2024
Images with harder-to-reconstruct visual representations leave stronger memory traces
Lin Q, Li Z, Lafferty J, Yildirim I. Images with harder-to-reconstruct visual representations leave stronger memory traces. Nature Human Behaviour 2024, 8: 1309-1320. PMID: 38740989, DOI: 10.1038/s41562-024-01870-3.Peer-Reviewed Original ResearchReconstruction errorFeature embeddingScene imagesAdaptive modulationLevels of processing theoryReconstruction residualsStronger memory tracesPerception interfaceInterface perceptionMemory accuracyVisual representationInfluence memoryPerceptual processingMemory durabilityMemory tracesResponse latencyMemoryImagesErrorEmbeddingDatasetArchitectureRetrievalIntentional selectionReconstructionKernel-elastic autoencoder for molecular design
Li H, Shee Y, Allen B, Maschietto F, Morgunov A, Batista V. Kernel-elastic autoencoder for molecular design. PNAS Nexus 2024, 3: pgae168. PMID: 38689710, PMCID: PMC11059255, DOI: 10.1093/pnasnexus/pgae168.Peer-Reviewed Original ResearchMaximum mean discrepancyMean discrepancyTransformer architectureCondition generatorWeighted reconstructionTraining datasetGenerative modelGeneration approachDocking applicationsMolecular designAutoencoderAccurate reconstructionVAESpectrum of applicationsAutoDock VinaEnhanced performanceDesignDatasetArchitectureGeneration performanceBenchmarksApplicationsGlide scoreReconstructionGeneration behavior
2023
Dual-Domain Iterative Network with Adaptive Data Consistency for Joint Denoising and Few-Angle Reconstruction of Low-Dose Cardiac SPECT
Chen X, Zhou B, Xie H, Guo X, Liu Q, Sinusas A, Liu C. Dual-Domain Iterative Network with Adaptive Data Consistency for Joint Denoising and Few-Angle Reconstruction of Low-Dose Cardiac SPECT. Lecture Notes In Computer Science 2023, 14307: 49-59. DOI: 10.1007/978-3-031-44917-8_5.Peer-Reviewed Original ResearchIterative networkAuxiliary modulesJoint denoisingLow reconstruction accuracySource codeData consistencyNetwork performanceAblation studiesReconstruction accuracyCardiac SPECTConsistency moduleHardware expensePrediction accuracyAngle reconstructionNetworkDenoisingImage noiseAngle projectionsModuleADC moduleAccuracyReconstructionImagesMPI dataCodeSVD Compression for Nonlinear Encoding Imaging with Model-based Deep Learning Reconstruction
Zhang Z, Selvaganesan K, Ha Y, Sun C, Samardzija A, Sun H, Galiana G, Constable R. SVD Compression for Nonlinear Encoding Imaging with Model-based Deep Learning Reconstruction. Proceedings Of The International Society For Magnetic Resonance In Medicine ... Scientific Meeting And Exhibition. 2023 DOI: 10.58530/2023/0833.Peer-Reviewed Original ResearchDeep learning reconstructionModel-based networkLearning reconstructionEncoded imageGPU memoryRegularization termEncoding matrixGraph nodesGaussian noiseSimulated Gaussian noiseBloch-SiegertEncoding dimensionEncodingSVDModel partPhysical model partNetworkGPUMR scannerNonlinear caseNodesRedundancyReconstructionCompressionNoise3D printing for virtual surgical planning of nasoseptal flap skull‐base reconstruction: A proof‐of‐concept study
Kayastha D, Wiznia D, Manes R, Omay S, Khoury T, Rimmer R. 3D printing for virtual surgical planning of nasoseptal flap skull‐base reconstruction: A proof‐of‐concept study. International Forum Of Allergy & Rhinology 2023, 13: 2073-2075. PMID: 37026426, DOI: 10.1002/alr.23165.Peer-Reviewed Original ResearchDSFormer: A Dual-domain Self-supervised Transformer for Accelerated Multi-contrast MRI Reconstruction
Zhou B, Dey N, Schlemper J, Salehi S, Liu C, Duncan J, Sofka M. DSFormer: A Dual-domain Self-supervised Transformer for Accelerated Multi-contrast MRI Reconstruction. 2023, 00: 4955-4964. DOI: 10.1109/wacv56688.2023.00494.Peer-Reviewed Original ResearchReconstruction networkSelf-supervised learning strategyHigh-fidelity reconstructionConvolutional architectureMRI reconstructionTraining dataFull supervisionInformation sharingTime costMulti-contrast MRINetworkLearning strategiesMultiple acquisitionsArchitectureSharingFine anatomical detailsRedundancyCostMultiple contrastsMRI sequencesReconstructionComplementary imaging modalities
2021
