2025
Microbubble Contrast Agent Use During Invasive Coronary Microvascular Assessment
Bahl R, Mehta S, Seligman H, Rajkumar C, Gafore S, Sen S, Nijjer S, Al-Lamee R, Chamié D, Bandeira D, Zhou X, Shin M, Tang M, Petraco R. Microbubble Contrast Agent Use During Invasive Coronary Microvascular Assessment. Journal Of The Society For Cardiovascular Angiography & Interventions 2025, 103857. DOI: 10.1016/j.jscai.2025.103857.Peer-Reviewed Original ResearchSignal-to-noise ratioSignal qualityCoronary flow reserveDoppler signal qualityUltrasound microbubble contrast agentFlow velocityDoppler-derived coronary flow reserveAssessment of coronary flow velocityMicrovascular assessmentMicrobubble contrast agentsAdministration of SonoVueContrast agent useMicrovascular function assessmentCoronary flow velocityCFR measurementsContrast agentsDoppler signalsAgent useMicrobubble contrastFlow reserveCovert attention modulates the SSVEP in a paradigm suitable for infants and young children
Ganea N, Aslin R, Lewkowicz D. Covert attention modulates the SSVEP in a paradigm suitable for infants and young children. Attention, Perception, & Psychophysics 2025, 87: 2085-2104. PMID: 40474052, DOI: 10.3758/s13414-025-03097-4.Peer-Reviewed Original ResearchCovert attentionIntertrial coherenceMeasures of attentional modulationYoung childrenCentral stimulusShifts of covert attentionOvert attentionSignal-to-noise ratioResponse epochsVisual stimuliSSVEP responsesCentral gazeInfantsAttention modulePeripheral stimuliPeripheral visual fieldVisual gazeStimuliSignificant attenuationFlickering visual stimuliVisual fieldGazeParadigmPeripheral eventsChildrenSequential multi-slice imaging strategy using field-cycling
Parra F, Ha Y, Samardzija A, Sun H, Sun C, Gross R, Nixon T, Galiana G, Constable T. Sequential multi-slice imaging strategy using field-cycling. Proceedings Of The International Society For Magnetic Resonance In Medicine ... Scientific Meeting And Exhibition. 2025 DOI: 10.58530/2025/3358.Peer-Reviewed Original ResearchA Manifold Learning-based Approach for Denoising in Deuterium Metabolic Imaging
Chi D, Han P, De Feyter H, de Graaf R, Ma C. A Manifold Learning-based Approach for Denoising in Deuterium Metabolic Imaging. Proceedings Of The International Society For Magnetic Resonance In Medicine ... Scientific Meeting And Exhibition. 2025 DOI: 10.58530/2025/3083.Peer-Reviewed Original ResearchOn‐scalp magnetoencephalography based on optically pumped magnetometers to investigate temporal lobe epilepsy
Feys O, Wens V, Depondt C, Rikir E, Gaspard N, Van Paesschen W, Aeby A, Bodart O, Carrette E, Holmes N, Brookes M, Ferez M, Corvilain P, De Tiège X. On‐scalp magnetoencephalography based on optically pumped magnetometers to investigate temporal lobe epilepsy. Epilepsia 2025, 66: e142-e151. PMID: 40317508, DOI: 10.1111/epi.18439.Peer-Reviewed Original ResearchInterictal epileptiform dischargesOptically pumped magnetometersTemporal lobe epilepsyOn-scalp magnetoencephalographyLobe epilepsyRefractory temporal lobe epilepsySignal-to-noise ratioInvestigate temporal lobe epilepsySurgical resection cavityExtratemporal lobe epilepsyOn-scalpResection cavityEpileptiform dischargesPatientsEpilepsyTemporal lobeMEG recordingsMagnetoencephalographyMagnetometerAmplitudeDeuterium Metabolic Imaging Denoising Using a Linear Tangent Space Alignment (LTSA) Model and Performance Analysis
