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
2010
Joint design of spoke trajectories and RF pulses for parallel excitation
Ma C, Xu D, King K, Liang Z. Joint design of spoke trajectories and RF pulses for parallel excitation. Magnetic Resonance In Medicine 2010, 65: 973-985. PMID: 21413061, DOI: 10.1002/mrm.22676.Peer-Reviewed Original ResearchConceptsJoint design problemExcitation errorsTransmitting sensitivityJoint designCost function evaluationsExcitation systemDesign problemRF pulse designSelection problemMultidimensional RF pulsesExperimental resultsComputational efficiencyRF pulsesPulse designThin slice selectionEquation simulationsBloch equation simulationsSequential selectionConventional methodsSpoke locationExcitation patternsK-spacePulseFunction evaluationsLength limit