## 5 Discussion
Nonetheless, the idealistic nature of the tissue mimicking phantoms and indeed the in silico phantoms means that the quality of the axial displacement alone is enough to reconstruct the [PERSON]'s modulus with high quality. While this indicates that STREAL has left little room for improvement in the studied phantoms, the impacts of STREAL are best exemplified in the clinical cases where the prominence of the inclusion was increased since the noise in these cases is substantial in both axial and lateral directions. In the clinical cases presented in this investigation, the AM2D estimator provides [PERSON]'s modulus images with remarkable heterogeneity. While this is consistent with a heterogeneous stiffness distribution expected in tumor tissue, a lack of underlying ground truth makes this difficult to ascertain. The GLUE estimator provides a more homogenous-appearing mass, which may be more appealing to clinicians. Moreover, the OVERWIND and SOUL estimators do not appear to provide a significant benefit as compared to the GLUE estimator, which has a significantly higher runtime. It must be noted that the CNR/SNR and ratios of the clinical reconstruction should be interpreted cautiously as the underlying stiffness distribution and, more importantly, its heterogeneity are unknown; hence naive comparisons of SNR and CNR are not appropriate as the true variance in the background is not known. One important aspect of the AM2D estimator is its ability to create high quality strain images at high speed for real-time image assessment. This study indicates that the AM2D estimator with STREAL enhancement is vastly superior in its quality to runtime ratio, as can be seen in both the tissue mimicking phantom and clinical examples, where the inclusion or tumor is clearly visible. It is noteworthy that with all of the tissue motion tracking techniques using US, the quality of the estimated displacement field is highly dependent on the consistency of the selected pair of RF frames corresponding to the pre- and post-compression states of the tissue. In other words, the two frames must represent the same tissue plane as much as possible. The greater the deviation from this situation, the lower the quality of the estimated displacement field. The recently developed method known as PCA-GLUE [18], which is a variant of the GLUE method optimized with machine learning, can be used for assessing RF frame pair quality. Given the high speed of the AM2D displacement estimator, it should also be possible to consider it a good candidate for real-time frame pair quality assessment before near imaging at near real-time rates. This concept is demonstrated in the flowchart shown in Figure 17.  
Figure 17: A hypothetical flow chart for a real-time US elastography system that could be deployed in the clinical setting. The red box indicates a real-time loop that the clinician can use to assess the quality of images in real time, where once a sufficiently high-quality strain image is found, it can be processed to reconstruct reliable [PERSON]’s modulus image. This investigation clearly demonstrates the image quality superiority of full-reconstruction imaging, where it is observed that SNR and CNR values are substantially higher for the tissue mimicking phantom at the reconstructed [PERSON]'s modulus level compared to their strain image counterparts. Moreover, [PERSON]'s modulus images are much easier to interpret, as can be seen in Figures 7, 10 and 13, with an overlay indicating substantially stiffer tissue. Some limitations of this study include the small number of tissue mimicking and clinical examples and the lack of underlying validation for the stiffness field of the clinical examples. Some future suggestions for this work would be to develop better tissue-mimicking samples that include heterogeneity, whether synthetic or physical, to evaluate the preservation of heterogeneity in the sample. Another obvious future direction is including a large clinical study to achieve more concrete conclusions.