Lesion edge preserved direct average strain estimation for ultrasound elasticity imaging

• Published in: Ultrasonics 2014-01, Vol.54 (1), p.137-146
• Main Authors: Hussain, Mohammad Arafat, Alam, Farzana, Rupa, Sharmin Akhtar, Awwal, Rayhana, Lee, Soo Yeol, Hasan, Md. Kamrul
• Format: Article
• Language: English
• Subjects: Adolescent; Adult; Algorithms; Average strain; Breast Neoplasms - diagnostic imaging; Breast Neoplasms - physiopathology; Computer simulation; Edge detection; Elastic Modulus; Elasticity Imaging Techniques - methods; Elastography; Female; Finite element method; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Imaging; Instantaneous strain; Lesions; Mathematical models; Middle Aged; Normalized cross-correlation; Pattern Recognition, Automated - methods; Reproducibility of Results; Sensitivity and Specificity; Strain; Ultrasonic imaging; Ultrasonography, Mammary - methods; Young Adult
• ISSN: 0041-624X; 1874-9968
• DOI: 10.1016/j.ultras.2013.05.010

•We introduce an edge detection and preservation criteria in the direct-average-strain-estimation (DASE) method.•Lesion edges are detected and preserved while the strain continuity is ensured by the DASE method.•The efficacy of the proposed method is tested with the FEM simulation, tissue mimicking phantom and in vivo patient data.•The edges and internal stiffness variation of the lesions are well preserved by the proposed method. Elasticity imaging techniques with built-in or regularization-based smoothing feature for ensuring strain continuity are not intelligent enough to prevent distortion or lesion edge blurring while smoothing. This paper proposes a novel approach with built-in lesion edge preservation technique for high quality direct average strain imaging. An edge detection scheme, typically used in diffusion filtering is modified here for lesion edge detection. Based on the extracted edge information, lesion edges are preserved by modifying the strain determining cost function in the direct-average-strain-estimation (DASE) method. The proposed algorithm demonstrates approximately 3.42–4.25dB improvement in terms of edge-mean-square-error (EMSE) than the other reported regularized or average strain estimation techniques in finite-element-modeling (FEM) simulation with almost no sacrifice in elastographic-signal-to-noise-ratio (SNRe) and elastographic-contrast-to-noise-ratio (CNRe) metrics. The efficacy of the proposed algorithm is also tested for the experimental phantom data and in vivo breast data. The results reveal that the proposed method can generate a high quality strain image delineating the lesion edge more clearly than the other reported strain estimation techniques that have been designed to ensure strain continuity. The computational cost, however, is little higher for the proposed method than the simpler DASE and considerably higher than that of the 2D analytic minimization (AM2D) method.