UltRAP-Net: Reverse Approximation of Tissue Properties in Ultrasound Imaging

Abstract

Medical ultrasound (US) has been widely used in clinical practices due to its merits of being low cost, real time, and radiation free. However, its capability to reveal the underlying tissue properties remains underexplored. A physics-constrained learning framework is studied to reversely approximate tissue property representations from multiple B-mode images acquired with varying dynamic ranges. First, an extractor network is used to generate property maps, that is, attenuation coefficient α, reflection coefficient β, border probability ρ<inf>b</inf>, scattering density ρ<inf>s</inf>, scattering amplitude ϕ, and one perturbation p map characterizing the variations caused by varying dynamic range. The α − ϕ maps are loosely regularized by rendering them forward to realistic US images using ray-tracing simulator. To further enforce the physics constraints, a ranking loss is introduced to align the disparity between two estimated p maps with the discrepancy between two distinct inputs. Due to the missing ground truth α − ϕ maps, alternatively, the method is validated by evaluating the consistency between the feature maps inferred from distinct images. The results demonstrate that the proposed method can robustly extract consistent intermediate maps from images. Furthermore, one potential downstream application is showcased to perform realistic US augmentation by introducing specific noise into the physics-inspired α − ϕ maps.