Chi D, Han P, De Feyter H, de Graaf R, Ma C. Deuterium Metabolic Imaging Denoising Using a Linear Tangent Space Alignment (LTSA) Model and Performance Analysis. 2025, 00: 1-5. DOI: 10.1109/isbi60581.2025.10980807.Peer-Reviewed Original ResearchCramer-Rao Lower BoundSignal-to-noise ratioNumerical simulation studyCramer-RaoImage denoisingSpace alignmentHuman body non-invasivelyBody non-invasivelyLower BoundPerformance analysisIntrinsic low-dimensional manifold structureLow-dimensional manifold structureDeuterium metabolic imagingSimulation studyIRSRMamba: Infrared Image Super-Resolution via Mamba-Based Wavelet Transform Feature Modulation Model
Huang Y, Miyazaki T, Liu X, Omachi S. IRSRMamba: Infrared Image Super-Resolution via Mamba-Based Wavelet Transform Feature Modulation Model. IEEE Transactions On Geoscience And Remote Sensing 2025, 63: 1-16. DOI: 10.1109/tgrs.2025.3584385.Peer-Reviewed Original ResearchInfrared image super-resolutionPeak signal-to-noise ratioImage Super-ResolutionState-of-the-artStructural similarity indexSuper-ResolutionSemantic consistency lossIR image enhancementState-space modelGlobal‐local fusionConsistency lossSignal-to-noise ratioPerceptual qualityFeature moduleImage enhancementBlockwise processingTexture preservationSpatial consistencyRobust generalizationSimilarity indexFine detailsWaveletModulation modelArchitectureBlockwise
2024
Comparison of EEG Signal Characteristics of Subdural and Depth Electrodes
Alkawadri C, Yan Q, Kinal A, Spencer D, Alkawadri R. Comparison of EEG Signal Characteristics of Subdural and Depth Electrodes. Journal Of Clinical Neurophysiology 2024, 42: 391-399. PMID: 39787440, DOI: 10.1097/wnp.0000000000001139.Peer-Reviewed Original ResearchStereo-EEGHigh gamma bandSurgical epilepsy centersDrug-resistant epilepsyIntracranial EEG evaluationElectrode contact pairsImpact of electrode sizeElectrode sizeEffects of electrode sizeEEG evaluationCompare signal characteristicsHigh-frequency signalsEpilepsy centersSignal-to-noise ratioInterquartile rangeMedian correlation coefficientIntracranial EEGPower spectral densitySubdural electrodesPower ratioElectrode designFast-Fourier transformHigh gammaMedian distanceContact pairsNoise-aware dynamic image denoising and positron range correction for Rubidium-82 cardiac PET imaging via self-supervision
Xie H, Guo L, Velo A, Liu Z, Liu Q, Guo X, Zhou B, Chen X, Tsai Y, Miao T, Xia M, Liu Y, Armstrong I, Wang G, Carson R, Sinusas A, Liu C. Noise-aware dynamic image denoising and positron range correction for Rubidium-82 cardiac PET imaging via self-supervision. Medical Image Analysis 2024, 100: 103391. PMID: 39579623, PMCID: PMC11647511, DOI: 10.1016/j.media.2024.103391.Peer-Reviewed Original ResearchImage denoisingPositron range correctionDynamic framesSelf-supervised methodsSuperior visual qualityLow signal-to-noise ratioCardiac PET imagingDenoising methodSignal-to-noise ratioSelf-supervisionVisual qualityHigh-energy positronsRange correctionsDenoisingNoise levelImage spatial resolutionImage qualityDefect contrastPET imagingImage quantificationRadioactive isotopesPatient scansQuantitative accuracyImagesFrameMultimodality Molecular Imaging of Brain Tumor Using Simultaneous [18F]FET-PET/MRSI
Ma C, Han P, Marin T, Zhuo Y, Shih H, Fakhri G. Multimodality Molecular Imaging of Brain Tumor Using Simultaneous [18F]FET-PET/MRSI. 2024, 00: 1-2. DOI: 10.1109/nss/mic/rtsd57108.2024.10656528.Peer-Reviewed Original ResearchList-mode dataMR spectroscopic imagingSpatial resolutionAccurate brain tumor delineationMR physicsIsotropic resolutionBrain tumor delineationImprove treatment planningSpectroscopic imagingTumor delineationSignal-to-noise ratioIntact blood-brain barrierImaging speedAmino acid radiotracerImaging timeMR signalHigher proliferation activityStructural MRTreatment planningBlood-brain barrierMR spectroscopic imaging dataMolecular imaging of brain tumorsTumor involvementTumor infiltrationTumor marginsREliable PIcking by Consensus (REPIC): a consensus methodology for harnessing multiple cryo-EM particle pickers
