Department of Ultrasound, Fraunhofer Institute for Biomedical Engineering, 66280 Sulzbach, Germany.
Department of Urology, Faculty of Medicine, Universitätsklinikum Freiburg University of Freiburg, Hugstetter Str. 55, 79106 Freiburg, Germany.
Sensors (Basel). 2021 Sep 28;21(19):6481. doi: 10.3390/s21196481.
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.
我们开发了一种新的移动超声设备，用于无需用户交互的长期和自动化膀胱监测，该设备由 32 个发射和接收电子设备以及一个 32 元件相控阵 3MHz 换能器组成。该设备架构基于数据数字化，并快速传输到消费类电子产品（例如平板电脑）进行信号重建（例如，通过平面波合成算法）和进一步的图像处理。所有重建算法都在 GPU 中实现，允许实时重建和成像。通过分析分辨率、信噪比和对比度噪声比，对系统和波束形成算法在标准超声体模（CIRS 多用途超声体模）上的成像性能进行了评估。此外，还开发了基于 ML 的分割算法，并评估了它们对不同充盈水平的人体膀胱进行可靠分割的能力。使用 253 个 B 模式数据集对相应的 CNN 进行了训练，并对 20 个 B 模式图像进行了评估。给出了膀胱分割的定量和定性结果，并与手动分割获得的真实值进行了比较。
