Eigenspace based minimum variance beamforming applied to ultrasound imaging of acoustically hard tissues

GPU-based minimum variance beamformer for synthetic aperture imaging of the eye

Minimum variance (MV) beamforming has emerged as an adaptive apodization approach to bolster the quality of images generated from synthetic aperture ultrasound imaging methods that are based on unfocused transmission principles. In this article, we describe a new high-speed, pixel-based MV beamforming framework for synthetic aperture imaging to form entire frames of adaptively apodized images at real-time throughputs and document its performance in swine eye imaging case examples. Our framework is based on parallel computing principles, and its real-time operational feasibility was realized on a six-GPU (graphics processing unit) platform with 3,072 computing cores. This framework was used to form images with synthetic aperture imaging data acquired from swine eyes (based on virtual point-source emissions). Results indicate that MV-apodized image formation with video-range processing throughput (>20 fps) can be realized for practical aperture sizes (128 channels) and frames with λ/2 pixel spacing. Also, in a corneal wound detection experiment, MV-apodized images generated using our framework revealed apparent contrast enhancement of the wound site (10.8 dB with respect to synthetic aperture images formed with fixed apodization). These findings indicate that GPU-based MV beamforming can, in real time, potentially enhance image quality when performing synthetic aperture imaging that uses unfocused firings.

Capon beamforming and moving objects--an analysis of lateral shift-invariance

If an ultrasound imaging system provides a presentation of a moving object which is sensitive to small spatial shifts, the system is said to be locally spatially shift-variant. This can happen, for instance, if the axial or lateral sampling is insufficient. The Capon beamformer has been shown to provide increased lateral resolution in ultrasound images. Increased lateral resolution should demand denser lateral sampling. However, in previous literature on Capon beamforming for medical ultrasound imaging, only single-frame scenarios have been simulated. Temporal behavior and effects caused by the increased resolution and lack of oversampling have therefore been neglected. In this paper, we analyze the local lateral shift-invariance of the Capon beamformer when imaging moving objects. We show that insufficient lateral sampling makes an imaging system based on the Capon beamformer laterally shift-variant. Different methods for oversampling on transmit and receive are then discussed and investigated to improve on the Capon beamformer. It is shown that lateral shift-invariance can be improved by oversampling based on phase rotation on receive without affecting the acquisition frame rate and with a minor change in processing complexity.

Beam-domain eigenspace-based minimum variance beamformer for medical ultrasound imaging

The eigenspace-based minimum variance (ESBMV) beamformer can provide good imaging resolution and contrast; however, the performance is achieved at the cost of high computational complexity. In adaptive array processing, the beamspace method is an efficient way to lower the computational complexity. In this paper, we combine the beamspace method with the ESBMV beamformer and propose a beamdomain ESBMV beamformer. We demonstrate the feasibility of introducing the beamspace into the ESBMV beamformer and propose an effective method of forming the transform matrix based on the spatial spectrum of the array signals. We also illustrate the performance of the proposed beamformer when resolving point scatterers and a cyst phantom with both simulated and experimental data. The results show that the proposed method can achieve performance comparable to the ESBMV beamformer within much shorter time.