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<p>Photoacoustic (PA) imaging enables high-contrast visualization of blood vessels; however, accurate 3-D vascular imaging requires volumetric information across a wide frequency band. Sparse-element 2-D matrix array transducers can enable rapid and cost-effective 3-D imaging. However, when used with conventional universal back projection (UBP) techniques, these systems often suffer from image degradation. Model-based learning (MBLr), which integrates physical models with deep learning, has emerged as a promising approach to address this limitation and improve image quality from sparse sensor configurations. In this study, we performed simulation-based analyses of 3-D PA images to investigate the extent to which MBLr can achieve high contrast using a sparse, cost-effective matrix array and to examine the mechanism underlying its image quality improvements. Quantitative image quality metrics showed that MBLr provided significant improvements compared with conventional UBP reconstruction methods. Specifically, a sparse 16 × 16 array (Case S) reconstructed using MBLr outperformed both the baseline 32 × 32 array (Case B) and the ultra-wideband 32 × 32 array (Case U). Spatial frequency analysis revealed that MBLr enhanced high-frequency recovery in the in-plane (<i>x</i> and <i>y</i>) directions and improved low-frequency components in the axial (<i>z</i>) direction. These improvements enabled substantially enhanced 3-D vascular visualization with superior vessel delineation, contrast, and structural preservation. Overall, our findings demonstrate that MBLr enables high-quality volumetric imaging with sparse array transducer, providing important insights for the design of cost-effective PA imaging systems.</p>

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Systematic evaluation of model-based learning for cost-effective 3-D photoacoustic imaging with sparse 2-D matrix arrays

  • 期刊论文

  • 发表日期：

    2026年4月1日

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  • Misaki Nishiyama,
  • Takeshi Namita,
  • Makoto Yamakawa,
  • Tsuyoshi Shiina

摘要

Photoacoustic (PA) imaging enables high-contrast visualization of blood vessels; however, accurate 3-D vascular imaging requires volumetric information across a wide frequency band. Sparse-element 2-D matrix array transducers can enable rapid and cost-effective 3-D imaging. However, when used with conventional universal back projection (UBP) techniques, these systems often suffer from image degradation. Model-based learning (MBLr), which integrates physical models with deep learning, has emerged as a promising approach to address this limitation and improve image quality from sparse sensor configurations. In this study, we performed simulation-based analyses of 3-D PA images to investigate the extent to which MBLr can achieve high contrast using a sparse, cost-effective matrix array and to examine the mechanism underlying its image quality improvements. Quantitative image quality metrics showed that MBLr provided significant improvements compared with conventional UBP reconstruction methods. Specifically, a sparse 16 × 16 array (Case S) reconstructed using MBLr outperformed both the baseline 32 × 32 array (Case B) and the ultra-wideband 32 × 32 array (Case U). Spatial frequency analysis revealed that MBLr enhanced high-frequency recovery in the in-plane (x and y) directions and improved low-frequency components in the axial (z) direction. These improvements enabled substantially enhanced 3-D vascular visualization with superior vessel delineation, contrast, and structural preservation. Overall, our findings demonstrate that MBLr enables high-quality volumetric imaging with sparse array transducer, providing important insights for the design of cost-effective PA imaging systems.