2022
Low-Dose Tau PET Imaging Based on Swin Restormer with Diagonally Scaled Self-Attention
Jang S, Lois C, Becker J, Thibault E, Li Y, Price J, Fakhri G, Li Q, Johnson K, Gong K. Low-Dose Tau PET Imaging Based on Swin Restormer with Diagonally Scaled Self-Attention. 2022, 00: 1-3. DOI: 10.1109/nss/mic44845.2022.10399169.Peer-Reviewed Original ResearchConvolutional neural networkSelf-attention mechanismSelf-attentionTransformer architectureComputer vision tasksLocal feature extractionLong-range informationVision tasksDenoising performanceSwin TransformerFeature extractionImage datasetsUNet structureNeural networkSwinComputational costReceptive fieldsImage qualityMap calculationNetwork structureArchitecturePET image qualityChannel dimensionsQuantitative evaluationDenoisingPrincipal Component Characterization of Deformation Variations Using Dynamic Imaging Atlases
Xing F, Jin R, Gilbert I, Fakhri G, Perry J, Sutton B, Woo J. Principal Component Characterization of Deformation Variations Using Dynamic Imaging Atlases. Proceedings Of The International Society For Magnetic Resonance In Medicine ... Scientific Meeting And Exhibition. 2022 DOI: 10.58530/2022/2849.Peer-Reviewed Original Research
2021
4D magnetic resonance imaging atlas construction using temporally aligned audio waveforms in speech
Xing F, Jin R, Gilbert I, Perry J, Sutton B, Liu X, Fakhri G, Shosted R, Woo J. 4D magnetic resonance imaging atlas construction using temporally aligned audio waveforms in speech. The Journal Of The Acoustical Society Of America 2021, 150: 3500-3508. PMID: 34852570, PMCID: PMC8580575, DOI: 10.1121/10.0007064.Peer-Reviewed Original ResearchConceptsAudio waveformTemporal domain informationMulti-subject dataAtlas constructionMutual information measureMR image datasetsImage datasetsTarget domainDomain informationPost-processing methodImage sequencesTemporal alignmentSpatiotemporal alignmentMatching patternsInformation measuresImage dataSquare errorAligned volumesAlignment mapOverall score increaseMR technologyCross-correlationDeformable registrationSpeechImages
2020
High-performance rapid MR parameter mapping using model-based deep adversarial learning
Liu F, Kijowski R, Feng L, El Fakhri G. High-performance rapid MR parameter mapping using model-based deep adversarial learning. Magnetic Resonance Imaging 2020, 74: 152-160. PMID: 32980503, PMCID: PMC7669737, DOI: 10.1016/j.mri.2020.09.021.Peer-Reviewed Original ResearchConceptsConvolutional neural networkMR parameter mappingAdversarial learningState-of-the-art reconstruction methodsEnd-to-end convolutional neural networkUndersampled k-space dataConvolutional neural network approachAdversarial learning approachState-of-the-artStructural similarity indexImage reconstruction frameworkEnd-to-endImage sharpnessData consistencyConventional reconstruction approachesReconstruction approachK-space dataImprove image sharpnessImage reconstruction approachEstimated parameter mapsImage sparsityTexture restorationNetwork trainingImage datasetsReconstruction performance