Cameron C, Seager S, Sigworth F, Tagare H, Gerstein M. REliable PIcking by Consensus (REPIC): a consensus methodology for harnessing multiple cryo-EM particle pickers. Communications Biology 2024, 7: 1421. PMID: 39482410, PMCID: PMC11528043, DOI: 10.1038/s42003-024-07045-0.Peer-Reviewed Original ResearchConceptsCryo-EM usersParticle identificationParticle pickingLow signal-to-noise ratioAchievable resolutionSignal-to-noise ratioState-of-the-art computational algorithmsInteger linear programmingParticle setManual interventionHigh-quality particlesGraph problemsParticle locationMultiple pickersParticlesHeteroscedastic Uncertainty Estimation Framework for Unsupervised Registration
Zhang X, Pak D, Ahn S, Li X, You C, Staib L, Sinusas A, Wong A, Duncan J. Heteroscedastic Uncertainty Estimation Framework for Unsupervised Registration. Lecture Notes In Computer Science 2024, 15002: 651-661. DOI: 10.1007/978-3-031-72069-7_61.Peer-Reviewed Original ResearchUnsupervised registrationReal-world medical imagesCollaborative training strategyMedical image datasetsDeep learning methodsAccurate displacement estimationSignal-to-noise ratioImage datasetsRegistration architectureLearning methodsMedical imagesTraining strategyNoise distributionUncertainty estimationWeighting schemeRegistration performanceSpatial domainEstimation frameworkInput-dependentUncertainty estimation frameworkUniform noise levelsDisplacement estimationFrameworkNoise levelUnsupervisedRefining Biologically Inconsistent Segmentation Masks with Masked Autoencoders
Sauer A, Tian Y, Bewersdorf J, Rittscher J. Refining Biologically Inconsistent Segmentation Masks with Masked Autoencoders. 2008 IEEE Computer Society Conference On Computer Vision And Pattern Recognition Workshops 2024, 00: 6904-6912. PMID: 39669420, PMCID: PMC7617224, DOI: 10.1109/cvprw63382.2024.00684.Peer-Reviewed Original ResearchIndirect 1H–[13C] MRS of the human brain at 7 T using a 13C‐birdcage coil and eight transmit–receive 1H‐dipole antennas with a 32‐channel 1H‐receive array
Jacobs S, Prompers J, van der Kemp W, van der Velden T, Gosselink M, Meliadò E, Hoogduin H, Mason G, de Graaf R, Miller C, Bredael G, van der Kolk A, Alborahal C, Klomp D, Wiegers E. Indirect 1H–[13C] MRS of the human brain at 7 T using a 13C‐birdcage coil and eight transmit–receive 1H‐dipole antennas with a 32‐channel 1H‐receive array. NMR In Biomedicine 2024, 37: e5195. PMID: 38845018, DOI: 10.1002/nbm.5195.Peer-Reviewed Original ResearchStimulated echo acquisition modeSignal-to-noise ratioSlice-selective excitationAdiabatic selective refocusingUltra-high fieldIncreased spectral dispersionEcho acquisition modeSpectral dispersionVoxel positionSemi-localPhantomAcquisition modeElectromagnetic simulationsHuman brainSAR limitsExcitationBi-frontalSLASERHealthy volunteersSpectraSpach Transformer: Spatial and Channel-Wise Transformer Based on Local and Global Self-Attentions for PET Image Denoising
