## 5 Discussion
It can be expected that systems with less than eight parallel beams will have a better image quality; however, due to their low impact on the frame rate they were not considered in this study. Furthermore, given that the inter-beam interference of the 12 MLT system was above the predefined threshold, it can be expected that adding more MLT beams will only result in worse image quality. In order to translate the proposed scan sequence to the clinical setting, several approaches could be envisioned. For example, one could consider a fixed conical volume based on a statistical analysis of what is likely to be encountered in a clinical setting, and define an optimal MLT sequence for this volume as described in the present study. However, given the large variability of ventricular size and shape, a fairly large FOV would be required implying that only a relatively moderate increase in frame rate would be achieved. A better alternative would be to ask the user to first identify the FOV in the pyramidal volume acquired using conventional imaging. This FOV could then be used to set up the fast MLT imaging sequence. Nevertheless, this manually delineated FOV adapted to the patients' anatomy would still remain relatively large to ensure that the myocardium remains inside during the full cardiac cycle. Evidently, as there is a time lag between finding the FOV and defining the imaging scan sequence, the latter would not have to be done in real-time (but sufficiently fast) and different MLT setups could prospectively be linked to characteristic dimensions of the conical volume in a look-up table (LUT). Finally, a third--and preferred--alternative would be to use a dynamic FOV definition based on a fully automatic 3D ultrasound myocardial segmentation. These technologies are already available, e.g., [24; 25]. This would lead to an optimal, narrow FOV, and thus an optimal frame rate gain. Given the simplicity of defining the proposed coverage function (whose calculation can be parallelized in a straightforward manner), and given the possibility of real-time template matching [26], the anatomical FOV could be dynamically defined. Then, the best-fitting parallelized scan sequence can be looked up in a LUT which could--similar to the LUT used in the manual FOV definition--prospectively be defined for a set of predefined dynamics FOVs. Of note in this context is that MLT beamforming has recently been demonstrated to be possible in real time [23] and does not require off-line image reconstruction. Thus, the ideal workflow for anatomical cardiac imaging could be envisioned as:  
1. A pyramidal volume is acquired at a conventional frame rate (i.e., ~30 Hz). 2. Using any existing automatic real-time segmentation framework (e.g., BEAS [24] or real-time contour tracking library, RCTL [25]), the left ventricular myocardium is detected. 3. The coverage function and the ring-shaped template matching are applied to define an anatomical conically shaped FOV. 4. Based on (iii), a fast scanning sequence is automatically selected using a LUT giving the best combination of transmit and receive parallelization (i.e., MLT-MLA) to scan the detected region-of-interest. 5. The anatomical relevant space is scanned at high spatiotemporal resolution for subsequent motion analysis. The proposed approach offers approximately twice the frame rate of the pyramidal 3D MLT approach previously presented by our team [17], despite the fact that it uses less parallelization (i.e., 12 MLT-4 MLA vs. 16 MLT-4 MLA). This improvement is obviously due to the reduced FOV to the anatomical relevant space. However, the effective gain in frame rate can only be quantified after choosing the scan sequence based on the desired application as pointed out above. Please note that the presented methodology was defined for apical view acquisitions only. This is because most of the 3D cardiac kinematic analysis, such as motion and deformation imaging, require a volume scanned from the apex.