Stolt migration based iterative trace reconstruction

Proper spatial sampling is critical for high quality imaging. If the sampling criterion is not met, grating lobe artifacts appear in the image. In non-destructive testing applications efficiency is being increased by the steady enlargement of the field of view, resulting in more elements and thus a higher transducer complexity and cost. In the volume scanning methods used in medical applications, the challenge lies in connecting the 2500+ elements of a matrix array to <;256 channels. Usually pre-beamforming is used to reduce the data at the cost of image quality. An alternative is to reconstruct the non-aliased data from spatially aliased data. Last year we reported a reconstruction method based on wave field extrapolation, which performs best for a limited depth range. In this work the method is extended to cover the entire imaging range at once. Its performance is investigated using phased array data of a tissue phantom. To reconstruct the traces the technique uses an iterative scheme based on a fast wavenumber-frequency domain mapping (Stolt migration) and its inverse in combination with thresholding to exclude the aliasing artifacts in the imaging domain. A properly sampled dataset was recorded of a tissue mimicking phantom using a linear phased array transducer connected to a research scanner for full channel data capture. From this dataset, undersampled datasets were created by selecting a limited number of channels. The datasets were imaged using wavenumber-frequency domain mapping (Stolt migration). The reconstruction method significantly improved the image quality of the aliased datasets.