## 4 Results
The red and blue lines with stars indicate statistical significance (\(p<0.05\)) between results generated by pairs of estimators. In this comparison, pre- and post-STREAL were separate groups; hence, the statistical comparisons are between estimators, not pre- and post-STREAL. To further demonstrate/compare the quality of [PERSON]'s modulus reconstruction in conjunction with each of the displacement field estimators, Figure 4 shows images of reconstructed [PERSON]'s modulus in tissue mimicking phantoms. Reconstructed images are shown for each displacement estimator with and without STREAL enhancement. This figure also demonstrates that the AM2D estimator provides a high-quality reconstructed image as compared to the \"ground truth\" image, but at a very low computational cost. To evaluate the performance and effectiveness of the four displacement estimators in the context of quality of reconstructed [PERSON]'s modulus images, contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), and inclusion to background [PERSON]'s modulus ratios for each reconstructed image were computed, and results pertaining to the tissue mimicking phantom are summarized in Table 3. This table indicates that all estimators lead to high CNR and SNR for reconstructed [PERSON]'s modulus images, while using STREAL  
Figure 4: Reconstructed [PERSON]’s modulus images of a tissue mimicking phantom obtained with each displacement estimator with and without STREAL enhancement. Figure 3: An example of [PERSON]’s modulus image reconstruction obtained with synthetic displacement data generated by each displacement field estimator with and without STREAL enhancement. The “ground truth” reconstruction image was obtained by processing the ground truth displacement field using the iterative [PERSON]’s modulus reconstruction algorithm. enhancement with the AM2D and GLUE estimators leads to little change in the respective image CNR and SNR. The table shows that, among the estimators, OVERWIND consistently led to a substantial increase in the CNR and SNR of the reconstructed [PERSON]'s modulus images. The ground truth inclusion to background [PERSON]'s modulus ratio is 1.86. This indicates that the AM2D, GLUE, and SOUL displacement estimators lead to reasonably high accuracy, while the OVERWIND estimator shows poor performance in [PERSON]'s modulus reconstruction. Strain imaging is common for obtaining an approximate distribution of tissue stiffness. To evaluate the performance and effectiveness of the four displacement estimators in the context of strain image quality, axial and lateral strain fields pertaining to the phantoms' dataset were analyzed. Figure 5 illustrates axial strain images of the tissue mimicking phantom obtained with each displacement estimator, with and without STREAL enhancement. Table 4 presents results summary of corresponding CNR, SNR, and Young's modulus ratio values obtained for the phantom dataset using each displacement estimator with and without STREAL enhancement. The figure and table indicate that the AM2D estimator provides a reasonably good-quality axial strain image but at a very low computational cost compared to other estimators. The table also indicates that the CNR and SNR values of axial strain images were slightly improved through the application of STREAL enhancement. Furthermore, while there is further deviation from the true value, the Young's ratio values obtained from these images once again show that AM2D leads to the most accurate estimates. A similar analysis was performed to evaluate the quality of lateral strain images using each of the displacement estimators with and without STREAL enhancement. As an example, Figure 6 illustrates lateral strain images of the tissue mimicking phantom obtained with each displacement estimator, with and without STREAL enhancement. Table 5 presents a summary of the pertinent CNR and SNR values obtained for the phantom dataset using each displacement estimator with and without STREAL enhancement.