发表机构
California Institute of Technology（加州理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对光声层析成像的运动伪影问题，提出深度偏好进化框架，利用成对图像比较器引导重建参数优化，在模拟及人体手掌图像中均实现了有效运动校正，为稀缺标签下的图像恢复提供了可迁移优化目标。
AI 中文摘要
三维光声层析成像（PAT）中，由稀疏阵列的大范围机械扫描导致的运动伪影会降低图像质量。由于稀疏采样会产生低质量的子重建，直接追踪运动极具挑战性。虽然最终图像质量原则上可指导伪影校正，但常规正则化器无法准确反映光声图像质量。本文提出深度偏好进化（DPE），这是一种无导数优化框架，其中学习到的图像比较器会引导对重建参数的进化搜索。通过学习同一目标图像对之间的相对质量差异而非绝对质量分数，该比较器在经过无标签域校准后，从模拟图像到体内图像的泛化能力比绝对评分器更稳健。基于比较器的DPE成功校正了不同解剖结构和背景下的合成运动，还减轻了人类手掌图像中自然、无约束运动带来的伪影。这些结果表明，当带标签的实验训练数据稀缺时，成对学习比较可为高维图像恢复提供可迁移的优化目标。
英文摘要
Motion artifacts in three-dimensional photoacoustic tomography (PAT), caused by extended mechanical scanning of sparse arrays, degrade image quality. Since sparse sampling results in low-quality sub-reconstructions, tracking motion directly is challenging. While final image quality could in principle guide artifact correction, conventional regularizers do not accurately reflect photoacoustic image quality. Here, we introduce deep preferential evolution (DPE), a derivative-free optimization framework where a learned image comparator guides evolutionary search over reconstruction parameters. By learning relative quality differences between same-target image pairs rather than absolute quality scores, the comparator generalized more robustly from simulation to in-vivo images (after unlabeled domain calibration) than an absolute scorer. Comparator-based DPE successfully corrected synthetic motion across diverse anatomies and backgrounds, and it mitigated artifacts from natural, unconstrained motion in human palm images. These results demonstrate that pairwise learned comparison can provide a transferable optimization objective for high-dimensional image restoration when labeled experimental training data are scarce.
