Robust Short-Lag Spatial Coherence Imaging of Breast Ultrasound Data: Initial Clinical Results

Abstract

Ultrasound is frequently used in conjunction with mammography in order to detect breast cancer as early as possible. However, due largely to the heterogeneity of breast tissue, ultrasound images are plagued with clutter that obstructs important diagnostic features. Short-lag spatial coherence (SLSC) imaging has proven to be effective at clutter reduction in noisy ultrasound images. M-Weighted SLSC and Robust-SLSC (R-SLSC) imaging were recently introduced to further improve image quality at higher lag values, while R-SLSC imaging has the added benefit of enabling the adjustment of tissue texture to produce a tissue signal-to-noise ratio (SNR) that is quantitatively similar to B-mode speckle SNR. This paper investigates the initial application of SLSC, M-Weighted SLSC, and R-SLSC imaging to nine targets in the female breast [two simple cysts, one complicated cyst, two fibroadenomas, one hematoma, one complex cystic and solid mass, one invasive ductal carcinoma (IDC), and one ductal carcinoma in situ (DCIS)]. As expected, R-SLSC beamforming improves cyst and hematoma contrast by up to 6.35 and 1.55 dB, respectively, when compared to the original B-mode image, and similar improvements are achieved with SLSC and M-Weighted SLSC imaging. However, an interesting finding from this initial investigation is that the solid masses (i.e., fibroadenoma, complex cystic and solid mass, IDC, and DCIS), which appear as hypoechoic in the B-mode image, have similarly high coherence to that of surrounding tissue in coherence-based images. This work holds promise for using SLSC, M-Weighted SLSC, and/or R-SLSC imaging to distinguish between fluid-filled and solid hypoechoic breast masses.

The Generalized Contrast-to-Noise Ratio: A Formal Definition for Lesion Detectability

“…This leads to a hypothesis that adaptive beamforming may improve the visualization of the interventricular septum of the heart. We would also like to point to the results mentioned in Section 4.4 from (Wiacek et al 2018b), where we showed that the novelty and clinical relevance of adaptive beamforming is not limited to merely improving the image quality in terms of contrast and resolution. Adaptive beamforming can improve diagnostics by displaying novel clinical information different from the conventional B-mode image.…”

“…More specifically, we showed that SLSC could "improve the fluid vs. solid classification of hypoechoic breast masses and improve the detectability of fluid-filled masses, thereby improving the diagnostic power of breast ultrasound imaging." (Wiacek et al 2018b). Three images from the publication is included in Figure 4.1, where (a) is a simple fluid-filled cyst, (b) is a fibroadenoma (a benign mass) while (c) is a hematoma (a collection of blood).…”

“…Three images from(Wiacek et al 2018b) where (a) is a simple fluid filled cyst, (b) is a fibroadenoma which is a benign mass while (c) is a hematoma which is a collection of blood. IEEE holds all copyrights ©.…”

Improved Lesion Detection Using Nonlocal Means Post-Processing

“…Perhaps they can be better used to detect highly coherent targets, such as microcalcifications, or interfaces. Coherence beamforming has also been shown to be able to aid in the differentiation between solid and fluidfilled masses in-vivo [10].…”

Deep Learning to Obtain Simultaneous Image and Segmentation Outputs From a Single Input of Raw Ultrasound Channel Data

“…To highlight the versatility of the DNN trained with I fds , this DNN was evaluated with a ten-frame sequence of an in vivo simple cyst surrounded by breast tissue (denoted as Cyst #2), which was originally acquired for the separate study reported in [ 45 ]. These data were acquired with focused (rather than plane wave) transmissions, using an Alpinion L8-17 linear array transducer with parameters for the acquisition listed in Table II .…”