APRIL: Adaptive Regression-Based Two-Dimensional Quantitative Anisotropy Imaging Using Acoustic Radiation Force Impulse

Objective This study aims to develop and validate a quantitative, depth-resolved anisotropy imaging framework that extends ARFI-based focal degree-of-anisotropy (DoA) estimation into two-dimensional mapping by modeling the depth-dependent relationship between shear modulus ratio (SMR) and peak displacement ratio (PDR). Methods We propose APRIL ( A daptive P olynomial R egression for anisotropy Imaging via ARFI-induced Disp L acements), a framework for quantitative, depth-resolved DoA imaging that adaptively selects polynomial regression or shape-preserving spline interpolation based on excitation PSF asymmetry. Training data were generated using an LS-DYNA3D + Field II simulation pipeline in homogeneous transversely isotropic media (SMR 0.9–4.9). Testing included shifted SMRs under varied acoustic conditions and three heterogeneous inclusion configurations (anisotropic inclusion in isotropic background and vice versa). Experimental validation was performed in an in-vivo murine tumor model over the time, ex-vivo chicken breast, and tissue-mimicking gelatin phantoms, using a Verasonics system with an L11-5v transducer. Results APRIL achieved depth-resolved SMR prediction errors below 9% over 10–30 mm, with highest accuracy in the focal region (MAE 2.3%, RMSE < 0.1) and stable performance across PSF transition zones. In heterogeneous phantoms, it reconstructed anisotropy maps with SSIM up to 86% and MPE below 7%, accurately delineating inclusion boundaries. Under acoustic parameter variations, mean absolute errors remained below 10%, demonstrating robustness to system and tissue heterogeneity. Conclusion APRIL enables robust, two-dimensional anisotropy imaging beyond focal estimates. Significance The method provides a physically grounded and generalizable framework for clinically viable anisotropy biomarkers in muscle, tendon, kidney, tumor and breast tissues. Highlights Novelty: APRIL introduces LoA-conditioned adaptive polynomial-spline regression to extend ARFI-based anisotropy estimation from focal point estimates into full 2D depth-resolved SMR imaging. Results: APRIL achieved SMR prediction errors below 9% over 10–30 mm, SSIM up to 86% in heterogeneous phantoms, MAE below 10% under acoustic variations, tracked tumor anisotropy progression in vivo, and differentiated anisotropic inclusion versus isotropic background in tissue-mimicking gelatin phantom. Significance: APRIL enables clinically viable, spatially resolved anisotropy biomarker imaging in muscle, tendon, kidney, and tumor tissues without requiring heterogeneous training data.