Jang S, Pan T, Li Y, Heidari P, Chen J, Li Q, Gong K. Spach Transformer: Spatial and Channel-Wise Transformer Based on Local and Global Self-Attentions for PET Image Denoising. IEEE Transactions On Medical Imaging 2024, 43: 2036-2049. PMID: 37995174, PMCID: PMC11111593, DOI: 10.1109/tmi.2023.3336237.Peer-Reviewed Original ResearchConceptsMulti-head self-attentionConvolutional neural networkSelf-attentionSignal-to-noise ratioState-of-the-art deep learning architecturesGlobal self-attentionState-of-the-artLocal feature extractionDeep learning architectureLow signal-to-noise ratioImage denoisingChannel informationChannel-wiseLearning architectureFeature extractionNeural networkTransformation frameworkComputational costReceptive fieldsImage qualityQuantitative meritDenoisingFrameworkQuantitative resultsDatasetModeling intra‐individual inter‐trial EEG response variability in autism
Dong M, Telesca D, Guindani M, Sugar C, Webb S, Jeste S, Dickinson A, Levin A, Shic F, Naples A, Faja S, Dawson G, McPartland J, Şentürk D. Modeling intra‐individual inter‐trial EEG response variability in autism. Statistics In Medicine 2024, 43: 3239-3263. PMID: 38822707, PMCID: PMC12096858, DOI: 10.1002/sim.10131.Peer-Reviewed Original ResearchComparing Recent Pulsar Timing Array Results on the Nanohertz Stochastic Gravitational-wave Background
Agazie G, Antoniadis J, Anumarlapudi A, Archibald A, Arumugam P, Arumugam S, Arzoumanian Z, Askew J, Babak S, Bagchi M, Bailes M, Nielsen A, Baker P, Bassa C, Bathula A, Bécsy B, Berthereau A, Bhat N, Blecha L, Bonetti M, Bortolas E, Brazier A, Brook P, Burgay M, Burke-Spolaor S, Burnette R, Caballero R, Cameron A, Case R, Chalumeau A, Champion D, Chanlaridis S, Charisi M, Chatterjee S, Chatziioannou K, Cheeseboro B, Chen S, Chen Z, Cognard I, Cohen T, Coles W, Cordes J, Cornish N, Crawford F, Cromartie H, Crowter K, Curyło M, Cutler C, Dai S, Dandapat S, Deb D, DeCesar M, DeGan D, Demorest P, Deng H, Desai S, Desvignes G, Dey L, Dhanda-Batra N, Di Marco V, Dolch T, Drachler B, Dwivedi C, Ellis J, Falxa M, Feng Y, Ferdman R, Ferrara E, Fiore W, Fonseca E, Franchini A, Freedman G, Gair J, Garver-Daniels N, Gentile P, Gersbach K, Glaser J, Good D, Goncharov B, Gopakumar A, Graikou E, Griessmeier J, Guillemot L, Gültekin K, Guo Y, Gupta Y, Grunthal K, Hazboun J, Hisano S, Hobbs G, Hourihane S, Hu H, Iraci F, Islo K, Izquierdo-Villalba D, Jang J, Jawor J, Janssen G, Jennings R, Jessner A, Johnson A, Jones M, Joshi B, Kaiser A, Kaplan D, Kapur A, Kareem F, Karuppusamy R, Keane E, Keith M, Kelley L, Kerr M, Key J, Kharbanda D, Kikunaga T, Klein T, Kolhe N, Kramer M, Krishnakumar M, Kulkarni A, Laal N, Lackeos K, Lam M, Lamb W, Larsen B, Lazio T, Lee K, Levin Y, Lewandowska N, Littenberg T, Liu K, Liu T, Liu Y, Lommen A, Lorimer D, Lower M, Luo J, Luo R, Lynch R, Lyne A, Ma C, Maan Y, Madison D, Main R, Manchester R, Mandow R, Mattson M, McEwen A, McKee J, McLaughlin M, McMann N, Meyers B, Meyers P, Mickaliger M, Miles M, Mingarelli C, Mitridate A, Natarajan P, Nathan R, Ng C, Nice D, Niţu I, Nobleson K, Ocker S, Olum K, Osłowski S, Paladi A, Parthasarathy A, Pennucci T, Perera B, Perrodin D, Petiteau A, Petrov P, Pol N, Porayko N, Possenti A, Prabu T, Leclere H, Radovan H, Rana P, Ransom S, Ray P, Reardon D, Rogers A, Romano J, Russell C, Samajdar A, Sanidas S, Sardesai S, Schmiedekamp A, Schmiedekamp C, Schmitz K, Schult L, Sesana A, Shaifullah G, Shannon R, Shapiro-Albert B, Siemens X, Simon J, Singha J, Siwek M, Speri L, Spiewak R, Srivastava A, Stairs I, Stappers B, Stinebring D, Stovall K, Sun J, Surnis M, Susarla S, Susobhanan A, Swiggum J, Takahashi K, Tarafdar P, Taylor J, Taylor S, Theureau G, Thrane E, Thyagarajan N, Tiburzi C, Toomey L, Turner J, Unal C, Vallisneri M, van der Wateren E, van Haasteren R, Vecchio A, Krishnan V, Verbiest J, Vigeland S, Wahl H, Wang S, Wang Q, Witt C, Wang J, Wang L, Wayt K, Wu Z, Young O, Zhang L, Zhang S, Zhu X, Zic A, Collaboration