Ultrasound imaging using an active sensing array has been extensively studied in both time domain and frequency domain. Subspace decomposition methods in match field beamforming such as the multiple signal classification (MUSIC) algorithm can achieve subwavelength resolution of distinct point scatterers. However, when the size of the target is on the order of one wavelength or larger, the MUSIC type algorithms suffer from poor performance due to a tangled eigen structure. This paper proposes an adaptive match field beamformer that does not require subspace decomposition to achieve high resolution imaging of extended targets. Specifically, the broadband coherent white noise constraint (C-WNC) algorithm is utilized to achieve high focusing ability of extended targets by exploiting the cross-frequency coherence in an active sensing scheme. The dynamic range bias in the adaptive beamformer benefits the C-WNC algorithm to achieve high contrast regardless of the SNR. Both simulations and experiments show that the C-WNC images retain their resolution cells on the tips of the extended target with sizes ranging from a wavelength to sizes as large as the physical aperture width. A robust imaging scheme is then proposed to obtain high quality images by combining C-WNC images with a statistically stable delay-multiply-and-sum (DMAS) algorithm to create high-contrast and high-resolution images of extended targets in both azimuth and axial range directions.
Keyphrases
- high resolution
- deep learning
- machine learning
- convolutional neural network
- mass spectrometry
- high speed
- magnetic resonance
- induced apoptosis
- tandem mass spectrometry
- optical coherence tomography
- contrast enhanced
- single molecule
- computed tomography
- cell death
- high throughput
- photodynamic therapy
- single cell
- magnetic resonance imaging
- cell cycle arrest
- oxidative stress
- pi k akt