Whole-cell organelle segmentation in volume electron microscopy
Heinrich L, Bennett D, Ackerman D, Park W, Bogovic J, Eckstein N, Petruncio A, Clements J, Pang S, Xu CS, Funke J, Korff W, Hess HF, Lippincott-Schwartz J, Saalfeld S, Weigel AV. Whole-cell organelle segmentation in volume electron microscopy. Nature 2021, 599: 141-146. PMID: 34616042, DOI: 10.1038/s41586-021-03977-3.Peer-Reviewed Original ResearchConceptsAutomatic reconstructionDeep learning architectureLearning architectureWeb repositoriesOpen dataAutomatic methodThree-dimensional reconstructionSuch methodsVolume electron microscopyQueriesSegmentationRepositoryArchitectureComputer codeSpatial interactionsDatasetReconstructionImagesMetricsCodeSuch reconstructionsVirtual Surgical Planning for Intracranial Intraosseous Meningioma Reconstruction
Shah R, Lu X, Dinis J, Junn A, Alperovich M. Virtual Surgical Planning for Intracranial Intraosseous Meningioma Reconstruction. Journal Of Craniofacial Surgery 2021, 32: 2536-2538. PMID: 34224462, DOI: 10.1097/scs.0000000000007934.Peer-Reviewed Original ResearchConceptsVirtual surgical planningHigh-fidelity reconstructionMaterialise Mimics softwareComputed tomography scan imagesTemporal bone reconstructionReconstruction processMean discrepancySurgical planningScan imagesSoftwarePlanned reconstructionTomography scan imagesAccuracy assessmentThree-dimensional planningPostoperative orbital volumeMimics softwarePlanningThree-dimensional modelCustom implantsCutting guidesReconstructionImagesAccuracyBone reconstructionUnaffected orbit
2019
MAPEM-Net: an unrolled neural network for Fully 3D PET image reconstruction
Gong K, Wu D, Kim K, Yang J, Sun T, Fakhri G, Seo Y, Li Q. MAPEM-Net: an unrolled neural network for Fully 3D PET image reconstruction. Proceedings Of SPIE--the International Society For Optical Engineering 2019, 11072: 110720o-110720o-5. DOI: 10.1117/12.2534904.Peer-Reviewed Original ResearchPET image reconstructionNeural networkImage reconstructionImage denoising applicationDeep neural networksNeural network frameworkConvolutional neural networkDenoising applicationsDenoising methodNetwork frameworkUpdate stepData consistencyIll-posedNetworkClinical datasetsInverse problemMAPEMFrameworkAlgorithmDatasetDetected photonsReconstructionMethodSimulation
2018
Digital Design and 3D Printing of Aortic Arch Reconstruction in HLHS for Surgical Simulation and Training
Chen SA, Ong CS, Malguria N, Vricella LA, Garcia JR, Hibino N. Digital Design and 3D Printing of Aortic Arch Reconstruction in HLHS for Surgical Simulation and Training. World Journal For Pediatric And Congenital Heart Surgery 2018, 9: 454-458. PMID: 29945510, DOI: 10.1177/2150135118771323.Peer-Reviewed Original ResearchConceptsSurgical simulationDigital designDigital 3D modelsThree-dimensional modeling softwareComputer-aided designModeling softwareSegmentation softwareTraining modelScan imagesSoftwareTomography scan imagesLife-sized modelSurgical suturingAortic arch morphologyTechnologyDesignSurgical trainingSimulationsThree-dimensional printing technologyReconstructionImagesSegmented modelTrainingModelPrinting technology
2017
High‐resolution dynamic 31P‐MRSI using a low‐rank tensor model
Ma C, Clifford B, Liu Y, Gu Y, Lam F, Yu X, Liang Z. High‐resolution dynamic 31P‐MRSI using a low‐rank tensor model. Magnetic Resonance In Medicine 2017, 78: 419-428. PMID: 28556373, PMCID: PMC5562044, DOI: 10.1002/mrm.26762.Peer-Reviewed Original ResearchConceptsLow-rank tensorImage reconstructionHigh-resolution image reconstructionImage functionSubspace structureData acquisitionFrame-ratePursuit approachCorrelation of dataSubspaceK-space coverageK-spaceImagesSNRMathematical structureReconstructionHigh-resolutionModeling purposesIn vivo studiesMethodTensor
2015
Encoding and Decoding with Prior Knowledge: From SLIM to SPICE
