Abstract
Software-based ultrasound imaging systems provide high flexibility that allows easy and fast adoption of newly developed algorithms. However, the extremely high data rate required for data transfer from sensors (e.g., transducers) to the ultrasound imaging systems is a major bottleneck in the software-based architecture, especially in the context of real-time imaging. To overcome this limitation, in this paper, we present a Binary cLuster (BL) code, which yields an improved compression ratio compared to the exponential Golomb code. Owing to the real-time encoding/decoding features without overheads, the universal code is a good solution to reduce the data transfer rate for software-based ultrasound imaging. The performance of the proposed method was evaluated using in vitro and in vivo data sets. It was demonstrated that the BL-beta code has a good stable lossless compression performance of 20%~30% while requiring no auxiliary memory or storage.
Highlights
Conventional ultrasound imaging systems are based on special-purpose hardware such as application-specific integrated circuits (ASICs) and field-programmable gate arrays (FPGAs) [1,2,3].These fixed-function chips can meet high data transfer rates and computation requirements for real-time ultrasound imaging
The high computational demands can be alleviated by using graphic processing units (GPUs), the large amount of data transfer poses an additional problem that hinders the practical implementation of software-based architecture in ultrasound imaging systems
We propose a Binary cLuster (BL) code-based ultrasound signal compression method for real-time software-based portable medical ultrasound imaging systems
Summary
Conventional ultrasound imaging systems are based on special-purpose hardware such as application-specific integrated circuits (ASICs) and field-programmable gate arrays (FPGAs) [1,2,3] These fixed-function chips can meet high data transfer rates and computation requirements for real-time ultrasound imaging. Many groups have developed several platforms of ultrasound imaging using programmable processors with high flexibility, which enable rapid prototyping and allow new applications to run on the same imaging platforms [4,5] These ultrasound imaging systems generally employ a PC (personal computer) as the imaging host and graphic processing units (GPUs) or digital signal processors (DSPs) to support the higher computational complexity [6,7]. A commercially available ultrasound imaging system (i.e., Vantage, Verasonics Inc., Kirkland, WA, USA) can support a data transfer rate up to 6.6 GB/s via Sensors 2018, 18, 3314; doi:10.3390/s18103314 www.mdpi.com/journal/sensors
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