Potential of coded excitation in medical ultrasound imaging

Misaridis, T.; Gammelmark, Kim; Jørgensen, Christian H.; Lindberg, Niklas; Thomsen, Anders H.; Pedersen, Morten Høgholm; Jensen, Jørgen Arendt

doi:10.1016/s0041-624x(99)00130-4

Cited by 98 publications

(57 citation statements)

References 2 publications

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“…Summing them up results in a composite auto-correlation function with a peak of 2N and zero sidelobes [8]. A pair of bi-phase sequences A (a (n), n = 1,. .…”

Section: Golay Codementioning

confidence: 99%

Ultrasound elasticity imaging using Golay code

Peng

Tie

2017

BIO Web Conf.

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Abstract. Ultrasound elasticity imaging is becoming a new diagnostic tool for clinicians to detect lesions or cancers in tissues. In this paper, Golay code is applied to elasticity imaging to improve its quality. Phase-zero algorithm is employed as the displacement estimator, and the amplitude modulation correction location estimate method is used as the location estimator. We compared the performance of Golay code and the conventional short pulse in simulation method. The simulation results demonstrate that Golay code can achieve higher elastographic signal-to-noise ratio (SNRe) than the short pulse in low echo signal-to-noise ratio (eSNR) conditions, because the eSNR gain with Golay code increases the accuracy of the displacement estimates. However, in high eSNR conditions, Golay code performs worse than the short pulse, because the range sidelobe level of Golay code will decrease the SNRe and the performance of Golay code depends mainly on its range sidelobe level in high eSNR conditions. Therefore, the optimal conditions for Goaly code to be used in elasticity imaging are the low eSNR, great depth or high attenuation conditions.

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“…Summing them up results in a composite auto-correlation function with a peak of 2N and zero sidelobes [8]. A pair of bi-phase sequences A (a (n), n = 1,. .…”

Section: Golay Codementioning

confidence: 99%

Ultrasound elasticity imaging using Golay code

Peng

Tie

2017

BIO Web Conf.

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“…The transmitted signal must, thus, be changed from emission to emission and from element to element. The curves should be able to have arbitrary shape and should include long chirp signals [17], [18] as well as conventional sinusoidal signals. Again a precision of 12 bits should be used together with emission focusing and apodization.…”

Section: System Demandsmentioning

confidence: 99%

8A-3 System Architecture of an Experimental Synthetic Aperture Real-Time Ultrasound System

Jensen

Hansen

Tomov

et al. 2007

2007 IEEE Ultrasonics Symposium Proceedings

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Abstract-Synthetic Aperture (SA) ultrasound imaging has many advantages in terms of flexibility and accuracy. One of the major drawbacks is, however, that no system exists, which can implement SA imaging in real time due to the very high number of calculations amounting to roughly 1 billion complex focused samples per second per receive channel. Real time imaging is a key aspect in ultrasound, and to truly demonstrate the many advantages of SA imaging, a system usable in the clinic should be made. The paper describes a system capable of real time SA Bmode and vector flow imaging. The Synthetic Aperture Real-time Ultrasound System (SARUS) has been developed through the last 2 years and can perform real time SA imaging and storage of RF channel data for multiple seconds. SARUS consists of a 1024 channel analog front-end and 64 identical digital boards. Each has 16 transmit channels and 16 receive channels both with a sampling frequency of 70 MHz/12 bits for arbitrary waveform emission and reception. The board holds five Virtex 4FX100 FPGAs, where one houses a PowerPC CPU used for control. The remaining four are used for generation of transmit signals, receive storage and matched filter processing, and focusing and summing of data. Each FPGA can perform 80 billion multiplications/s and the full system can perform 25,600 billion multiplications/s. The FPGAs are connected through multiple 3.2 Gbit Rocket IO links, which makes it possible to send more than 1.6 GBytes/s of data between the FPGAs and between boards. The system can concurrently sample in 1024 channels, thus, generating 140 GBytes/s of data, which also can be processed in real time or stored. The system is controlled over a 1 Gbit/s Ethernet link to each digital board that runs Linux. The control and processing are divided into functional units that are accessed through an IP numbering scheme in a hierarchical order. A single controlling mechanism can, thus, be used to access the whole system from any PC. It is also possible for the controlling PowerPCs to access all other boards, which enables advanced adaptive imaging. The software is written in C++ and runs under Matlab for high level access to the system in a command structure similar to the Field II simulation program. This makes it possible for the user to specify imaging in very few lines of code and the set-up is fast due to the employment of the 64 PowerPCs in parallel. Focusing is done using a parametric beam former. Code synthesized for a Xilinx V4FX100 speed grade 11 FPGA can operate at a maximum clock frequency of 167.8 MHz producing 1 billion I and Q samples/second sufficient for real time SA imaging. The system is currently in production, and all boards have been laid out. VHDL and C++ code for the control has been written and the code for real time beamformation has been made and has obtained a sufficient performance for real-time imaging.

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“…The influence of an 8.5-MHz linear array transducer has been introduced, and the parameters for and are MHz, MHz, and s. As seen the temporal sidelobes are around 40 dB, which is not sufficient for medical imaging. To reduce the sidelobes, previous research has shown that this can effectively be done by applying amplitude weighting on the linear FM signal and compression filter [8], [19], [20]. In this study, a Tukey window with a 10% duration is applied on the linear FM signal, and a Chebychev window with 70 dB relative sidelobe attenuation is applied on the compression filter .…”

Section: B Temporal Encoding Using Linear Fm Signalsmentioning

confidence: 99%