Ma C, Lam F, Liang Z. Encoding and Decoding with Prior Knowledge: From SLIM to SPICE. 2015, 535-542. DOI: 10.1002/9780470034590.emrstm1441.Peer-Reviewed Original ResearchImage reconstructionLimited-data problemHigh-quality image reconstructionMagnetic resonance spectroscopic imaging methodBoundary informationSparsely sampled dataFourier encodingTruncated Fourier seriesEncodingData acquisitionSpectral localizationConventional magnetic resonance spectroscopic imagingFourier seriesImagesDecodingMagnetic resonance spectroscopic imagingFourierSubspaceSparsenessSpectroscopic imagingCodeDataMethodReconstructionSpicesThe outlook of physician histories: J. Marion Sims and ‘The Discovery of Anaesthesia’
Rosenbloom JM, Schonberger RB. The outlook of physician histories: J. Marion Sims and ‘The Discovery of Anaesthesia’. Medical Humanities 2015, 41: 102. PMID: 26048369, DOI: 10.1136/medhum-2015-010680.Peer-Reviewed Original Research
2014
Monte Carlo Simulations of the GE Discovery Alcyone CZT SPECT System
Ljungberg M, Liu C, Fan P, Pretorius P. Monte Carlo Simulations of the GE Discovery Alcyone CZT SPECT System. 2014, 1-3. DOI: 10.1109/nssmic.2014.7430823.Peer-Reviewed Original ResearchTof-Pet Ordered Subset Reconstruction Using Non-Uniform Separable Quadratic Surrogates Algorithm
Kim K, Ye J, Cheng L, Ying K, Fakhri G, Li Q. Tof-Pet Ordered Subset Reconstruction Using Non-Uniform Separable Quadratic Surrogates Algorithm. 2014, 963-966. DOI: 10.1109/isbi.2014.6868032.Peer-Reviewed Original ResearchSignal-to-noise ratioQuadratic surrogatesTOF-basedAlgorithm timeNoise ratioReconstruction algorithmReconstructed imagesAlgorithmConvergence rateImage qualityPET reconstructionTransmission reconstructionComputer simulationsTOF PET reconstructionTOF-PETEmission reconstructionAccurate imagingImagesSmall regionConvergenceComputerReconstructionNon-uniformityOSEMImproved Image Reconstruction for Subspace-Based Spectroscopic Imaging Using Non-Quadratic Regularization
Wu Z, Lam F, Ma C, Liang Z. Improved Image Reconstruction for Subspace-Based Spectroscopic Imaging Using Non-Quadratic Regularization. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2014, 2014: 2432-2435. PMID: 25570481, DOI: 10.1109/embc.2014.6944113.Peer-Reviewed Original ResearchConceptsImage reconstructionLow-rank modelNon-quadratic regularizationHigh-resolution metabolic imagingSparsely sampled datasetsCapabilities of SPICESPICE frameworkOptimization problemPrimal-dualNon-quadraticImagesSNRAlgorithmDatasetPhantom studySparsenessSpectroscopic imaging methodReconstructionSpectroscopic imagingOptimizationRegularizationMethodCapability
2011
IMPATIENT MRI: ILLINOIS MASSIVELY PARALLEL ACCELERATION TOOLKIT FOR IMAGE RECONSTRUCTION WITH ENHANCED THROUGHPUT IN MRI
Wu X, Gai J, Lam F, Fu M, Haldar J, Zhuo Y, Liang Z, Hwu W, Sutton B. IMPATIENT MRI: ILLINOIS MASSIVELY PARALLEL ACCELERATION TOOLKIT FOR IMAGE RECONSTRUCTION WITH ENHANCED THROUGHPUT IN MRI. 2011, 1: 69-72. DOI: 10.1109/isbi.2011.5872356.Peer-Reviewed Original ResearchEnhanced throughputImage reconstructionGraphics processing cardsMagnetic field inhomogeneityComputational powerNoisy dataComputing facilitiesComputation timeField inhomogeneityProcessing cardThroughputAcquisition trajectoriesMagnetic resonance imagingPhysical effectsToolkitImagesAdvanced techniquesClinical imagesTemporal resolutionReconstruction
2009
Integrating Sequencing Technologies in Personal Genomics: Optimal Low Cost Reconstruction of Structural Variants
Du J, Bjornson RD, Zhang ZD, Kong Y, Snyder M, Gerstein MB. Integrating Sequencing Technologies in Personal Genomics: Optimal Low Cost Reconstruction of Structural Variants. PLOS Computational Biology 2009, 5: e1000432. PMID: 19593373, PMCID: PMC2700963, DOI: 10.1371/journal.pcbi.1000432.Peer-Reviewed Original ResearchConceptsDifferent read lengthsDifferent technologiesSemi-realistic simulationComputational complexityMaximum accuracyAssembly algorithmReconstruction efficiencySimulation toolboxPersonal genomicsAccurate detectionLow costChallenging stepTechnologyCostAlgorithmAccurate assemblyComplexitySmall enough scalesReconstructionGoalIndividual genomesCanonical problemImportant goalToolboxSimulations
1988
Computers and Morphology
Braverman I. Computers and Morphology. JAMA Dermatology 1988, 124: 1415-1417. PMID: 3415287, DOI: 10.1001/archderm.1988.01670090071016.Peer-Reviewed Original Research
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