2022
Unsupervised arterial spin labeling image superresolution via multiscale generative adversarial network
Cui J, Gong K, Han P, Liu H, Li Q. Unsupervised arterial spin labeling image superresolution via multiscale generative adversarial network. Medical Physics 2022, 49: 2373-2385. PMID: 35048390, DOI: 10.1002/mp.15468.Peer-Reviewed Original ResearchConceptsPeak signal-to-noise ratioStructural similarity indexNearest neighbor interpolationSignal-to-noise ratioTrilinear interpolationNeighbor interpolationAblation studiesB-spline interpolationLow-pass-filterLayer-by-layer trainingLoss termLow resolutionClearer structure boundariesLow signal-to-noise ratioIn vivo datasetsImage superresolutionGAN frameworkAdversarial networkB-spline interpolation methodReduce image noiseWeak labelsPrior informationSimilarity indexDipping methodSimulated data
2020
Super Resolution of Arterial Spin Labeling MR Imaging Using Unsupervised Multi-scale Generative Adversarial Network
Cui J, Gong K, Han P, Liu H, Li Q. Super Resolution of Arterial Spin Labeling MR Imaging Using Unsupervised Multi-scale Generative Adversarial Network. Lecture Notes In Computer Science 2020, 12436: 50-59. DOI: 10.1007/978-3-030-59861-7_6.Peer-Reviewed Original ResearchPeak signal-to-noise ratioGenerative adversarial networkStructural similarity indexMulti-scale generative adversarial networkSignal-to-noise ratioSuper-resolutionAdversarial networkHigher peak signal-to-noise ratioLow resolutionSuper-resolution methodsLow signal-to-noise ratioUnsupervised trainingPre-trainingWhole tissue volumeNoise interferenceGround truthSpline interpolation methodSimilarity indexConventional ASLNetworkImage noiseInterpolation methodLong acquisition timesHigh-resolutionStructural information
2019
Arterial spin labeling MR image denoising and reconstruction using unsupervised deep learning
Gong K, Han P, Fakhri G, Ma C, Li Q. Arterial spin labeling MR image denoising and reconstruction using unsupervised deep learning. NMR In Biomedicine 2019, 35: e4224. PMID: 31865615, PMCID: PMC7306418, DOI: 10.1002/nbm.4224.Peer-Reviewed Original ResearchConceptsSignal-to-noise ratioImage denoisingReconstruction frameworkDeep learning-based image denoisingDeep learning-based denoisersMR image denoisingLearning-based denoisingLow signal-to-noise ratioK-space dataNoisy imagesTraining labelsTraining pairsNetwork inputNeural networkDenoisingIn vivo experiment dataSuperior performanceImaging speedReconstruction processImage qualityLong imaging timesNetworkFrameworkImagesSpatial resolution