Year: 2025
Type: article
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 ρb, scattering density ρs, 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.
Source: Advanced Intelligent Systems
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,970
Awards: Soft-robot-empowered acoustic phase and amplitude modulation for high-efficacy MRI-guided focused ultrasound treatments, High-fidelity tendon-driven robotic assistant with multimodal sensing for joint arthroplasty, G2514893556, G4491430088, 3D Forward-viewing Optical Coherence Tomography through Soft Robotic Scanning for In-Situ Endoscopic Augmented Reality, G5676179972, G5965398168
OpenAlex ID: W4409566800