The proposed weighted mean of VWT-Change is more sensitive than existing biomarkers in detecting treatment effects. This measurement tool will allow for many proof-of-principal studies to be performed for various novel treatments before a more costly study involving a larger population is held to validate the results.

This paper describes a framework for vascular image segmentation evaluation. Since the size of vessel wall and plaque burden is defined by the lumen and wall boundaries in vascular segmentation, these two boundaries should be considered as a pair in statistical evaluation of a segmentation algorithm. This work proposed statistical metrics to evaluate the difference of local vessel wall thickness (VWT) produced by manual and algorithm-based semi-automatic segmentation methods (ΔT) with the local segmentation standard deviation of the wall and lumen boundaries considered. ΔT was further approximately decomposed into the local wall and lumen boundary differences (ΔW and ΔL respectively) in order to provide information regarding which of the wall and lumen segmentation errors contribute more to the VWT difference. In this study, the lumen and wall boundaries in 3D carotid ultrasound images acquired for 21 subjects were each segmented five times manually and by a level-set segmentation algorithm. The (absolute) difference measures (i.e., ΔT, ΔW, ΔL and their absolute values) and the pooled local standard deviation of manually and algorithmically segmented wall and lumen boundaries were computed for each subject and represented in a 2D standardized map. The local accuracy and variability of the segmentation algorithm at each point can be quantified by the average of these metrics for the whole group of subjects and visualized on the 2D standardized map. Based on the results shown on the 2D standardized map, a variety of strategies, such as adding anchor points and adjusting weights of different forces in the algorithm, can be introduced to improve the accuracy and variability of the algorithm.

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Early diagnosis and classification of breast cancer is a critical step in choosing appropriate treatment plan. An ultrasound (US) elastography method for unifocal and multifocal breast cancer is presented. While this technique uses full inversion approach, it is cost-effective, fast, and expected to be more sensitive and specific than conventional US based elastography methods. This technique is capable of imaging absolute Young’s modulus (YM) of the tumour in real-time fashion, in contrast with other conventional elastography techniques that image relative elastic modulus off-line. To validate the proposed technique, numerical and tissue mimicking phantom studies were conducted. In the tissue mimicking study, a block shape gelatine-agar phantom was constructed with a cylindrical inclusion located deep inside the phantom. Results obtained from this study show accurate reconstruction of the YM with average error of less than 3%. The numerical phantom study has been extended for multifocal cases with average errors less than 6%.

Elastography is a non-invasive imaging technique that images tissue stiffness. Given the well known association between tissue stiffness and cancer type, it can be used effectively for breast cancer detection and assessment. This study involves system development of a real-time ultrasound based elastography system designed for assessing multifocal breast cancer. This system is capable of imaging breast tissues absolute Young's Moduli. The imaging involves tissue mechanical stimulation, displacement and force data acquisition followed by Young's modulus reconstruction using a constrained full-inversion approach. This approach utilizes axial strain field and surface force data acquired by the elastography system via an iterative numerical process to construct the breast tissue Young's modulus. The strain field is obtained using an ultrasound machine equipped with an RF signal processing module. For force data acquisition, a system comprised of two load cells attached at the ultrasound system probe was employed. Each iteration of the reconstruction algorithm involves tissue stress calculation followed by tissue Young's modulus updating. To speed up the reconstruction process, a novel accelerated finite element method developed in our laboratory was used for stress calculation. To validate the proposed method, tissue-mimicking phantom studies were conducted. These studies showed promising results paving the way for further validation and application in a clinical setting.