Spiral Scanning and Self-Supervised Image Reconstruction Enable Ultra-Sparse Sampling Multispectral Photoacoustic Tomography
arxiv(2024)
摘要
Multispectral photoacoustic tomography (PAT) is an imaging modality that
utilizes the photoacoustic effect to achieve non-invasive and high-contrast
imaging of internal tissues. However, the hardware cost and computational
demand of a multispectral PAT system consisting of up to thousands of detectors
are huge. To address this challenge, we propose an ultra-sparse spiral sampling
strategy for multispectral PAT, which we named U3S-PAT. Our strategy employs a
sparse ring-shaped transducer that, when switching excitation wavelengths,
simultaneously rotates and translates. This creates a spiral scanning pattern
with multispectral angle-interlaced sampling. To solve the highly
ill-conditioned image reconstruction problem, we propose a self-supervised
learning method that is able to introduce structural information shared during
spiral scanning. We simulate the proposed U3S-PAT method on a commercial PAT
system and conduct in vivo animal experiments to verify its performance. The
results show that even with a sparse sampling rate as low as 1/30, our U3S-PAT
strategy achieves similar reconstruction and spectral unmixing accuracy as
non-spiral dense sampling. Given its ability to dramatically reduce the time
required for three-dimensional multispectral scanning, our U3S-PAT strategy has
the potential to perform volumetric molecular imaging of dynamic biological
activities.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要