Purpose: To propose and evaluate a new model-based reconstruction method for highly accelerated phase-contrast magnetic resonance imaging (PC-MRI) with sparse sampling.
Theory and methods: This work presents a new constrained reconstruction method based on low-rank and sparsity constraints to accelerate PC-MRI. More specifically, we formulate the image reconstruction problem into separate reconstructions of flow-reference image sequence and complex differences. We then utilize the joint partial separability and sparsity constraints to enable high quality reconstruction from highly undersampled (k,t)-space data. We further integrate the proposed method with ESPIRiT based parallel imaging model to effectively handle multichannel acquisition.
Results: The proposed method was evaluated with in vivo data acquired from both 2D and 3D PC flow imaging experiments, and compared with several state-of-the-art methods. Experimental results demonstrate that the proposed method leads to more accurate velocity reconstruction from highly undersampled (k,t)-space data, and particularly superior capability of capturing the peak velocity of blood flow. In terms of flow visualization, blood flow patterns obtained from the proposed reconstruction also exhibit better agreement with those obtained from the fully sampled reference.
Conclusion: The proposed method achieves improved accuracy over several state-of-the-art methods for velocity reconstruction with highly accelerated (k,t)-space data. Magn Reson Med 77:1036-1048, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
Keywords: complex differences; low-rank modeling; model-based reconstruction; parallel imaging; partial separability; phase-contrast MRI; sparsity.
© 2016 International Society for Magnetic Resonance in Medicine.