A smart-phone based portable ultrasound imaging system for point-of-care applications

Kim, Jin Hyun; Yeo, Sunmog; Kim, Min; Kye, Sang-Bum; Lee, Youngbae; Song, Tai-Kyong

doi:10.1109/cisp-bmei.2017.8302121

Cited by 4 publications

(5 citation statements)

References 8 publications

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“…In this paper, we present a series of techniques: interpolation filter, limited aperture size and dynamic power management to improve the efficiency of a fully dynamic ultrasound beamformer for a handhold realtime 128-channel ultrasound system, providing higher image resolution compared to the 32-channel [26] and 64-channel beamformer [32]. The proposed architecture is programmed and synthesized in a single Arria V FPGA using Verilog, allowing fully dynamic beamforming in a low-cost single FPGA implementation.…”

Section: Resultsmentioning

confidence: 99%

“…Our ultrasound imaging system, compared to prevailing 32-channel [26] and 64-channel ultrasound imaging system [32], outputs higher resolution images with the better use of FPGA area. An overview architecture block diagram of the developed N-channel power-efficient and space-efficient ultrasound imaging system is shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

“…Another widely used method is precomputing the delays. However, to overcome the impending memory limitation of FPGAs, only a pseudodynamic focusing technique is utilized where delay information is only updated for a pre-determined depth rather than for a pre-determined delay resolution [26]. Different articles introduced the digital beamformer design using a 5 MHz center frequency beamformer linear array implemented on FPGA as discussed by [27] and with a 50 MHz center frequency annular array by [28,29].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An FPGA Implementation of Resource-Optimized Dynamic Digital Beamformer for a Portable Ultrasound Imaging System

Zheng

Chirala

et al. 2018

Adv. sci. technol. eng. syst. j.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An FPGA Implementation of Resource-Optimized Dynamic Digital Beamformer for a Portable Ultrasound Imaging System

Zheng

Chirala

et al. 2018

Adv. sci. technol. eng. syst. j.

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“…To overcome this limitation, we propose a structure where the DLN is divided to implement ultrasound image reconstruction and DLN inference in real time. The proposed method is evaluated on a mobile device with a portable ultrasound system [3,24], and DLN models are implemented by transferring network coefficients trained in a PC workstation to a mobile device.…”

Section: Introductionmentioning

confidence: 99%

Reverse Scan Conversion and Efficient Deep Learning Network Architecture for Ultrasound Imaging on a Mobile Device

Lee

Kim²,

Lim³

et al. 2021

Sensors

Self Cite

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Point-of-care ultrasound (POCUS), realized by recent developments in portable ultrasound imaging systems for prompt diagnosis and treatment, has become a major tool in accidents or emergencies. Concomitantly, the number of untrained/unskilled staff not familiar with the operation of the ultrasound system for diagnosis is increasing. By providing an imaging guide to assist clinical decisions and support diagnosis, the risk brought by inexperienced users can be managed. Recently, deep learning has been employed to guide users in ultrasound scanning and diagnosis. However, in a cloud-based ultrasonic artificial intelligence system, the use of POCUS is limited due to information security, network integrity, and significant energy consumption. To address this, we propose (1) a structure that simultaneously provides ultrasound imaging and a mobile device-based ultrasound image guide using deep learning, and (2) a reverse scan conversion (RSC) method for building an ultrasound training dataset to increase the accuracy of the deep learning model. Experimental results show that the proposed structure can achieve ultrasound imaging and deep learning simultaneously at a maximum rate of 42.9 frames per second, and that the RSC method improves the image classification accuracy by more than 3%.

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“…It uses an 11.6-inch Tablet PC for display. In [7], a smartphone-based POCUS that includes a 128-element transducer, a 32-channel scanner module, and a single Xilinx Artix 7 FPGA was proposed. This system uses a commercially available smartphone for backend processing with the GPU and displays the results with an interactive graphical user interface (GUI).…”

Section: Introductionmentioning

confidence: 99%

An Angle Independent Depth Aware Fusion Beamforming Approach for Ultrafast Ultrasound Flow Imaging

Madhavanunni

Panicker

2021

2021 43rd Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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In this paper, we present a GPU-accelerated prototype implementation of a portable ultrasound imaging pipeline on an Nvidia CLARA AGX development kit. The raw data is acquired with nonsteered plane wave transmit using a programmable handheld open platform that supports 128-channel transmit and 64-channel receive. The received signals are transferred to the Nvidia CLARA AGX developer platform through a host system for accelerated imaging. GPU-accelerated implementation of the conventional delay and sum (DAS) beamformer along with two adaptive nonlinear beamformers and two Fourierbased techniques was performed. The feasibility of the complete pipeline and its imaging performance was evaluated with in-vitro phantom imaging experiments and the efcacy is demonstrated with preliminary in-vivo scans. The image quality quantied by the standard contrast and resolution metrics was comparable with that of the CPU implementation. The execution speed of the implemented beamformers was also investigated for different sizes of imaging grids and a signicant speedup as high as 180 times that of the CPU implementation was observed. Since the proposed pipeline involves Nvidia CLARA AGX, there is always the potential for easy incorporation of online/active learning approaches.

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A smart-phone based portable ultrasound imaging system for point-of-care applications

Cited by 4 publications

References 8 publications

An FPGA Implementation of Resource-Optimized Dynamic Digital Beamformer for a Portable Ultrasound Imaging System

An FPGA Implementation of Resource-Optimized Dynamic Digital Beamformer for a Portable Ultrasound Imaging System

Reverse Scan Conversion and Efficient Deep Learning Network Architecture for Ultrasound Imaging on a Mobile Device

An Angle Independent Depth Aware Fusion Beamforming Approach for Ultrafast Ultrasound Flow Imaging

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