Strain estimation by a Fourier Series-based extrema tracking algorithm for elastography

A Brief Review on Breast Carcinoma and Deliberation on Current Non Invasive Imaging Techniques for Detection

BACKGROUND

Breast carcinoma is a life threatening disease that accounts for 25.1% of all carcinoma among women worldwide. Early detection of the disease enhances the chance for survival.

DISCUSSION

This paper presents comprehensive report on breast carcinoma disease and its modalities available for detection and diagnosis, as it delves into the screening and detection modalities with special focus placed on the non-invasive techniques and its recent advancement work done, as well as a proposal on a novel method for the application of early breast carcinoma detection.

CONCLUSION

This paper aims to serve as a foundation guidance for the reader to attain bird's eye understanding on breast carcinoma disease and its current non-invasive modalities.

Artifacts detection-based adaptive filtering to noise reduction of strain imaging

Strain imaging in medical ultrasound is the imaging modality of elastic properties of biological tissue. In general, strain image will suffer from artifacts noise, which degrades lesion detectability and increases the likelihood of misdiagnosis. How to both suppress artifacts effectively and preserve the structure is vital for diagnosis and also for image post-processing. The bilateral filtering can reduce artifact noise and, at the same time, maintain the tissue structure. However, the balance between noise suppression and edge preservation often makes the threshold selection difficult. This paper is to solve the problem of difficult threshold selection in bilateral filtering. The probability distribution function of amplitude modulation noise in this paper is derived from the statistics of uncompressed speckle. The statistical model of artifact formation is useful for designing an adaptive fast bilateral filter for artifact reduction in ultrasound strain imaging. Both simulation and phantom testing show that the proposed method can improve the quality of ultrasonic strain imaging. Furthermore, the elastographic signal-to-noise ratio was increased by 129.91% and 52.36% for simulated and phantom strain images. The elastographic contrast-to-noise ratio was increased by 521.42% and 218.07% for simulated and phantom strain images, respectively. As indicated by the profiles, the proposed method produces a better result for the purpose of visualization.

Adaptive mesh refinement for elastic modulus reconstruction in elastography

Meshes play a crucial role in determining the accuracy of the elastic modulus reconstruction in the elastography when the finite element method is employed. In this article, we propose an adaptive mesh refinement strategy which can ensure the coincidence of the meshes with the shape of the inclusions in the observed tissue. This strategy is based on the intensity distribution of the strain image where the variance of the strain distribution in each element of the meshes is used to measure the homogeneity of the element, that is, the larger the strain variance is the more inhomogeneous the element will be and hence more detailed information will be included in this element. For more accurate reconstruction of such detailed information, mesh refinement procedure is implemented in such elements. Besides, two refinement steps are employed for the reconstruction to improve the fitness of the reconstructed image and the observed tissue. Simulation results show that the two-stage adaptive mesh refinement algorithm performs well without needing any prior information about the internal geometric shape in tissue. Not only Young’s moduli of models but also shapes of the inclusions can be reconstructed perfectly and quickly with our proposed method.

A Fast Peak-Searching Algorithm for Ultrasonic Elastography

Tissue axial strain estimation with ultrasound elastography has become a hot field in recent years. However, for keypoints tracking-based elastography algorithms, locating extrema in multimodal ultrasonic radiofrequency signals is still a challenging problem. In this paper, a new method is proposed to locate the local maxima and minima of the RF signals directly without derivation operation. This algorithm can accurately locate extrema even if disturbed peaks resulting from different noise exist. Furthermore, the new algorithm can speed up approximately 79% of the implementation process as compared with the standard cross-correlation method on the same computing platform. In addition, the elastographic signal-to-noise ratio and the contrast-to-noise ratio are also significantly improved with this new method.