A Learning-based Framework for Spatial Impulse Response Compensation in 3D Photoacoustic Computed Tomography.

2026-01-28

arXiv.org

AI Summary

This work addresses the resolution degradation in three-dimensional photoacoustic computed tomography caused by the spatial impulse response of large-aperture ultrasound transducers, which, while enhancing sensitivity, compromises image fidelity. Existing accurate correction methods are computationally prohibitive. To overcome this, the authors propose a learning-based data-domain compensation framework that enables end-to-end spatial impulse response correction for the first time in 3D photoacoustic imaging. By integrating a physics-informed Deconv-Net with a U-Net architecture, the method maps real measured data to that expected from an ideal point-like transducer, thereby enabling compatibility with efficient analytical reconstruction algorithms. Coupled with a fast analytical strategy for generating training data, the approach significantly improves resolution in both simulated and in vivo breast imaging, effectively recovering fine structures obscured by artifacts, and demonstrates robustness against noise, target complexity, and acoustic heterogeneity.

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Abstract

Photoacoustic computed tomography (PACT) is a promising imaging modality that combines the advantages of optical contrast with ultrasound detection. Utilizing ultrasound transducers with larger surface areas can improve detection sensitivity. However, when computationally efficient analytic reconstruction methods that neglect the spatial impulse responses (SIRs) of the transducer are employed, the spatial resolution of the reconstructed images will be compromised. Although optimization-based reconstruction methods can explicitly account for SIR effects, their computational cost is generally high, particularly in three-dimensional (3D) applications. To address the need for accurate but rapid 3D PACT image reconstruction, this study presents a framework for establishing a learned SIR compensation method that operates in the data domain. The learned compensation method maps SIR-corrupted PACT measurement data to compensated data that would have been recorded by idealized point-like transducers. Subsequently, the compensated data can be used with a computationally efficient reconstruction method that neglects SIR effects. Two variants of the learned compensation model are investigated that employ a U-Net model and a specifically designed, physics-inspired model, referred to as Deconv-Net. A fast and analytical training data generation procedure is also a component of the presented framework. The framework is rigorously validated in virtual imaging studies, demonstrating resolution improvement and robustness to noise variations, object complexity, and sound speed heterogeneity. When applied to in-vivo breast imaging data, the learned compensation models revealed fine structures that had been obscured by SIR-induced artifacts. To our knowledge, this is the first demonstration of learned SIR compensation in 3D PACT imaging.

Problem

• Photoacoustic computed tomography
• Spatial impulse response
• 3D imaging
• Image reconstruction
• Resolution degradation

Innovation

• learned SIR compensation
• 3D photoacoustic computed tomography
• data-domain correction
• physics-inspired deep learning
• spatial impulse response