2023
MCP-Net: Introducing Patlak Loss Optimization to Whole-Body Dynamic PET Inter-Frame Motion Correction
Guo X, Zhou B, Chen X, Chen M, Liu C, Dvornek N. MCP-Net: Introducing Patlak Loss Optimization to Whole-Body Dynamic PET Inter-Frame Motion Correction. IEEE Transactions On Medical Imaging 2023, 42: 3512-3523. PMID: 37368811, PMCID: PMC10751388, DOI: 10.1109/tmi.2023.3290003.Peer-Reviewed Original ResearchMotion estimation blockDeep learning benchmarksGood generalization capabilityMotion correctionMotion correction frameworkMotion prediction errorGeneralization capabilityNetwork performanceNeural networkMotion correction techniqueLearning benchmarksRegistration problemLoss functionEstimation blockLoss optimizationPenalty componentDynamic frameFitting errorSpatial alignmentParametric imagesSpatial misalignmentDynamic positron emission tomographySubject motionPrediction errorCorrection framework
2022
MCP-Net: Inter-frame Motion Correction with Patlak Regularization for Whole-body Dynamic PET
Guo X, Zhou B, Chen X, Liu C, Dvornek N. MCP-Net: Inter-frame Motion Correction with Patlak Regularization for Whole-body Dynamic PET. Lecture Notes In Computer Science 2022, 13434: 163-172. PMID: 38464686, PMCID: PMC10923180, DOI: 10.1007/978-3-031-16440-8_16.Peer-Reviewed Original ResearchConvolutional long short-term memory (ConvLSTM) layersLong short-term memory layersMotion estimation moduleShort-term memory layersDeep learning benchmarksEnhanced network performanceImage registration problemMotion correction frameworkMotion correctionU-NetNetwork performanceLearning benchmarksSimilarity measurementEstimation moduleRegistration problemGradient lossMemory layerLoss functionDynamic frameDynamic positron emission tomographyFitting errorSpatial alignmentSpatial misalignmentPatient motionModule
2019
Deep Learning based Respiratory Pattern Classification and Applications in PET/CT Motion Correction
Guo Y, Dvornek N, Lu Y, Tsai Y, Hamill J, Casey M, Liu C. Deep Learning based Respiratory Pattern Classification and Applications in PET/CT Motion Correction. 2019, 00: 1-5. DOI: 10.1109/nss/mic42101.2019.9059783.Peer-Reviewed Original ResearchDeep learningNeural networkMotion correction methodDeep neural networksDeep learning modelsHybrid neural networkConvolutional layersHigh prediction accuracyRecurrent layersGeneralization capabilityData preprocessingLearning modelPattern classificationRespiratory motionAnzai systemLoss functionLinear classifierPrediction accuracyIntra-gate motionRPM systemMotion correctionTumor detectionNetworkIrregular breathersCT images