2024
Disentangled multimodal brain MR image translation via transformer-based modality infuser
Cho J, Liu X, Xing F, Ouyang J, Fakhri G, Park J, Woo J. Disentangled multimodal brain MR image translation via transformer-based modality infuser. Progress In Biomedical Optics And Imaging 2024, 12926: 129262h-129262h-6. DOI: 10.1117/12.3006502.Peer-Reviewed Original ResearchConvolutional neural networkBrain tumor segmentation taskModality-specific featuresTumor segmentation taskImage translationAdversarial networkSegmentation taskSynthesis qualityBrain MR imagesNeural networkMR modalitiesAcquired imagesExperimental resultsNetworkGlobal relationshipsDisease diagnosisImagesEncodingBraTSDatasetFeaturesTaskMethodSuperiorityMR imaging
2023
Attenuation correction for PET imaging using conditional denoising diffusion probabilistic model
Dong Y, Jang S, Han P, Johnson K, Ma C, Fakhri G, Li Q, Gong K. Attenuation correction for PET imaging using conditional denoising diffusion probabilistic model. 2023, 00: 1-1. DOI: 10.1109/nssmicrtsd49126.2023.10338188.Peer-Reviewed Original ResearchDiffusion probabilistic modelGenerative adversarial networkConditional encodingAttenuation correctionDenoising diffusion probabilistic modelLow-level featuresProbabilistic modelAttenuation coefficientAdversarial networkExtract featuresPET/MR systemsEncodingPET acquisitionNovel methodDiffusion encodingMagnetic resonanceImagesPET imagingCorrectionMR imagingUNetAttenuationNetworkFeaturesResonancePET image denoising based on denoising diffusion probabilistic model
Gong K, Johnson K, El Fakhri G, Li Q, Pan T. PET image denoising based on denoising diffusion probabilistic model. European Journal Of Nuclear Medicine And Molecular Imaging 2023, 51: 358-368. PMID: 37787849, PMCID: PMC10958486, DOI: 10.1007/s00259-023-06417-8.Peer-Reviewed Original ResearchConceptsDenoising diffusion probabilistic modelPET image denoisingDiffusion probabilistic modelImage denoisingDenoising methodNonlocal meansNetwork inputGenerative adversarial networkData consistency constraintsProbabilistic modelLearning-based modelsAdversarial networkData distributionDenoisingRefinement stepsIterative refinementFlexible frameworkImage qualityPhysical degrading factorsUNetNetworkDatasetImagesInputNoise level
2022
Tagged-MRI Sequence to Audio Synthesis via Self Residual Attention Guided Heterogeneous Translator
Liu X, Xing F, Prince J, Zhuo J, Stone M, El Fakhri G, Woo J. Tagged-MRI Sequence to Audio Synthesis via Self Residual Attention Guided Heterogeneous Translator. Lecture Notes In Computer Science 2022, 13436: 376-386. PMID: 36820764, PMCID: PMC9942274, DOI: 10.1007/978-3-031-16446-0_36.Peer-Reviewed Original ResearchAudio waveformEnd-to-end deep learning frameworkAdversarial training approachDeep learning frameworkEnd-to-endTwo-dimensional spectrogramAdversarial networkIntermediate representationLearning frameworkResidual attentionDisentanglement strategyAudio synthesisDataset sizeImprove realismHeterogeneous representationsHeterogeneous translationAttentional strategiesTraining approachExperimental resultsMuscle deformationIntelligible speechMotor control theoriesTagged-MRIRelated-disordersSpeech acousticsCmri2spec: Cine MRI Sequence to Spectrogram Synthesis via A Pairwise Heterogeneous Translator
Liu X, Xing F, Stone M, Prince J, Kim J, El Fakhri G, Woo J. Cmri2spec: Cine MRI Sequence to Spectrogram Synthesis via A Pairwise Heterogeneous Translator. 2013 IEEE International Conference On Acoustics, Speech And Signal Processing 2022, 00: 1481-1485. PMID: 36212702, PMCID: PMC9544268, DOI: 10.1109/icassp43922.2022.9746381.Peer-Reviewed Original ResearchMultimodal representation learningAdversarial training approachCine MRI sequencesCNN encoderRepresentation learningConvolutional decoderAdversarial networkDataset sizeSequence encodingEnhance realismHeterogeneous translationSpeech wordsSynthesis frameworkTraining approachExperimental resultsEncodingSynthesis accuracySpectrogramFrameworkDecoding
2021
A cross-scanner and cross-tracer deep learning method for the recovery of standard-dose imaging quality from low-dose PET
Xue S, Guo R, Bohn K, Matzke J, Viscione M, Alberts I, Meng H, Sun C, Zhang M, Zhang M, Sznitman R, El Fakhri G, Rominger A, Li B, Shi K. A cross-scanner and cross-tracer deep learning method for the recovery of standard-dose imaging quality from low-dose PET. European Journal Of Nuclear Medicine And Molecular Imaging 2021, 49: 1843-1856. PMID: 34950968, PMCID: PMC9015984, DOI: 10.1007/s00259-021-05644-1.Peer-Reviewed Original ResearchConceptsStructural similarity index measurePET imagingGenerative adversarial networkNuclear medicine physiciansArtificial intelligenceLow-dose scansBaseline image qualityDose reductionConditional generative adversarial networkClinical imaging assessmentSimilarity index measureDiversity of clinical practiceDevelopment of AI technologyDeep learning developmentDose acquisitionImaging assessmentMedicine physiciansImage qualityResultsThe improvementPatientsClinical acceptanceClinical practiceClinical settingAdversarial networkLow-dose PET