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.

Volumetric ultrasound imaging is of great importance in many medical fields, especially in cardiology, but also in therapy monitoring applications. For development of new imaging technologies and scanning strategies, it is crucial to be able to use a hardware platform that is as free and flexible as possible and does not restrict the user in his research in any way. For this purpose, multi-channel ultrasound systems are particularly suitable, as they are able to control each individual element of a matrix array without the use of a multiplexer. We set out to develop a fully integrated, compact 1024-channel ultrasound system that provides full access to all transmission parameters and all digitized raw data of each transducer element. For this purpose, we synchronize four research scanners of our latest “DiPhAS” ultrasound research system generation, each with 256 parallel channels, all connected to a single PC on whose GPUs the entire signal processing is performed. All components of the system are housed in a compact, movable 19-inch rack. The system is designed as a general-purpose platform for research in volumetric imaging; however, the first-use case will be therapy monitoring by tracking radiation-sensitive ultrasound contrast agents.

The reliable assessment of muscle states, such as contracted muscles vs. non-contracted muscles or relaxed muscles vs. fatigue muscles, is crucial in many sports and rehabilitation scenarios, such as the assessment of therapeutic measures. The goal of this work was to deploy machine learning (ML) models based on one-dimensional (1-D) sonomyography (SMG) signals to facilitate low-cost and wearable ultrasound devices. One-dimensional SMG is a non-invasive technique using 1-D ultrasound radio-frequency signals to measure muscle states and has the advantage of being able to acquire information from deep soft tissue layers. To mimic real-life scenarios, we did not emphasize the acquisition of particularly distinct signals. The ML models exploited muscle contraction signals of eight volunteers and muscle fatigue signals of 21 volunteers. We evaluated them with different schemes on a variety of data types, such as unprocessed or processed raw signals and found that comparatively simple ML models, such as Support Vector Machines or Logistic Regression, yielded the best performance w.r.t. accuracy and evaluation time. We conclude that our framework for muscle contraction and muscle fatigue classifications is very well-suited to facilitate low-cost and wearable devices based on ML models using 1-D SMG.

X-ray imaging, based on ionizing radiation, can be used to determine bone age by examining distal growth plate fusion in the ulna and radius bones. Legal age determination approaches based on ultrasound signals exist but are unsuitable to reliably determine bone age. We present a low-cost, mobile system that uses one-dimensional ultrasound radio frequency signals to obtain a robust binary classifier enabling the determination of bone age from data of girls and women aged 9 to 24 years. These data were acquired as part of a clinical study conducted with 148 subjects. Our system detects the presence or absence of the epiphyseal plate by moving ultrasound array transducers along the forearm, measuring reflection and transmission signals. Even though classical digital signal processing methods did not achieve a robust classifier, we achieved an ${\mathrm{F}}_1$ score of approximately 87% for binary classification of completed bone growth with machine learning approaches, such as the gradient boosting machine method CatBoost. We demonstrate that our ultrasound system can classify the fusion of the distal growth plate of the radius bone and the completion of bone growth with high accuracy. We propose a non-ionizing alternative to established X-ray imaging methods for this purpose.

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.

Mobile and cost effective ultrasound devices are being used in point of care scenarios or the drama room. To reduce the costs of such devices we already presented the possibilities of consumer devices like the Apple iPad for full signal processing of raw data for ultrasound image generation. Using technologies like plane wave imaging to generate a full image with only one excitation/reception event the acquisition times and power consumption of ultrasound imaging can be reduced for low power mobile devices based on consumer electronics realizing the transition from FPGA or ASIC based beamforming into more flexible software beamforming. The massive parallel beamforming processing can be done with the Apple framework “Metal” for advanced graphics and general purpose GPU processing for the iOS platform. We were able to integrate the beamforming reconstruction into our mobile ultrasound processing application with imaging rates up to 70 Hz on iPad Air 2 hardware.