Adaptive Beamformer Combined with Phase Coherence Weighting Applied to Ultrafast Ultrasound

Abstract

Ultrafast ultrasound imaging is a promising technique for measurement of fast moving objects. In ultrafast ultrasound imaging, the high temporal resolution is realized at the expense of the lateral spatial resolution and image contrast. The lateral resolution and image contrast are important factors determining the quality of a B-mode image, and methods for improvements of the lateral resolution and contrast have been developed. In the present study, we focused on two signal processing techniques; one is an adaptive beamformer, and the other is the phase coherence factor (PCF). By weighting the output of the modified amplitude and phase estimation (mAPES) beamformer by the phase coherence factor, image quality was expected to be improved. In the present study, we investigated how to implement the PCF into the mAPES beamformer. In one of the two examined strategies, the PCF is estimated using element echo signals before application of the weight vector determined by the adaptive beamformer. In the other strategy, the PCF was evaluated from the element signals subjected to the mAPES beamformer weights. The performance of the proposed method was evaluated by the experiments using an ultrasonic imaging phantom. Using the proposed strategies, the lateral full widths at half maximum (FWHM) were both 0.288 mm, which was better than that of 0.348 mm obtained by the mAPES beamformer only. Also, the image contrasts realized by the mAPES beamformer with the PCFs estimated before and after application of the mAPES beamformer weights to the element signals were 5.61 dB and 5.32 dB, respectively, which were better than that of 5.14 dB obtained by the mAPES beamformer only.

Seismic Imaging Method for Medical Ultrasound Systems

“…The synergy with the synthetic aperture acquisition allows the target region to be illuminated from a wide range of incidence directions, giving rise to many overlapping partial images each containing the high-accuracy properties of the undulatory approach. As an alternative, keeping the three-step approach and its undulatory nature unchanged, nothing would prevent the adaptation of the source term of the forward problem to the steered emission of all transducer elements and the obtaining of a very fast plane-wave imaging tool [4,12,37], provided one accepts a lower resolution [38].…”

Classification of Liver Diseases Based on Ultrasound Image Texture Features

“…To reduce the unnecessary number of biopsy cases, other noninvasive methods for diagnosis have been applied widely, especially imaging techniques such as ultrasound (US) (e.g., see [2][3][4][5][6][7][8][9]), computed tomography (CT) (e.g., see [4,[10][11][12][13][14][15]) or magnetic resonance imaging (MRI) (e.g., see [2,3,12,16]). Among those methods, ultrasound imaging, with unique advances advantages such as no radiation, low cost, easy operation, and noninvasiveness, is widely used to visualize the liver for clinical diagnoses.…”

A Computationally Efficient Mean Sound Speed Estimation Method Based on an Evaluation of Focusing Quality for Medical Ultrasound Imaging

“…Various phase aberration correction methods have previously been proposed to compensate for the phase distortions [6][7][8][9][10][11][12][13]. Representatively, cross-correlation-based [6][7][8][9] and speckle-brightness-based [10] phase aberration correction methods have been proposed.…”

“…Representatively, cross-correlation-based [6][7][8][9] and speckle-brightness-based [10] phase aberration correction methods have been proposed. Recently, methods combined with adaptive beamforming methods have been proposed [11][12][13] that can improve the quality of whole images, but the implementation of these methods is still challenging due to their high computational complexity [13,14]. In the nearest-neighbor cross-correlation (NNCC) method [6,12,13], the number of multiplications is expressed approximately as N mult = (N − 1) × K image × L image × M, where N, L image and K image are the number of channels, scanlines and samples per a scanline in the whole image, respectively, and M is the total number of samples that contribute to the cross-correlation function.…”