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Poor resolution of ultrasound images due to convolution of the tissuereflectivity function (TRF) with the system point spread function (PSF) is amajor issue in medical ultrasound imaging. In this paper, we propose acorrelation constrained missing-data estimation based blind multichannelfrequency- domain least-mean-squares (md-bMCFLMS) algorithm to undo the effectof PSF on the ultrasound radio-frequency (RF) data. In the first step, ablock-based MCFLMS (bMCFLMS) algorithm is proposed to estimate the TRFs and thePSF which are used in the second step to estimate the missing data. Thismissing data is used in the md-bMCFLMS algorithm to construct a modified costfunction for further improvement of the image resolution. To account for thenonstationarity of the PSF, unlike the blocking approach described in theliterature, we introduce a time-efficient blocking method in this paper. Theblocking approach described here uses a block position independent fixed sizematrix and can be implemented parallely. The bMCFLMS algorithm, however, showsmisconvergence due to both channel noise and propagation of TRF estimationerror from the previous blocks. This phe- nomenon is more intense in the caseof md-bMCFLMS algorithm because of increased estimation error. To address thisproblem, a novel constraint based on the correlation between the measured RFdata and estimated TRF is proposed in this paper. The efficacy of our proposedblind deconvolution algorithm is measured using simulation phantom,experimental phantom and in-vivo data.

Ultrasonic Tissue Reflectivity Function Estimation Using Correlation Constrained Multichannel FLMS Algorithm with Missing RF Data