16 research outputs found
Ultrasound Nondestructive Evaluation (NDE) Imaging with Transducer Arrays and Adaptive Processing
This paper addresses the challenging problem of ultrasonic non-destructive evaluation (NDE) imaging with adaptive transducer arrays. In NDE applications, most materials like concrete, stainless steel and carbon-reinforced composites used extensively in industries and civil engineering exhibit heterogeneous internal structure. When inspected using ultrasound, the signals from defects are significantly corrupted by the echoes form randomly distributed scatterers, even defects that are much larger than these random reflectors are difficult to detect with the conventional delay-and-sum operation. We propose to apply adaptive beamforming to the received data samples to reduce the interference and clutter noise. Beamforming is to manipulate the array beam pattern by appropriately weighting the per-element delayed data samples prior to summing them. The adaptive weights are computed from the statistical analysis of the data samples. This delay-weight-and-sum process can be explained as applying a lateral spatial filter to the signals across the probe aperture. Simulations show that the clutter noise is reduced by more than 30 dB and the lateral resolution is enhanced simultaneously when adaptive beamforming is applied. In experiments inspecting a steel block with side-drilled holes, good quantitative agreement with simulation results is demonstrated
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Minimum variance beamformers for coherent plane-wave compounding
In this paper we present and analyse a technique for applying minimum variance distortionless response (MVDR) beamforming to a coherent plane-wave compounding (CPWC) acquisition system. In the past, this has been done using a spatial smoothing approach that reduces the effective size of the receive aperture and degrades the image resolution. In this paper, we apply the MVDR algorithms in a novel way to the acquired data from the individual transducer elements, before any summation or other compounding. This enables us to propose a new approach for estimation of the covariance matrix that decorrelates the coherence among the components at all the different acquisition angles. This results in a new approach to receive beamforming for CPWC acquisition. The new beamformer is demonstrated on imaging data acquired with a research scanner. We find the new beamformer offers substantial improvements over the DAS method. It also significantly outperforms the previously published MVDR/CPWC beamformer on phantom studies where the signal from the main target is dominated by noise and interference. These improvements motivate further study in this new approach for enhancing image quality