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by claude@2026-06, 2026-06-24
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The paper introduces PA-SfM, a tracker-free differentiable acoustic structure-from-motion framework for 3D photoacoustic imaging, aiming to estimate sensor-array poses directly from photoacoustic measurements instead of using motor feedback or external tracking hardware. Using a differentiable acoustic radiation model, hierarchical optimization, and rigid-array constraints, the method jointly recovers inter-view transformations and reconstructs 3D photoacoustic volumes. The authors validate it in numerical simulations and in vivo rat kidney and liver imaging with known relative geometry, as well as in mechanically scanned 3D PAI, reporting higher-quality vascular reconstructions than encoder-based fusion in a mouse liver rotation study and expanded-field-of-view mapping without translation-stage pose input, with quantitative fidelity metrics (PSNR 38.90–41.42 dB; SSIM 0.9637–0.9864) relative to ground truth or known-pose references. The main caveat is that quantitative benchmarking depends on simulations and reference/known-geometry setups rather than fully unconstrained ground truth in freehand conditions. This paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.
Abstract
Three-dimensional photoacoustic imaging (3D PAI) commonly relies on sparse sensor arrays, which has both limited spatial sampling and field-of-view (FOV). Moving the sensor array or the target provides an effective route to multi-view imaging and large volume PA mapping, but accurate fusion of multiple poses conventionally depends on motor feedback or external tracking hardware. Such tracking increases system complexity and can suffer from calibration errors, backlash and motion instability. Here we introduce PA-SfM, a tracker-free differentiable acoustic structure-from-motion (SfM) framework that recovers relative imaging poses directly from PA measurements. By integrating a differentiable acoustic radiation model with hierarchical optimization and rigid array constraints, PA-SfM jointly estimates inter-view transformations and reconstructs 3D PA volumes without external pose measurements. We demonstrate genuine freehand 3D PAI of human hand vasculature, in which arbitrary hand motion over approximately 1 s provides multi-view measurements from which PA-SfM recovers the relative poses and jointly reconstructs a large FOV vascular network without motion tracking or predefined trajectories. We further validate PA-SfM using numerical simulations, in vivo rat kidney and liver imaging with known relative geometry, and a mechanically scanned 3D PAI system. Compared with encoder-based fusion of the mechanically scanned system, PA-SfM produced sharper and more continuous vascular reconstructions with expanded FOV. In controlled quantitative validations, PA-SfM achieved high reconstruction fidelity, with PSNRs of 38.90–41.42 dB and SSIMs of 0.9637–0.9864 relative to ground truth or known pose reference reconstructions. These results establish PA-SfM as a robust computational framework for tracker-free, freehand, multi-view and expanded FOV 3D PAI, providing an algorithmic foundation for flexible large volume PA vascular imaging. The source code is publicly available at https://github.com/JaegerCQ/PA-SfM .
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Abstract
Three-dimensional photoacoustic imaging (3D PAI) commonly relies on sparse sensor arrays, which restrict angular sampling, detection aperture, and instantaneous field-of-view (FOV). Moving the sensor array relative to the target provides an effective route to multi-view imaging and large volume photoacoustic mapping, but accurate fusion of multiple poses conventionally depends on motor feedback or external tracking hardware. Such tracking increases system complexity and can suffer from calibration errors, backlash, and motion instability. Here we introduce PA-SfM, a tracker-free differentiable acoustic structure-from-motion (SfM) framework that recovers sensor array poses directly from photoacoustic measurements. By integrating a differentiable acoustic radiation model with hierarchical optimization and rigid-array constraints, PA-SfM jointly estimates inter-view transformations and reconstructs 3D photoacoustic volumes without external pose measurements. We validated PA-SfM using numerical simulations, in vivo rat kidney and liver imaging with known relative geometry, and a mechanically scanned 3D PAI system. In mechanically rotated mouse liver imaging, PA-SfM produced sharper and more continuous vascular reconstructions than encoder-based fusion. In translational multi-pose imaging, PA-SfM supported expanded FOV vascular mapping without translation-stage pose input. In controlled quantitative validations, PA-SfM achieved high reconstruction fidelity, with PSNRs of 38.90–41.42 dB and SSIMs of 0.9637–0.9864 relative to groundtruth or known-pose reference reconstructions. These results establish PA-SfM as a robust computational framework for tracker-free multi-view and expanded FOV 3D PAI, providing a complete algorithmic foundation for freehand 3D PAI. The source code is publicly available at https://github.com/JaegerCQ/PA-SfM.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Expanded FOV results added, result section and discussion section updated.
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