HIGHLIGHTSWe introduced a model‐based beamforming algorithm.We assessed the algorithm's limitation and ability to suppress off‐axis scattering.We tested the performance of ADMIRE in the presence of gross sound speed deviation. ABSTRACT… Click to show full abstract
HIGHLIGHTSWe introduced a model‐based beamforming algorithm.We assessed the algorithm's limitation and ability to suppress off‐axis scattering.We tested the performance of ADMIRE in the presence of gross sound speed deviation. ABSTRACT Previously, we introduced a model‐based beamforming algorithm to suppress ultrasound imaging artifacts caused by clutter sources, such as reverberation and off‐axis scattering. We refer to this method as aperture domain model image reconstruction (ADMIRE). In this study, we evaluated the algorithm's limitations and ability to suppress off‐axis energy using Field II‐based simulations, experimental phantoms and in vivo data acquired by a Verasonics ultrasound system with a curvilinear transducer (C5–2). We compared image quality derived from a standard delay‐and‐sum (DAS) beamformer, DAS with coherence factor (CF) weighting, ADMIRE and ADMIRE plus CF weighting. Simulations, phantoms and in vivo scan results demonstrate that ADMIRE substantially suppresses off‐axis energy, while preserving the spatial resolution of standard DAS beamforming. We also observed that ADMIRE with CF weighting further improves some aspects of image quality. We identified limitations of ADMIRE when suppressing off‐axis clutter in the presence of strong scattering, and we suggest a solution. Finally, because ADMIRE is a model‐based beamformer, we used simulated phantoms to test the performance of ADMIRE under model‐mismatch caused by gross sound speed deviation. The impact of sound speed errors largely mimics DAS beamforming, but ADMIRE never does worse than DAS itself in resolution or contrast. As expected the CF weighting used as a post processing technique provides a boost in contrast but decreases CNR and speckle SNR. The results indicate that ADMIRE is robust in terms of model‐mismatch caused by sound speed variation, especially when the actual sound speed is slower than the assumed sound speed. As an example, the image contrast obtained using DAS, DAS+CF, ADMIRE and ADMIRE+CF in the presence of −5% gross sound speed error are 24.9±0.71dB, 39.1±1.2dB, 43.2±2.3dB and 52.5±2.9dB, respectively.
               
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