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

Abstract

ObjectiveThis 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). MethodsWe propose APRIL (Adaptive Polynomial Regression for anisotropy Imaging via ARFI-induced DispLacements), 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. ResultsAPRIL 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. ConclusionAPRIL enables robust, two-dimensional anisotropy imaging beyond focal estimates. SignificanceThe method provides a physically grounded and generalizable framework for clinically viable anisotropy biomarkers in muscle, tendon, kidney, tumor and breast tissues. HighlightsO_LINovelty: 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. C_LIO_LIResults: 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. C_LIO_LISignificance: APRIL enables clinically viable, spatially resolved anisotropy biomarker imaging in muscle, tendon, kidney, and tumor tissues without requiring heterogeneous training data. C_LI