous tumorhavequitedifferentattenuationcharacteristics andvelocity propagation.However,only limitedsuccess hasbeenfoundby usingtheseproperties. The objectiveof this work isto employmorefeaturesto characterizebiologicaltissues by theuse of hierarchicalclusteringmethods.In our approach,five featuresfrom the echoreturn areextracted;they arethe total energy, centralfrequency,frequencyat whichthe peakamplitudeoccurs,3-dB bandwidth of the echospectrum,and the correlationcoefftcientbetweenthe incidentandreflectedsignals.These featurescontainnot only the informationof acousticimpedancevariation but alsothe attenuationand velocity characteristics.Basedon thesedata, hierarchicalclusteringtechniquessuch as singlelink, completelink andWard’smethods[2] areusedfor clusterformation.Fromthe resultsof clustering,the typesof tissuecanbe readily identified.A sectionof humanbrain with hemorrhaged tumor is usedfor the preliminarystudy. Color graphicis usedto indicatethe amountof featurevariation. The indicesof clustervalidity will be reviewed.Techniquesaswell asexperimentalresultswill be presented. Birnholz, J.C. IEEE Ultrasonic Symposium, pp. 31-32(1972). PI Jain, A.K andDubes,R.C., Algorithmsfor Clustering Data, Prentice-Hall,Inc., N. J. (1988). PI
QUANTITATIVE MEASURESOFBACKSCATTERFROM HUMAN SKELETAL MUSCLE: CHANGESWITH DUCHENNE’SMUSCULAR DYSTROPHY, Maria Helguera,’JackG. Mottley,’ ShreedeviPandya’andRichardMoxley*, RochesterCenterfor BiomedicalUltrasound,‘Departmentof ElectricalEngineering,Collegeof EngineeringandApplied Sciences,and department of Neurology, Schoolof MedicineandDentistry, University of Rochester,Rochester,NY 14627. Preliminarystudieshave shownthat variationsin ultrasonicpropertiessuchasbackscatterand attenuationof skeletalmusclemay be relatedto the physiologicstateof thosetissues,thereforeholding greatpromiseas indicesfor quantitativetissuecharacterization.This studywasdesignedto elucidate patternsof changein ultrasonicbackscatterfrom skeletalmusclewith disease.Five normalvolunteers andfive patientssufferingfrom Duchenne’sMuscularDystrophy @MD) werestudied. A water-tilled 1.5
190
IMAGING AND TISSUE CHARACTERIZATION
SYMPOSIUM
ABSTRACTS Monday,
June
1. Tissue Parameters
3 I
1.1 SPECTRAL ESTIMATION IN TISSUE CHARACTERIZATION: AUTOREGRESSIVE MOVING-AVERAGE METHODS VS. CLASSICAL METHODS, K. A. Wear,’ R. F. Wagner,’ M. F. Insana,*andT. J. Hall’, ‘Centerfor DevicesandRadiologicalHealth,FDA, 12720TwinbrookPkwy, Rockville, MD 20857,andDepartmentof Radiology,Universityof Kansas MedicalCenter,KansasCity, KS, 66103. Spectralestimationis of fundamentalimportancein ultrasonictissuecharacterization.Spectra are commonlyestimatedfrom squaredmoduliof FFT’s of digitizedradiofrequencysignals.However, if the datacontaina considerable amountof noise,or possess an intrinsicallyrandomnature(as is the casewith speckle),then this methodcan exhibit a high degreeof variance. Autoregressivemovingaveragemethodsfor spectralestimationoffer an alternativeto classical(FFT based)methodsandhave beendemonstrated to be useful in many fields, includingradar, seismology,oil exploration,speech processing,Dopplerultrasound,radiography,andstockmarketforecasting. We havepreviously reportedon the efficacy of pure moving-averageandpure autoregressive spectralestimates on ultrasounddata,acquiredusinga clinicalscanneranda tissue-mimicking phantom. We concludedthat pure moving averagemodelswerepreferableto pure autoregressive modelsfor this application. In addition, we found that moving averagespectralestimateswere comparableto, but offeredno significantadvantage over, classicalmethodsin termsof bias,varianceandmeansquareerror. Wehaveexpandedthiswork to includecombinedautoregressive movingaveragespectralestimation. We haveevaluatedautoregressive movingaveragespectralestimates on phantomdatain termsof bias, variance,andmeansquareerror. In addition,we havedevelopeda theoreticalmodelwhich showsthe correspondence betweenpuremovingaverageandclassicalspectralestimates.This modeldelineates the conditionsunderwhichthe two spectralestimates are equivalent. Undertheseconditions,the classical biasvs. variancetrade-off curve representsa boundwhich cannotbe surpassed by the pure moving averageestimate.
