Introduction

Cine DENSE measures myocardial displacements by encoding tissue displacement into the signal phase. Displacement encoding frequencies (k_e) are selected to balance signal-to-noise ratio, displacement sensitivity, and artifact suppression [1], resulting in phase wrapping during systole. Spatiotemporal phase unwrapping is required to compute Lagrangian motion trajectories and strain [1, 2]. Phase unwrapping may be aided by delineating the myocardium using manually-defined contours [3]. A fully automatic phase unwrapping method would eliminate the need for user intervention, and a method was previously developed, however it identified many extramyocardial pixels as input into the displacement trajectory and strain calculations [2], increasing the computational burden. A further limitation of the previous algorithm was an inability to unwrap DENSE datasets with multiple cycles of wrap. We propose a phase unwrapping algorithm based on phase predictions using multiple pathways and region growing [4]. This approach may identify fewer extramyocardial pixels and successfully unwrap datasets with multiple cycles of phase wrap.

Methods

The proposed method utilizes multiple phase prediction pathways and consistency checking for region growing. Initially, a small myocardial region is automatically identified by applying principle component analysis to DENSE images. The initial region is selected at an early cardiac phase where phase wrapping has not occurred. A perimeter of potentially phase-wrapped pixels around the initial region is identified, and multiple spatiotemporal linear predictions from previously unwrapped pixels are used to predict the phases of the perimeter pixels (Fig 1(A)). Once an attempt is made to unwrap a pixel, reliability thresholds are applied to assess the attempt. The thresholds are based on the overall predicted phase (T_Prediction) and the variation of the multiple predictions (T_Deviation). If the attempt is deemed reliable, the pixel is included in the unwrapped region. The process continues, allowing the region to grow and including regions with reliable predictions and rejecting regions with unreliable predictions, i.e., noise. For 2D displacement encoding, the algorithm is simultaneously applied to both encoding directions so that the region grows identically for both dimensions. The prior automatic unwrapping algorithm [2] and our proposed algorithm were applied to 5 volunteer datasets acquired using 2D short-axis cine DENSE with localized signal generation and with k_e = 0.1 cyc/mm, in-plane pixel size of 2.3 × 2.3 mm², and 24-32 cardiac phases. To generate gold standard data for comparisons, the myocardium was manually contoured, phase unwrapping was applied to the segmented region, and the unwrapping results were manually inspected to ensure the absence of unwrapping errors. Optimal thresholds were determined by using the new algorithm with a range of thresholds and finding those that minimize the root mean square (RMS) error between the gold-standard data and the masked results from the proposed algorithm. To test the algorithm, additional datasets (n=3) were acquired with displacement encoding frequencies of 0.14, 0.18 and 0.22 cyc/mm, leading to increased degrees of phase wrapping. Displacement trajectories and principle shortening strain (PSS) were calculated using the proposed method and the gold standard method [3, 4].

Results

Fig 1(B) illustrates RMS error plots for all k_e values to identify the best reliability thresholds. The unsmooth RMS error function is due to the non-linearity of the phase-unwrapping operation. Optimal values of π /3 for T_Prediction and 7π /12 for T_Deviation were identified. Fig. 1(C, D) illustrate poor phase unwrapping where either T_Prediction and T_Deviation were too low and regions of myocardium were improperly excluded during region growing, or T_Prediction and T_Deviation were too high and regions of surrounding tissue and blood were improperly included in the growth region and caused unwrapping errors. Fig. 1(E) illustrates successful region definition and myocardial phase unwrapping using the optimal thresholds. Fig. 2 shows that the proposed algorithm excludes more extramyocardial pixels than the prior automatic method. Fig. 3 illustrates that the proposed algorithm successfully unwrapped datasets with multiple cycles of phase wrapping, in contrast to the prior algorithm. For different k_e values, Fig. 4 illustrates the decrease in incorrectly unwrapped frames using the proposed algorithm compared to the prior algorithm. Fig 5(A) shows an end-systolic PSS map automatically computed using the proposed method. Fig 5 (B-E) show the comparison of PSS computed automatically and using manually-delineated myocardial contours, and Fig 5(D, E) show the Bland-Altman and correlation plots comparing the two methods indicating a small bias (+0.007) and excellent correlation (R2=0.82).

Conclusion

Fully automatic phase unwrapping using multiple phase prediction pathways and region growing provides better delineation of myocardial tissue and more reliable phase unwrapping than prior automatic methods. The proposed method may facilitate fully automatic in-line strain mapping for DENSE in the future.

Acknowledgements

Research support from Siemens Healthineers

References

References: 1. Spottiswoode. IEEE TMI 2007; 26:15-30 2. Gilliam. IEEE TMI 2012; 31:1669-81. 3. Spottiswoode. MIA 2009; 13:105–115 4.Xu. IEEE TGRS 1999; 37:124-34