A fractional filter-based beamformer architecture using postfiltering approach to minimize hardware complexity

Cho, Jeong; Lee, Jun-Young; Song, Jaeki; Kim, Younglok; Song, Tai-Kyong

doi:10.1109/tuffc.2007.354

Cited by 14 publications

(17 citation statements)

References 10 publications

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“…2) Multi-Beamforming Method Based on a Shared FIFO Block: In the PUS-SOC, an MBF method based on a shared FIFO block supporting single-, dual-, and quad-beamforming is implemented in a dynamic receive beamforming architecture based on post-fractional filtering [15], [21]. The MBF method can produce multiple scanlines simultaneously without reducing the beamforming frequency, unlike the conventional time-sharing MBF method [22].…”

Section: ) Pseudo-dynamic Delay Calculationmentioning

confidence: 99%

“…The RF data are fed into the dynamic receive beamformer (RxBF) module through the low-voltage differential signaling block (LVDS) in the PUS-SOC and sequentially stored in RF memory. The RxBF module provides up to 4 simultaneous scanlines; the module is based on a post-fractional delay filtering architecture composed of a shared first-in-first-out (FIFO) block, a selective summation block (SSB), and a fractional delay filtering block (FDF) [15]. The beamformed results are sent to the mid-processor, which performs direct current rejection filtering (DCR), data decimation, scanline accumulation (SLA), time-gain compensation (TGC), digital quadrature demodulation (DQD), and baseband code compression (BCC).…”

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confidence: 99%

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A System-on-Chip Solution for Point-of-Care Ultrasound Imaging Systems: Architecture and ASIC Implementation

Kang

Yoon

Lee

et al. 2016

IEEE Trans. Biomed. Circuits Syst.

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In this paper, we present a novel system-on-chip (SOC) solution for a portable ultrasound imaging system (PUS) for point-of-care applications. The PUS-SOC includes all of the signal processing modules (i.e., the transmit and dynamic receive beamformer modules, mid- and back-end processors, and color Doppler processors) as well as an efficient architecture for hardware-based imaging methods (e.g., dynamic delay calculation, multi-beamforming, and coded excitation and compression). The PUS-SOC was fabricated using a UMC 130-nm NAND process and has 16.8 GFLOPS of computing power with a total equivalent gate count of 12.1 million, which is comparable to a Pentium-4 CPU. The size and power consumption of the PUS-SOC are 27×27 mm(2) and 1.2 W, respectively. Based on the PUS-SOC, a prototype hand-held US imaging system was implemented. Phantom experiments demonstrated that the PUS-SOC can provide appropriate image quality for point-of-care applications with a compact PDA size ( 200×120×45 mm(3)) and 3 hours of battery life.

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Section: ) Pseudo-dynamic Delay Calculationmentioning

confidence: 99%

mentioning

confidence: 99%

A System-on-Chip Solution for Point-of-Care Ultrasound Imaging Systems: Architecture and ASIC Implementation

Kang

Yoon

Lee

et al. 2016

IEEE Trans. Biomed. Circuits Syst.

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“…On the other hand, in IBF-POST, block data are selected by coarse delay and accumulated to a block buffer memory according to fine delay (i.e., D=0, D=0.25, D=0.5 and D=0.75 where D is fine delay). Consequently, accumulated block data are filtered with a polyphase filter [11].…”

Section: ) Ibf-pre Vs Prbf-postmentioning

confidence: 99%

An effective beamforming algorithm for a GPU-based ultrasound imaging system

Kwon

Song

Bae

et al. 2012

2012 IEEE International Ultrasonics Symposium

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In this paper, four beamforming algorithms (i.e., interpolation and phase rotation with pre-and post-filtering, IBF-PRE, IBF-POST, PRBF-PRE and PRBF-POST, respectively) implemented on a high-performance graphicsprocessing unit (GPU) were presented. Each beamforming method was divided into two kernels consisting of various beamforming and mid-processing blocks and efficiently implemented on a NVIDIA's Computer Unified Device Architecture (CUDA) platform (GeForce GTX560 Ti, NVIDIA, Santa Clara, CA, USA). To evaluate the performance of each method, pre-beamformed radio-frequency (RF) data were captured by a commercial ultrasound machine equipped with a research package (G40, Siemens Healthcare, Mountain View, CA, USA) from a tissue mimicking phantom. The execution time for each beamforming algorithm was measured by using a time stamp produced by a CUDA timer. The IBF-PRE outperforms over other methods (i.e., IBF-POST, PRBF-PRE, PRBF-POST), in terms of execution time, i.e., 7.89 ms vs. 16.19 ms, 21.89 ms, and 10.62 ms, respectively. This result indicates that the IBF-PRE method is suitable for the fully software based ultrasound imaging system.

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“…2 shows the block diagram of the developed USI system which consists of the ASIC-US, Virtex-4 FPGA, DSP (TMS320C6416TGLZ) and Samsung mobile CPU (S3C6410XH). The ASIC-US performs transmit beamforming, low-voltage differential signaling (LVDS) receiver block, 32-channel receive beamforming, and mid/back-end processing with hardware-efficient and performance enhancing architecture and algorithms, e.g., post-fractional filter based receive beamforming architecture, time-sharing bilinear receive focusing delay interpolation, extended aperture, multi-access register based multi-beamforming, dynamic quadrature demodulation, and frequency compounding algorithm [9][10][11][12][13]. FPGA was used for interface between the ASIC-US and DSP, and real-time control for the USI system, e.g., sequence generation and CPU interface.…”

Section: Introductionmentioning

confidence: 99%

A Point-of-care diagnosis system for emergency ultrasound: Prototype system implementation

Yoon

Kang

Kim

et al. 2012

2012 IEEE International Ultrasonics Symposium

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In Point-of-care (POC) applications, the need of a diagnosis imaging tool (e.g., portable ultrasound) combined with a physiological monitoring device (e.g., echocardiogram) is rapidly growing since it is essential for effectively evaluating patient's condition. In this paper, a new POC diagnosis system is presented, which can simultaneously provide ultrasound imaging and vital biosignal information for immediate diagnosis on the urgent signs of internal injuries, e.g., hemoperitoneum, hemopericardium, hemothorax, and pneumothorax.

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A fractional filter-based beamformer architecture using postfiltering approach to minimize hardware complexity

Cited by 14 publications

References 10 publications

A System-on-Chip Solution for Point-of-Care Ultrasound Imaging Systems: Architecture and ASIC Implementation

A System-on-Chip Solution for Point-of-Care Ultrasound Imaging Systems: Architecture and ASIC Implementation

An effective beamforming algorithm for a GPU-based ultrasound imaging system

A Point-of-care diagnosis system for emergency ultrasound: Prototype system implementation

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