Novel technique for simultaneous imaging of the breast stiffness and incompressibility using quasi-static elastography - Caius, Matthew; Samani, Abbas (CMBES Proceedings, 2023)
I. INTRODUCTION Breast cancer is one of the most common cancers, representing 25% of all new cancers and 13% of all cancer related deaths in Canadian women [1]. Early detection before treatment of breast cancer is paramount as survival rates decrease significantly over time. Some of the most common diagnostic and screening procedures include breast MRI, mammography, and manual examination. These methods are either too costly or have difficulty detecting or differentiating malignant tumors from benign ones without a follow-up biopsy. One technique that has shown a potential to minimize the number of biopsy cases is ultrasound elastography (USE), which images the breast stiffness which is known to be substantially different for normal and pathological tissue [2]. Currently, the images produced by USE tend to be of low quality, plagued by noise and distortions due to the nature of ultrasound, inconsistent mechanical stimulation by the operator, and other inconsistencies in acquisition or tissue structure. We have developed new real-time techniques aimed at improving the data quality by enforcing known physical properties [3]. This work aims at developing a method by which the Young’s modulus, shear modulus, and Poisson’s ratio are simultaneously reconstructed. II. PROPOSED METHODS The reconstruction algorithms follow an iterative technique that include stress calculation followed by Young’s modulus and shear modulus reconstruction based on Hooke’s law where both axial and lateral strains are utilized. The Poisson’s ratio is then constructed using the two moduli images. By running these algorithms on a dataset of several in-silico models of tissue deformation at different loading levels, along with clinical breast cancer cases, we analyzed the accuracy, signal to noise ratio, and contrast to noise ratio of the stiffness images to determine which algorithms are more suited to breast cancer diagnosis. III. RESULTS The results of both in-silico and clinical cases show that, measured by their high signal-to-noise ratio and contrast-tonoise ratio, the reconstructed images of Young’s modulus, shear modulus and Poisson’s ratio are of high quality. Each image shows some unique features. For example, while the Young’s modulus measures tissue stiffness the shear modulus can be used to assess bonding between adjacent types of tissue. The latter shows low shear modulus at the outline of benign lesions. IV. CONCLUSION The investigation shows promising capability of the proposed algorithms to produce high quality images of tissue Young’s modulus, shear modulus and Poisson’s ratio that showed complementary features that can be fused for accurate breast cancer diagnosis. Further investigation is necessary to measure the method’s sensitivity and specificity.