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arXiv · 2610.05751

Physics-Grounded Objective-Conditioned Deep Beamforming for Adaptive Resolution-Contrast Control in Plane-Wave Ultrasound Imaging

Gopika Gopikrishnan·Mahesh Raveendranatha Panicker
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Abstract

Plane-wave ultrasound enables ultrafast imaging but remains constrained by a receive-aperture-dependent resolution- contrast trade-off. Fine structures benefit from high spatial resolution, whereas diffuse tissue and lesions often require improved contrast and clutter suppression. These objectives favor different receive-aperture configurations, yet conventional beamformers operate at a fixed imaging point. We propose a physics-grounded, objective-conditioned deep beamformer that enables controllable image formation within a single network. From single-plane-wave channel data, the network predicts spatially varying receive-apodization weights, conditioned on the desired imaging objective through feature-wise linear modulation. The conditioning variable is explicitly tied to a physically defined receive-aperture configuration and supervised using the correspondingcoherent-plane-wave-compounding target. Differentiable, region-of-interest-aware, physics-guided quality losses further promote objective-specific resolution and contrast during end-to-end training. The model is trained exclusively on simulated channel data spanning diverse target morphologies and echogenicities, and evaluated zero-shot on the PICMUS and CUBDL datasets, including unseen probe geometries. The proposed framework generally improves upon single-plane-wave delay-and-sum reconstruction while providing objective-dependent control. On PICMUS, the proposed method improves gCNR by up to 16.8% and axial resolution by 22% compared with the reference beamforming method. Moreover, switching between the contrast- and resolution-oriented objectives enables controllable changes of up to 4.9% in lesion contrast and 11.4% in lateral resolution. Compared with the best-performing learning-based baselines, the proposed method reduces lateral full-width-at-half-maximum by 5.9% and improves generalized contrast-to-noise ratio by 6.6%.

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BibTeXRIS

Gopika Gopikrishnan, Mahesh Raveendranatha Panicker. 2026-10-05. Physics-Grounded Objective-Conditioned Deep Beamforming for Adaptive Resolution-Contrast Control in Plane-Wave Ultrasound Imaging. https://arxiv.org/abs/2610.05751

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