Featured Publications
Cross-Attention for Improved Motion Correction in Brain PET
Cai Z, Zeng T, Lieffrig E, Zhang J, Chen F, Toyonaga T, You C, Xin J, Zheng N, Lu Y, Duncan J, Onofrey J. Cross-Attention for Improved Motion Correction in Brain PET. Lecture Notes In Computer Science 2023, 14312: 34-45. PMID: 38174216, PMCID: PMC10758996, DOI: 10.1007/978-3-031-44858-4_4.Peer-Reviewed Original ResearchDeep learning networkCross-attention mechanismDeep learning benchmarksMotion correctionTraining data domainPET list-mode dataPET image reconstructionQuality of reconstructionData domainCross attentionLearning networkSupervised mannerLearning benchmarksReference imageMotion trackingInherent informationList-mode dataImage reconstructionBrain PET dataPrediction resultsDifferent scannersHead motionImproved motion correctionNetworkSpatial correspondence
2022
An objective evaluation method for head motion estimation in PET—Motion corrected centroid-of-distribution
Sun C, Revilla EM, Zhang J, Fontaine K, Toyonaga T, Gallezot JD, Mulnix T, Onofrey JA, Carson RE, Lu Y. An objective evaluation method for head motion estimation in PET—Motion corrected centroid-of-distribution. NeuroImage 2022, 264: 119678. PMID: 36261057, DOI: 10.1016/j.neuroimage.2022.119678.Peer-Reviewed Original ResearchConceptsMotion informationHardware-based methodsHead motion estimationPET image reconstructionMotion estimation methodData-driven methodPET raw dataHead motionMask segmentationFinal image qualityMotion estimationTracking hardwareDifferent motion estimation methodsBrain PET studiesGround truthImage reconstructionRaw dataNew algorithmObjective quality controlInaccurate motion informationImage qualityMotion correction algorithmAlgorithmMotion errorsCorrection algorithm