CONCEPTSFOR TISSUE CHARACTERIZATION IN CLINICAL PRACTICE, M. Fein, I. Zuna, G. vanKaick andU R%th,Instituteof RadiologyandPathophysiologie, GermanCancerResearch Center,INF 280, 6900Heidelberg,FRG. Data acquisition,raw dataprocessing,parameterextractionandclassificationof the resultsare the components of a tissuecharacterizationsystem. While ultrasonicimaginggainsmore and more importancein clinical practice,tissuecharacterizationis still not well established.In this paper,the possibleconfigurations,online or offline systems,are discussedwith regard to clinical practice. Realizationandimplementation of the knowledgebasefor classificationarethe mainproblemsusinga parametricimagedescriptionfor tissuecharacterization.Our newly developedtissuecharacterization systemis presentedanddiscussed asonepossiblesolution. All stepsfor the configurationof a tissue characterizationsystemfor liver tissueare explained. The data acquisitionis realizedby a LeCroy digitizer or a video digitizer. All other systemcomponents are combinedin one menudriven program on a Unix workstation. On this system,the doctorhimselfcanreplaythe storedimageon the monitor, enterthe patientdataandchoosea regionof interestQOI). Thenthe selectedparameters arecalculated within the ROI. All necessarystatisticalanalysiscan alsobe donein this program:the selectionof a usefulparameterset, a discriminantanalysisandthe calculationof the classificator.First resultsof our liver studywill bepresented.Easyhandling,quickcalculationandthe displayof all relevantresultsare the basicadvantagesof this system. The amountof data for images(0.4 Mbyte per image)is one problemin manystudies.Therefore,selectionof the ROI andthe parametercalculationshouldbe done insidethe ultrasoundmachineor the systemshouldbe a part of an integratedsystemfor the storageof all kindsof imagesin onehospital. Nevertheless, the classificationof the resultsshouldbe carriedout 1.2
189
ABSTRACTS, ULTRASONIC IMAGING AND TISSUE CHARACTERIZATION
SYMPOSIUM
offline. Thus, objective image parameters can be combined with the subjective image analysis of the investigator. This combination of an objective and subjective image description is planned to achieve improved diagnosis. APPLICATION OF NEURAL NETS TO ULTRASOUND TISSUECHARACTERIZATION, J. S. Ostrem,A. D. ValdesandP. D. Edmonds,Bioengineering Research andElectromagnetic Sciences Laboratories,SRI International,Menlo Park, CA 94025. Measurements of ultrasoundspeedandfrequencydependence of attenuationcoefficient in human breastbiopsyspecimens werepreviouslyanalyzedby two differentstatisticaltechniques.Thegoalswere to discriminatenormalfrom pathologicaltissues andbenignfrom malignanttissue,if possible.128cases, comprising50 normal,56 benignand22 malignantwereanalyzedby classicaldiscriminantanalysisand classificationandregression trees(CART). While soundspeedwasconsistentlyidentifiedasthevariable with the greatestpower to discriminatenormalfrom pathologicaltissues,considerable difficulty was found in discriminatingbenignfrom malignanttissues.Nevertheless,CART appeared morepromising thandiscriminantanalysis. Another classificationapproachof interestis the neuralnetwork. Our data were presentedto a network in a fully-connected,feed-forwardconfigurationwith 6 inputs(the variables),2 hiddenlayers of 12 andeither6 or 9 units, respectively,and3 outputs(the categories).75% of the data(comprising thetrainingset)wereall correctly classifiedafter adjustment of the networkweightsby a backpropagation algorithmthat minimizedmean-square classification error. Amongthetestset,comprisingthe remaining 25% of thedata, 11112normal,11/15benign,and4/4 malignantwereclassifiedcorrectly. 