Abstract：
:Most strain imaging techniques follow a pipeline strategy: in the first step, tissue displacement is estimated from radio-frequency (RF) frames, and in the second step, a spatial derivative operation is applied. There are two main issues that arise from this framework. First, the gradient operation amplifies noise, and therefore, smoothing techniques have to be adopted. Second, strain estimation does not exploit the original RF data. It rather relies solely on the noisy displacement field. In this paper, a novel technique is proposed that utilizes both the displacement field and the RF frames to accurately obtain the strain estimates. The normalized cross correlation (NCC) metric between two corresponding windows around the samples of the pre- and post-compressed images is employed to generate a dissimilarity measurement. The derivative of NCC with respect to the strain is analytically derived using the chain rule. This allows an efficient minimization of the dissimilarity metric with respect to the strain using the gradient descent optimization technique. The effectiveness of the proposed method is investigated through simulation data, phantom experiments, and in vivo patient data. The experimental results show that exploiting the information in RF data significantly improves the strain estimates.
journal_name
IEEE Trans Med Imagingjournal_title
IEEE transactions on medical imagingauthors
Omidyeganeh M,Xiao Y,Ahmad MO,Rivaz Hdoi
10.1109/TMI.2017.2685522subject
Has Abstractpub_date
2017-06-01 00:00:00pages
1347-1358issue
6eissn
0278-0062issn
1558-254Xjournal_volume
36pub_type
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