Fri, 13 May, 15:00 - 15:45 UTC
In this demo, we present a software built for recovering the vasculature of breast lesions from contrast enhanced ultrasound scans. Specifically, we present demonstrations on in vivo human scans of three different breast lesions acquired with a clinical ultrasound scanner.
Breast cancer is the most common malignancy in women. Early diagnosis of breast cancer is primordial to enable appropriate treatments and improve prognosis. Ultrasound is a widely available and safe imaging tool. It is often used as an adjunct to mammography for screening, especially in women with dense breast tissue. However, it is used only as a support tool and not as a main tool for diagnosis due to its inherent disadvantages, such as low specificity and resolution. We recently proposed a way to enhance the use of ultrasound as a diagnostic tool for early breast cancer detection [1]. By using contrast-enhanced ultrasound in combination with an advanced super-resolution algorithm we were able to demonstrate the microvascular profile of breast lesions. We use a model based deep learning method for super resolution ultrasound imaging to achieve sub diffraction resolution. The network exploits the properties of the ultrasound signal to devise a parameter efficient network that can generalize well. By leveraging our trained network, the microvasculature structure is recovered in a short time, overcoming challenges such as prior knowledge about the system PSF and limited clinical data for training.
Our demonstration platform consists of an interactive user interface that enables the user to choose an ultrasound scan to reconstruct. The user will be able to see the data as seen to the physician in real time, and will enable the user to process the data and generate a super-resolved image showing microvascular structures that were unseen in the original scan. The user will be able to compare the super-resolved images that exhibit different structures, thus enabling to better differentiate between the lesions.
References: [1] Bar-Shira, O., Grubstein, A., Rapson, Y., Suhami, D., Atar, E., Peri-Hanania, K., Rosen, R. and Eldar, YC. "Learned super resolution ultrasound for improved breast lesion characterization." In International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 109-118. Springer, Cham, 2021.