T. Comparing Recent Pulsar Timing Array Results on the Nanohertz Stochastic Gravitational-wave Background. The Astrophysical Journal 2024, 966: 105. DOI: 10.3847/1538-4357/ad36be.Peer-Reviewed Original ResearchInternational Pulsar Timing ArrayPulsar timing arraysGravitational-wave backgroundTiming arraysStochastic gravitational-wave backgroundPulsar timing array dataData Release 3Noise parametersRelease 3PulsarSignal-to-noise ratioNoise modelCombination of dataArrayStandard noise modelsAmplitudeParametersThe NANOGrav 15 yr Data Set: Search for Transverse Polarization Modes in the Gravitational-wave Background
Agazie G, Anumarlapudi A, Archibald A, Arzoumanian Z, Baier J, Baker P, Bécsy B, Blecha L, Brazier A, Brook P, Burke-Spolaor S, Burnette R, Case R, Casey-Clyde J, Charisi M, Chatterjee S, Cohen T, Cordes J, Cornish N, Crawford F, Cromartie H, Crowter K, DeCesar M, DeGan D, Demorest P, Dolch T, Drachler B, Ferrara E, Fiore W, Fonseca E, Freedman G, Garver-Daniels N, Gentile P, Glaser J, Good D, Gültekin K, Hazboun J, Jennings R, Johnson A, Jones M, Kaiser A, Kaplan D, Kelley L, Kerr M, Key J, Laal N, Lam M, Lamb W, Lazio T, Lewandowska N, Liu T, Lorimer D, Luo J, Lynch R, Ma C, Madison D, McEwen A, McKee J, McLaughlin M, McMann N, Meyers B, Mingarelli C, Mitridate A, Natarajan P, Ng C, Nice D, Ocker S, Olum K, Pennucci T, Perera B, Pol N, Radovan H, Ransom S, Ray P, Romano J, Saffer A, Sardesai S, Schmiedekamp A, Schmiedekamp C, Schmitz K, Shapiro-Albert B, Siemens X, Simon J, Siwek M, Stairs I, Stinebring D, Stovall K, Sun J, Susobhanan A, Swiggum J, Taylor J, Taylor S, Turner J, Unal C, Vallisneri M, Vigeland S, Wahl H, Witt C, Young O, Collaboration T. The NANOGrav 15 yr Data Set: Search for Transverse Polarization Modes in the Gravitational-wave Background. The Astrophysical Journal Letters 2024, 964: l14. DOI: 10.3847/2041-8213/ad2a51.Peer-Reviewed Original ResearchGravitational-wave backgroundTransverse polar modesMedian signal-to-noise ratioPolarization modesScalar transverseMetric theories of gravityTheory of gravitySignal-to-noise ratioST correlationsGravitational-waveGravitational wavesFactor ~2Spectral indexMetric theoryNANOGravHDCorrelation signaturesCorrelated signalsCross-correlationOptimal statisticsAmplitudeModeEstimated amplitudePolarizationGravityStatistical and Machine Learning Analysis in Brain-Imaging Genetics: A Review of Methods
Cheek C, Lindner P, Grigorenko E. Statistical and Machine Learning Analysis in Brain-Imaging Genetics: A Review of Methods. Behavior Genetics 2024, 54: 233-251. PMID: 38336922, DOI: 10.1007/s10519-024-10177-y.Peer-Reviewed Original Research
2023
Next generation PET imager
Pestotnik R, Gascón D, Gola A, Benlloch J, Alamo J, Barberá J, Dolenec R, Fernández-Tenllado J, Gómez S, Grogg K, Guberman D, Korpar S, Križan P, Majewski S, Manera R, Marin T, Mariscal-Castilla A, Mauricio J, Merzi S, Morera C, Orehar M, Pavon G, Penna M, Razdevšek G, Seljak A, Studen A, Fakhri G. Next generation PET imager. 2023, 00: 1-1. DOI: 10.1109/nssmicrtsd49126.2023.10338336.Peer-Reviewed Original ResearchPET imaging systemsReadout electronicsTime resolutionDetected scintillation photonsDual-sided readoutFast readout electronicsTime-of-flight measurementsLYSO crystal arrayFast-timing applicationsFlat-panel detectorTotal-body PET imagingScintillation photonsPET detectorsSilicon photomultipliersScintillation materialsXCAT phantomPET systemPanel detectorPhoto sensorCrystal arrayImaging systemTime spreadTiming precisionScintillationSignal-to-noise ratio
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