# Wearable 256-Element MUX-Based Linear Array Transducer for Monitoring of Deep Abdominal Muscles
## 4 Discussion
In applications in which tissue displacements occur in the time frame needed for a compound image (e.g., cardiac imaging), this would result in motion artifacts highlighting the need for faster data transfer. However, since SSE can be considered a static exercise, such motion artifacts did not play a role in the present work. The limited transfer bandwidth of the wireless data link implemented so far nevertheless induced a tradeoff between image rate and image quality, such that the USB link had to be used for ideal temporal sampling of the TrA thickness change in Figure 7.  
While the image resolution is only mildly affected by the number of transmit angles (Figure 5E), the SLL is improved by 10 dB when using 10 or more transmit angles. However, when it comes to these metrics, our system performed very similarly to the same array driven by a 256 ch system (where all elements are addressed individually without the need for multiplexing). Contrast metrics such as the CR and the CNR showed a larger difference between the MUX-based and the fully populated version in the order of 10 and 5 dB (for CR and CNR, respectively). The largest difference in CR is seen in the most shallow lesion, which we assume is due to the fact that elements are addressed with identical transmit delays in groups of four, potentially resulting in a non-homogeneous near-field wave-front. On the other hand, the contrast drops for the deepest lesion, which, to our understanding, is due to the lower transmit pressure when compared with the fully populated array. Our work focused on a proof of concept of using a large element count array based on a MUX scheme together with a portable electronics system as biofeedback in SSE. Although we could show that motion and deformation of deep abdominal muscles could be monitored with our setup, our work is just a first step in view of more widespread clinical or even consumer use. To achieve these goals, different challenges must be tackled. As already mentioned, the current implementation of the wireless data link represents a bottleneck, which, however, can be overcome in three ways. First, the system is currently used with WLAN in access point mode with a measured rate of 15 Mbit/s. To optimize the transmission speed, the Ad-Hoc mode could be implemented, which would result in 21 Mbit/s. Second, by adding a mini PCIe interface for the Wi-Fi link, the data rate could easily be increased by a factor of 10 or more, and thus significantly improve the temporal resolution at the highest image quality. Finally, the system FPGA is prepared for onboard beamforming and gray-scale conversion. Currently, one image (in 11 angles PWC mode, 5 cm depth, 25 MSPS) requires 11 \(\times\) 4 \(\times\) 32 \(\times\) 1666 = 2.3 \(\times\) 10\({}^{6}\) samples of 12-bit. Incontrast, a reconstructed and gray-scaled data set would be 512 \(\times\) 512 samples of 8-bit, thus dividing the amount of data by 13. Onboard implementation of feature extraction, such as in the proposed application, could further reduce the amount of data to be transferred and thereby open the door to even higher temporal resolution. Next, fully automated analysis and segmentation of ultrasound data are needed so that metrics relating to the training success, which are easily understood by non-experts (in contrast to B-mode images whose understanding requires intense training), are made available. Finally, widespread use anticipates that the system fulfills regulatory requirements such as standards defined by the MDR (medical device directive). This can be considered a minor hurdle since the system has been designed according to (and partially testing for compliance with) specific aspects of IEC 60601-1 (electrical safety). Furthermore, the implemented B-mode scheme based on plane waves leads to very low-pressure levels, such that the device output is in compliance with the thresholds defined by IEC 60601-2-37 (acoustic safety).