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
Unsupervised inter-frame motion correction for whole-body dynamic PET using convolutional long short-term memory in a convolutional neural network
Guo X, Zhou B, Pigg D, Spottiswoode B, Casey ME, Liu C, Dvornek NC. Unsupervised inter-frame motion correction for whole-body dynamic PET using convolutional long short-term memory in a convolutional neural network. Medical Image Analysis 2022, 80: 102524. PMID: 35797734, PMCID: PMC10923189, DOI: 10.1016/j.media.2022.102524.Peer-Reviewed Original ResearchConceptsConvolutional neural networkNeural networkConvolutional long short-term memory (ConvLSTM) layersDeep learning-based frameworkConvolutional long short-term memoryLong short-term memory layersDeep learning baselinesLong short-term memoryDynamic temporal featuresLearning-based frameworkDeep learning approachShort-term memory layersTracer distribution changeMotion estimation networkMotion prediction errorInference timeEstimation networkLearning baselinesNon-rigid registration methodLearning approachMotion correction methodMemory layerShort-term memoryTemporal featuresRegistration method