All entries for Bonnie Lam

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Analog Front-end Design for Portable Ultrasound Systems

• Authors: K. Chen, S. Lee, B. Lam, H.-S. Lee, A. P. Chandrakasan, C. G. Sodini
• Sponsorship: SRC/FCRP C2S2

The Capacitive Micromachined Ultrasound Transducer (CMUT) is an alternative to traditional piezoelectric transducers. The CMUT technology provides an opportunity for highly integrated ultrasound-imaging system solutions because of its CMOS compatibility, ease of large array fabrication, and improved bandwidth and sensitivity performance ^[1] .

This project aims to provide a highly flexible platform for 3D ultrasound imaging. Figure 1 presents the system architecture. The CMUT device is flip-chip bonded to the supporting electronic circuits, which eliminates the cables that are usually required by traditional systems between the piezoelectric transducers and circuits. As a result, the channel count of the imaging system is increased and the capacitive loading due to cables is greatly reduced.

The first prototype chip of the transmitter and receiver analog front-end for a 1D CMUT array is fabricated and is under testing. The block diagram of the implemented chip is shown in Figure 2. It contains four channels of independent transmitters and receiver chains. External control can be implemented for beamforming and Time-Gain Compensation (TGC). In each channel, the transmitter generates high voltage electric pulses to drive the CMUT device. A 3-level pulse shaping transmitter is designed to increase the transmitted signal power within the transducer bandwidth. The design uses MOS high voltage transistors for a pulse magnitude as large as 32 Vpp. The pulse frequency is programmable between 1~10 MHz and the pulse duration is programmable between about 0.5~20 us.

On the receiver side, a Low Noise Amplifier (LNA) implemented with a trans-impedance amplifier interfaces to the CMUT. The LNA is optimized for noise, power, and bandwidth trade-offs. The LNA can also be switched from “on” and “off” within 5 us. This switching saves system power when LNA is not needed. A Variable Gain Amplifier (VGA) follows the LNA to realize the Time-Gain Compensation function. Instead of a linear TGC profile, this VGA implements the TGC in a low power way, with discrete gain steps to compensate signal attenuation with coarse resolution. The VGA consumes 300 uA, and the gain setting can be changed in 6 dB per step with a tunable range of about 54 dB.

The prototyped chip is 3 mm X 3 mm in size. The simulated performance shows that each receive channel consumes 18.1 mW in normal mode and 1.7 mW in sleep mode. The programmable Rx gain range is from 152 dB to 99 dB at 3 MHz, with gain steps of 6 dB per step. The Rx Bandwidth is 6.0 MHz and the Rx Noise Figure is 11.3 dB within the signal bandwidth. The Tx pulsing energy efficiency is 38.2 nJ / pulse for an assumed 60 pF load from one CMUT element.



Figure 1: The architecture of a 3D ultrasound imaging system with CMUT devices.



Figure 2: The block diagram of the first prototype ultrasound analog front-end chip.




1. O. Oralkan, “Acoustical imaging using capacitive micromachined ultrasonic transducer arrays: Devices, circuits, and systems,” Ph.D. dissertation, Stanford, Palo Alto, 2004. [↩]

Digital Processing and Beamforming for Portable Medical Ultrasound Imaging

• Authors: B. Lam, K. Chen, S. Lee, C. G. Sodini, H.-S. Lee, A. P. Chandrakasan
• Sponsorship: Texas Instruments, SRC/FCRP C2S2

An ultrasound image is formed from a collection of ultrasonic beams transmitted and received by an array of transducer elements.  As the resolution of an image and the range over which an image is to be formed increase, so do the number of these transducer elements and the corresponding digital processing units.  The intensive signal processing power required for ultrasound imaging ^[1] means that conventional ultrasound systems are often large and expensive, and this demand for processing power can only worsen as more transducers and signal channels are implemented.  Three-dimensional (3D) ultrasound images, composed of a set of adjacently acquired two-dimensional images, are formed using two-dimensional (2D) transducer arrays ^[2] .  This further increases the signal processing requirement: if a one-dimensional (1D) array of N transducers costs a complexity of N, the corresponding 3D image would require a complexity of N².  Countering these trends is the movement to smaller form-factors for portable ultrasound systems that are less operator-dependent, warranting the use of advanced algorithms and architectures for back-end digital processing.  Figure 1 illustrates a simplified block diagram of a typical ultrasound system.

Beamforming, which in its simplest form involves delaying, scaling, and summing to produce a coherent signal from the collection of received beams, has been identified as an area for algorithmic research and development ^[3] .  Figure 2 shows a schematic describing the delay-and-sum operation.  This work explores advanced beamforming techniques with an efficient and low-power hardware implementation in mind. Specifically, synthetic apertures and adaptive beamforming are implemented to reduce the number of channels and the data rate required for image formation.  The power and area reduction in a low-power implementation of the beamformer are demonstrated.



Figure 1: Simplified block diagram of a typical ultrasound imaging system ^[4] . The blocks enclosed by the dotted box represent the backend digital processing.



Figure 2: Schematic representation of delay-and-sum beamforming on the receiving side of an ultrasound imaging system ^[5] .




1. M. Ali, D. Magee, and U. Dasgupta, “Signal processing overview of ultrasound systems for medical imaging,” Texas Instruments, Dallas, TX, SPRAB12, 2008. [↩]
2. D. Kouame, J. M. Gregoire, L. Pourcelot, J. M. Girault, M. Lethiecq, and F. Ossant, “Ultrasound imaging: signal acquisition, new advanced processing for biomedical and industrial applications,” in Proc. IEEE International Conf. on Acoustics, Speech, and Signal Processing, vol. 5, pp. 993-995, Mar. 2005. [↩]
3. S. Stergiopoulos, Advanced Signal Processing Handbook: Theory and Implementation for Radar, Sonar, and Medical Imaging Real-Time Systems.  Boca Raton: CRC Press, Inc., 2000. [↩]
4. J. U. Quistgaard, “Signal acquisition and processing in medical diagnostic ultrasound,” IEEE Signal Processing Magazine, vol. 14, no. 1, pp. 67-74, Jan. 1997. [↩]
5. M. L. Palmeri, “Ultrasound beamforming and image formation,” presented at Radiological Society of North America, 2007, p. 7 [↩]