PURPOSE: To implement a regularization method for the phase-constrained generalized partially parallel acquisitions (GRAPPA) algorithm to reduce image artifacts caused by data inconsistencies. METHODS: Phase-constrained GRAPPA reconstructions are implemented through the use of virtual coils. To that end, synthetic virtual coils are generated by using complex conjugate symmetric signals from the actual coils. Regularization is achieved by applying coefficient-based penalty factors during the GRAPPA calibration procedure. Different penalizing factors are applied for the actual and virtual coils. The method is tested in vivo using T2-weighted turbo spin echo (TSE) images. RESULTS: T2 signal decay perturbs conjugate k-space symmetry and produces artifacts in phase-constrained parallel MRI reconstructions of T2-weighted TSE images. Using the proposed regularization method, artifacts are suppressed at the cost of noise amplification. However, there is still a significant SNR gain compared with conventional GRAPPA. CONCLUSION: The proposed regularization method is an efficient approach for artifact suppression and maintains the SNR benefit of phase-constrained parallel MRI over conventional parallel MRI.
PURPOSE: To implement a regularization method for the phase-constrained generalized partially parallel acquisitions (GRAPPA) algorithm to reduce image artifacts caused by data inconsistencies. METHODS: Phase-constrained GRAPPA reconstructions are implemented through the use of virtual coils. To that end, synthetic virtual coils are generated by using complex conjugate symmetric signals from the actual coils. Regularization is achieved by applying coefficient-based penalty factors during the GRAPPA calibration procedure. Different penalizing factors are applied for the actual and virtual coils. The method is tested in vivo using T2-weighted turbo spin echo (TSE) images. RESULTS: T2 signal decay perturbs conjugate k-space symmetry and produces artifacts in phase-constrained parallel MRI reconstructions of T2-weighted TSE images. Using the proposed regularization method, artifacts are suppressed at the cost of noise amplification. However, there is still a significant SNR gain compared with conventional GRAPPA. CONCLUSION: The proposed regularization method is an efficient approach for artifact suppression and maintains the SNR benefit of phase-constrained parallel MRI over conventional parallel MRI.
Authors: Berkin Bilgic; Itthi Chatnuntawech; Mary Kate Manhard; Qiyuan Tian; Congyu Liao; Siddharth S Iyer; Stephen F Cauley; Susie Y Huang; Jonathan R Polimeni; Lawrence L Wald; Kawin Setsompop Journal: Magn Reson Med Date: 2019-05-20 Impact factor: 4.668
Authors: Guobin Li; Jürgen Hennig; Esther Raithel; Martin Büchert; Dominik Paul; Jan G Korvink; Maxim Zaitsev Journal: MAGMA Date: 2015-02-26 Impact factor: 2.310
Authors: Suhyung Park; Liyong Chen; Jennifer Townsend; Hyunyeol Lee; David A Feinberg Journal: IEEE Trans Med Imaging Date: 2019-08-12 Impact factor: 10.048
Authors: Congyu Liao; Jason Stockmann; Qiyuan Tian; Berkin Bilgic; Nicolas S Arango; Mary Kate Manhard; Susie Y Huang; William A Grissom; Lawrence L Wald; Kawin Setsompop Journal: Magn Reson Med Date: 2019-08-01 Impact factor: 4.668