  Poor resolution of ultrasound images due to convolution of the tissue
reflectivity function (TRF) with the system point spread function (PSF) is a
major issue in medical ultrasound imaging. In this paper, we propose a
correlation constrained missing-data estimation based blind multichannel
frequency- domain least-mean-squares (md-bMCFLMS) algorithm to undo the effect
of PSF on the ultrasound radio-frequency (RF) data. In the first step, a
block-based MCFLMS (bMCFLMS) algorithm is proposed to estimate the TRFs and the
PSF which are used in the second step to estimate the missing data. This
missing data is used in the md-bMCFLMS algorithm to construct a modified cost
function for further improvement of the image resolution. To account for the
nonstationarity of the PSF, unlike the blocking approach described in the
literature, we introduce a time-efficient blocking method in this paper. The
blocking approach described here uses a block position independent fixed size
matrix and can be implemented parallely. The bMCFLMS algorithm, however, shows
misconvergence due to both channel noise and propagation of TRF estimation
error from the previous blocks. This phe- nomenon is more intense in the case
of md-bMCFLMS algorithm because of increased estimation error. To address this
problem, a novel constraint based on the correlation between the measured RF
data and estimated TRF is proposed in this paper. The efficacy of our proposed
blind deconvolution algorithm is measured using simulation phantom,
experimental phantom and in-vivo data.