1112normals was misclassified as benignand4/15 benignswere misclassified as normals. This performancewas superiorto that of the CART anddiscriminantanalyses. This work wassupportedby PHS-NIH-NC1grant CA34398 1.3
1.4 TISSUECHARACTERIZATION BY HIERARCHICAL CLUSTERINGTECHNIQUES,N. H. Wang,J. T. Sheu,andB. Ho, MichiganStateUniversity, UltrasoundResearch Laboratory,EastLansing, MI 48824. Most tissuecharacterizationobtainedfrom pulse-echoultrasoundis basedon the acoustic impedance differenceat tissue interfaces.Unfortunately,it hasbeenshownthat there is no significant variation of acousticimpedance betweennormalandcancerous tissues[11.Onthe otherhand,tumorand cancerous tumorhavequitedifferentattenuationcharacteristics andvelocity propagation.However,only limitedsuccess hasbeenfoundby usingtheseproperties. The objectiveof this work isto employmorefeaturesto characterizebiologicaltissues by theuse of hierarchicalclusteringmethods.In our approach,five featuresfrom the echoreturn areextracted;they arethe total energy, centralfrequency,frequencyat whichthe peakamplitudeoccurs,3-dB bandwidth of the echospectrum,and the correlationcoefftcientbetweenthe incidentandreflectedsignals.These featurescontainnot only the informationof acousticimpedancevariation but alsothe attenuationand velocity characteristics.Basedon thesedata, hierarchicalclusteringtechniquessuch as singlelink, completelink andWard’smethods[2] areusedfor clusterformation.Fromthe resultsof clustering,the typesof tissuecanbe readily identified.A sectionof humanbrain with hemorrhaged tumor is usedfor the preliminarystudy. Color graphicis usedto indicatethe amountof featurevariation. The indicesof clustervalidity will be reviewed.Techniquesaswell asexperimentalresultswill be presented. Birnholz, J.C. IEEE Ultrasonic Symposium, pp. 31-32(1972). PI Jain, A.K andDubes,R.C., Algorithmsfor Clustering Data, Prentice-Hall,Inc., N. J. (1988). PI
QUANTITATIVE MEASURESOFBACKSCATTERFROM HUMAN SKELETAL MUSCLE: CHANGESWITH DUCHENNE’SMUSCULAR DYSTROPHY, Maria Helguera,’JackG. Mottley,’ ShreedeviPandya’andRichardMoxley*, RochesterCenterfor BiomedicalUltrasound,‘Departmentof ElectricalEngineering,Collegeof EngineeringandApplied Sciences,and department of Neurology, Schoolof MedicineandDentistry, University of Rochester,Rochester,NY 14627. Preliminarystudieshave shownthat variationsin ultrasonicpropertiessuchasbackscatterand attenuationof skeletalmusclemay be relatedto the physiologicstateof thosetissues,thereforeholding greatpromiseas indicesfor quantitativetissuecharacterization.This studywasdesignedto elucidate patternsof changein ultrasonicbackscatterfrom skeletalmusclewith disease.Five normalvolunteers andfive patientssufferingfrom Duchenne’sMuscularDystrophy @MD) werestudied. A water-tilled 1.5
190