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arXiv 2404.00471physics.med-phcs.CVcs.LGeess.IV

用于光声断层成像图像重建的基于分数的扩散模型

Score-Based Diffusion Models for Photoacoustic Tomography Image Reconstruction

California Institute of Technology(加州理工学院)

Sreemanti Dey, Snigdha Saha, Berthy T. Feng, Manxiu Cui, Laure Delisle, Oscar Leong, Lihong V. Wang, Katherine L. Bouman

AI总结:

本文提出使用基于分数的扩散模型解决光声断层成像中因传感器覆盖或换能器阵列密度不足导致的不适定逆重建问题,利用在模拟血管结构上学习的表达性先验,实现对不同换能器稀疏条件的鲁棒重建。

摘要:

光声断层成像(PAT)是一种快速发展的医学成像模态,它结合了光学吸收对比度和超声成像深度。PAT中的一个挑战是在传感器覆盖范围有限或换能器阵列密度不足的情况下,声学信号不充分时的图像重建。这类情况需要求解一个不适定的逆重建问题。在这项工作中,我们使用基于分数的扩散模型来求解从有限的PAT测量中重建图像的逆问题。所提出的方法使我们能够融入一个由扩散模型在模拟血管结构上学习到的表达性先验,同时仍能对不同的换能器稀疏条件保持鲁棒性。

英文摘要:

Photoacoustic tomography (PAT) is a rapidly-evolving medical imaging modality that combines optical absorption contrast with ultrasound imaging depth. One challenge in PAT is image reconstruction with inadequate acoustic signals due to limited sensor coverage or due to the density of the transducer array. Such cases call for solving an ill-posed inverse reconstruction problem. In this work, we use score-based diffusion models to solve the inverse problem of reconstructing an image from limited PAT measurements. The proposed approach allows us to incorporate an expressive prior learned by a diffusion model on simulated vessel structures while still being robust to varying transducer sparsity conditions.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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