Deep-learning-based methods of attenuation correction for SPECT and PET
Chen X, Liu C. Deep-learning-based methods of attenuation correction for SPECT and PET. Journal Of Nuclear Cardiology 2022, 30: 1859-1878. PMID: 35680755, DOI: 10.1007/s12350-022-03007-3.Peer-Reviewed Original ResearchConceptsHigh computational complexityAC strategyNeural networkRaw emission dataComputational complexityLearning methodsCT imagesΜ-mapsPET imagesLow accuracySuperior performanceImagesAttenuation correctionPromising resultsMR imagesAttenuation mapPET/CT scannerHigh noise levelsArtifactsNetworkCT artifactsPET/MRI scannerIntermediate stepComplexityScannerPET respiratory motion correction: quo vadis?
Lamare F, Bousse A, Thielemans K, Liu C, Merlin T, Fayad H, Visvikis D. PET respiratory motion correction: quo vadis? Physics In Medicine And Biology 2022, 67: 03tr02. PMID: 34915465, DOI: 10.1088/1361-6560/ac43fc.Peer-Reviewed Original ResearchConceptsRespiratory motion correctionPET respiratory motion correctionMotion correctionGeneric motion modelImage reconstruction processRespiratory motion informationMotion estimationMotion informationTerms of synchronizationImage spaceSynchronization stepsReconstruction processMotion modelOverall approachPET/magnetic resonance imagingDevice systemRespiratory motionImaging devicesMRI deviceDevicesSynchronizationNumber of stepsComprehensive coverageDatasetGreat interest