KEYWORDS: Computer programming, Signal to noise ratio, Tissues, Ultrasonography, Elastography, Medical imaging, Interference (communication), Image compression, Signal analyzers, Tumors
Elastography, computation of elasticity modulus of tissue is one of medical imaging methods with
applications such as tumor detection and ablation therapy. Phase-based time delay estimation methods exploit
the frequency information of the RF data to obtain strain estimates [1]. Although iterative Phase zero
estimation is more computationally efficient in comparison to methods that seek for the absolute maximum
cross-correlation between precompression and postcompression echo signals, it is quite sensitive to noise. The
reason for this sensitivity is that for this iterative method an initial guess for the time shift is needed for each
pixel. To estimate time shifts for the sample k, the time shift resulted from iterative phase zero method applied
on sample k-1 is used as an initial value. This makes the method sensitive to noise because the error is
propagating sample by sample and if the method gets unstable for any pixel, it will give unstable result for the
following pixels in image line. Proposed strategy in this work to overcome this problem is to first estimate the
displacement using Dynamic Programming [2] and use the results from DP as an initial guess of displacement
for each pixel in iterative Phase zero method. Recently, regularized methods that incorporate the prior of
tissue continuity in time delay estimation have been shown to produce low-noise and high contrast strain
images [3,5]. In this work, we also incorporate the prior of tissue motion continuity in the phase zero method
to make the zero-phase method more robust to signal decorrelation.
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