2024
A deep learning-based approach to nuisance signal removal from MRSI data aqcuired without suppression
Lee W, Zhuo Y, Marin T, Han P, Chi D, Fakhri G, Ma C. A deep learning-based approach to nuisance signal removal from MRSI data aqcuired without suppression. Proceedings Of The International Society For Magnetic Resonance In Medicine ... Scientific Meeting And Exhibition. 2024 DOI: 10.58530/2024/0259.Peer-Reviewed Original ResearchDeep learning-based methodsLearning-based methodsU-Net structureSignal removalIn vivo MRSI dataNeural networkU-NetMRSI dataImage reconstructionSuperior performanceData processingRobust performanceHankel matrixNetworkNuisance signalsConventional methodsPerformanceMRSI signalsSignalMethodRemove nuisance signalsRemovalHankel
2016
High‐resolution 1H‐MRSI of the brain using short‐TE SPICE
Ma C, Lam F, Ning Q, Johnson C, Liang Z. High‐resolution 1H‐MRSI of the brain using short‐TE SPICE. Magnetic Resonance In Medicine 2016, 77: 467-479. PMID: 26841000, PMCID: PMC5493212, DOI: 10.1002/mrm.26130.Peer-Reviewed Original ResearchConceptsSignal-to-noise ratioHigh-resolution spectroscopic imagingSpatiospectral correlationSpectroscopic imagingIn-plane resolutionSubspace-based techniquesAccelerated data acquisitionSignal processing algorithmsMetabolite signalsIn-planeProcessing algorithmsNuisance signalsLipid signalingBaseline signalDatasetData acquisitionProperties of water
2015
Removal of nuisance signals from limited and sparse 1H MRSI data using a union‐of‐subspaces model
Ma C, Lam F, Johnson C, Liang Z. Removal of nuisance signals from limited and sparse 1H MRSI data using a union‐of‐subspaces model. Magnetic Resonance In Medicine 2015, 75: 488-497. PMID: 25762370, PMCID: PMC4567537, DOI: 10.1002/mrm.25635.Peer-Reviewed Original Research