We developed a new mobile ultrasound device for long-term and automated bladder monitoring without user interaction consisting of 32 transmit and receive electronics as well as a 32-element phased array 3 MHz transducer. The device architecture is based on data digitization and rapid transfer to a consumer electronics device (e.g., a tablet) for signal reconstruction (e.g., by means of plane wave compounding algorithms) and further image processing. All reconstruction algorithms are implemented in the GPU, allowing real-time reconstruction and imaging. The system and the beamforming algorithms were evaluated with respect to the imaging performance on standard sonographical phantoms (CIRS multipurpose ultrasound phantom) by analyzing the resolution, the SNR and the CNR. Furthermore, ML-based segmentation algorithms were developed and assessed with respect to their ability to reliably segment human bladders with different filling levels. A corresponding CNN was trained with 253 B-mode data sets and 20 B-mode images were evaluated. The quantitative and qualitative results of the bladder segmentation are presented and compared to the ground truth obtained by manual segmentation.

Reliable acoustic coupling in a non-handheld mode and reducing the form factor of electronics are specific challenges in making ultrasound wearable. Applications relying on a large field of view (such as tracking of large muscles) induce a need for a large element count to achieve high image quality. In our work, we developed a 256-element linear array for imaging of abdominal muscles with four integrated custom-developed 8:32 multiplexer Integrated Circuits (ICs), allowing the array to be driven by our compact 32 ch electronics. The system is optimized for flexible use in R&D applications and allows adjustable transmit voltages (up to +/−100 V), arbitrary delay patterns, and 12-bit analog-to-digital conversion (ADC) with up to 50 MSPS and wireless (21.6 MBit/s) or USB link. Image metrics (SLL, FWHM) were very similar to a fully populated array driven with a 256 ch system. The contrast allowed imaging of lesions down to 7 cm in the phantom. In a first in-vivo study, we demonstrated reliable acoustic contact even during exercise and were able to visualize deep abdominal muscles such as the TrA. In combination with a muscle tracking algorithm, the change of thickness of the TrA during SSE could be monitored, demonstrating the potential of the approach as biofeedback for physiotherapy training.