Exhibit Hall | Forum 1
Purpose: Retrospective phase sorting of 4D-MRF data results in inconsistency of the accuracy of parametric maps. This study demonstrates a strategy to reduce the parametric maps’ error by utilizing sliding window (SW) reconstruction and inter-phase data sharing (IDS) before the low-rank projection of MRF data and dictionary for the subsequent iterative denoising.
Methods: The proposed strategy was tested on a single slice MRF data of a health volunteer acquired on a 3T scanner (GE Healthcare) with a 2D inversion-recovery FISP sequence and a total of 2000 frames. All parametric maps in this experiment were generated by low-rank back-projection (LBP) method. The accuracy and quality of parametric maps reconstructed by the SW+IDS+LBP, SW+LBP and LBP only strategy were evaluated by normalized root-mean-square error (NRMSE), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) index.
Results: The T1, T2, and PD maps generated by the proposed strategy (SW+IDS+LBP) have better accuracy and quality as compared to other methods. The quantitative results show that both T1, T2, and PD maps generated by SW+IDS+LBP method have lower NRMSE (T1: 0.176 ± 0.0242; T2: 0.147 ± 0.0213; PD: 0.0913 ± 0.0118), higher PSNR (T1: 53.8 ± 1.19; T2: 58.8 ± 1.33; PD: 20.8 ± 1.05) and higher SSIM (T1: 0.63 ± 0.0873; T2: 0.666 ± 0.0516; PD: 0.7 ± 0.0385) than that generated by SW+LBP, and LBP only methods. Meanwhile, the qualitative results suggested that the respiratory motion-resolved parametric maps retain high motion fidelity.
Conclusion: Both qualitative and quantitative results suggest that the proposed SW+IDS+LBP can reduce the parametric maps’ error induced by the data segmentation in the phase sorting process of 4D-MRF. The enhanced quality of the low-rank subspace images could potentially benefit the convergence of subsequent iterative reconstruction.
Funding Support, Disclosures, and Conflict of Interest: This work was supported by research funding from the GRF (15102219).