Abstract

FDMAS has been successfully used in microwave imaging for breast cancer detection. FDMAS gained its popularity due to its capability to produce results faster than any other adaptive beamforming technique such as minimum variance (MV) which requires higher computational complexity. The average computational time for single point spread function (PSF) at 40 mm depth for FDMAS is 87 times faster than MV. The new beamforming technique has been tested on PSF and cyst phantoms experimentally with the ultrasound array research platform version 2 (UARP II) using a 3–8 MHz 128 element clinical transducer. FDMAS is able to improve both imaging contrast and spatial resolution as compared to DAS. The wire phantom main lobes lateral resolution improved in FDMAS by 40.4% with square pulse excitation signal when compared to DAS. Meanwhile the contrast ratio (CR) obtained for an anechoic cyst located at 15 mm depth for PWI with DAS and FDMAS are −6.2 dB and −14.9 dB respectively. The ability to reduce noise from off axis with auto-correlation operation in FDMAS pave the way to display the B-mode image with high dynamic range. However, the contrast to noise ratio (CNR) measured at same cyst location for FDMAS give less reading compared to DAS. Nevertheless, this drawback can be compensated by applying compound plane wave imaging (CPWI) technique on FDMAS. In overall the new FDMAS beamforming technique outperforms DAS in laboratory experiments by narrowing its main lobes and increases the image contrast without sacrificing its frame rates.

Highlights

  • Beamforming is a process of generating time delay that will be applied to a set of array at each time during transmission and reception

  • The B-Mode images of PWI on wire phantom formed with DAS and FDMAS beamforming techniques using square pulse excitation signals is shown in Fig. 2 at 40 dB dynamic range

  • A new type of non-liner beamforming technique known as FDMAS has been applied to PWI and compound plane wave imaging (CPWI) using square pulse excitation signals

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Summary

Introduction

Beamforming is a process of generating time delay that will be applied to a set of array at each time during transmission and reception. Apodization technique have been applied at transmitting and receiving stages Even though this technique able to reduce the noise and side lobes but as trade off it increase the main lobes size which directly reduce the image resolution. To overcome this issue, several new types of adaptive beamforming methods such as minimum variance and eigenvector minimum variance has been introduced [1]. These new beam formers able to dynamically change the receive aperture weights based on the received signals and able to increase the resolution and reduce side lobes but at expenses of high computational complexity and time. We have explore the potential of this new non-linear beamforming technique with PWI and CPWI

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