2023
Direct estimation of metabolite maps from undersampled k-space data using linear tangent space alignment
Ma C, Marin T, Han P, El Fakhri G. Direct estimation of metabolite maps from undersampled k-space data using linear tangent space alignment. Proceedings Of The International Society For Magnetic Resonance In Medicine ... Scientific Meeting And Exhibition. 2023 DOI: 10.58530/2023/0496.Peer-Reviewed Original Research
2019
Penalized Relative Error Estimation of a Partially Functional Linear Multiplicative Model
Zhang T, Huang Y, Zhang Q, Ma S, Ahmed S. Penalized Relative Error Estimation of a Partially Functional Linear Multiplicative Model. Contributions To Statistics 2019, 127-144. DOI: 10.1007/978-3-030-17519-1_10.Peer-Reviewed Original ResearchFinite sample performanceRelative error estimationTecator dataScalar responseLinear multiplicative modelsScalar variablesSample performanceFunctional predictorsError estimationBasis functionsMultiplicative modelConsistency propertiesLeast squaresLoss functionTrue structureRelative errorClassic methodsEstimationPenalizationModelFunctional dataSquaresSimulations
2016
Direct PET Reconstruction of Regional Binding Potentials
Gravel P, Soucy J, Reader A. Direct PET Reconstruction of Regional Binding Potentials. 2016, 1-3. DOI: 10.1109/nssmic.2016.8069457.Peer-Reviewed Original Research
2010
Motion compensation for fully 4D PET reconstruction using PET superset data
Verhaeghe J, Gravel P, Mio R, Fukasawa R, Rosa-Neto P, Soucy J, Thompson C, Reader A. Motion compensation for fully 4D PET reconstruction using PET superset data. Physics In Medicine And Biology 2010, 55: 4063-4082. PMID: 20601774, DOI: 10.1088/0031-9155/55/14/008.Peer-Reviewed Original ResearchConceptsSubject head motionPET reconstructionPET image reconstructionHead motionImage reconstruction algorithmMotion compensation methodImage framesOptical tracking systemMotion compensationList-mode eventsImage reconstructionTracking systemReconstruction algorithmSingle instanceMotion compensation strategyAlgorithmMultiple time framesReconstruction methodNormalization schemeImportant issuePET dataBasis functionsDynamic PET imagingHead movementsParametric imagesAsymptotic efficiency and finite-sample properties of the generalized profiling estimation of parameters in ordinary differential equations
Qi X, Zhao H. Asymptotic efficiency and finite-sample properties of the generalized profiling estimation of parameters in ordinary differential equations. The Annals Of Statistics 2010, 38: 435-481. DOI: 10.1214/09-aos724.Peer-Reviewed Original ResearchOrdinary differential equationsDifferential equationsBasis functionsEstimation procedureAsymptotic covariance matrixEfficient estimation procedureFinite sample propertiesMaximum likelihood estimationODE solutionStatistical propertiesAsymptotic normalityAsymptotic efficiencySpline approximationTrue solutionCovariance matrixComputational efficiencyLikelihood estimationLinear combinationUniform normApproximationEstimation methodEquationsObserved dataDynamic behaviorSolution
2001
Multi-Layered Image Representation
Meyer F, Averbuch A, Coifman R. Multi-Layered Image Representation. Computational Imaging And Vision 2001, 19: 281-304. DOI: 10.1007/978-94-015-9715-9_10.Peer-Reviewed Original ResearchImage representationMulti-layer approachVideo codingImage compressionLossy wayImage understandingDifferent bitratesDifferent transformsMulti-layered representationRepresentation techniquesTexture layerMain contributionSparse representationDecomposition algorithmBasis functionsAlgorithmNew paradigmPrevious compressionImagesRepresentationBeautiful applicationsBitrateMeaningful way
1996
Model-based deformable surface finding for medical images
Staib L, Duncan J. Model-based deformable surface finding for medical images. IEEE Transactions On Medical Imaging 1996, 15: 720-731. PMID: 18215953, DOI: 10.1109/42.538949.Peer-Reviewed Original Research
1995
Local discriminant bases and their applications
Saito N, Coifman R. Local discriminant bases and their applications. Journal Of Mathematical Imaging And Vision 1995, 5: 337-358. DOI: 10.1007/bf01250288.Peer-Reviewed Original ResearchOrthonormal basisStatistical methodsClassification problemSignificant coordinatesBasis functionsSignal classification problemsTrigonometric basisLocal trigonometric basesLinear discriminant analysisInput signalDirect applicationRegression treesProblemBest basis algorithmAlgorithmTime-frequency planeSignal componentsFurther applicationCoordinatesDimensionalityImage classification problemsApplicationsSmall numberTexture classification problemOn local orthonormal bases for classification and regression
Saito N, Coifman R. On local orthonormal bases for classification and regression. 2013 IEEE International Conference On Acoustics, Speech And Signal Processing 1995, 3: 1529-1532 vol.3. DOI: 10.1109/icassp.1995.479852.Peer-Reviewed Original ResearchOrthonormal basisRegression problemsLocal orthonormal basisRelative entropyRegression errorsStatistical methodsSynthetic examplesSignificant coordinatesBasis functionsLinear discriminant analysisRegression methodEnergy distributionRegression treesProblemClassification problemTime-frequency planeSignal classificationEntropyTraditional methodsCoordinatesDimensionalityTime-frequency energy distributionSmall number
1994
Selection of best bases for classification and regression
Coifman R, Saito N. Selection of best bases for classification and regression. 1994, 51. DOI: 10.1109/wits.1994.513882.Peer-Reviewed Original Research
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