GenMC：用于定量光声成像的实时生成式蒙特卡罗替代模型
GenMC: Real-Time Generative Monte Carlo Surrogate for Quantitative Photoacoustic Imaging
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- Imperial College London（帝国理工学院）
- King’s College London（伦敦国王学院）
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中文总结 AI 辅助
GenMC提出一种基于条件生成对抗网络的实时蒙特卡罗替代模型，通过超声引导的解剖先验快速估计光通量，实现定量光声成像中血氧饱和度的准确、稳健测量。
中文摘要 AI 辅助
光声（PA）成像能够提供组织的分子和功能信息，如血氧饱和度（sO2），但其临床转化受到定量不准确的阻碍。一个主要的误差来源是光谱着色效应，即波长依赖的光衰减扭曲了局部光通量。蒙特卡罗（MC）模拟是模拟光传输的金标准，但其计算需求阻碍了实时使用。本文提出了GenMC，一种基于条件生成对抗网络的深度生成框架，它从组织解剖结构和文献来源的光学特性估计光通量分布，其中解剖先验来自共配准的超声图像。在MC生成的合成数据集上训练后，GenMC在每帧不到30毫秒内生成高保真通量图，相比传统MC模拟实现了四个数量级的加速，并在体内达到高达36.24 dB的峰值信噪比，优于UNet和Pix2Pix基线。在模拟血液的体模和涵盖Fitzpatrick皮肤类型III-V的37名人类志愿者中的验证表明，sO2估计的准确性、稳健性和生理一致性均有提高。通过实现实时、准确和可重复的组织氧合定量，GenMC解决了定量PA成像的一个关键障碍，并为组织中光传输的快速、高保真近似提供了一种通用策略。
英文摘要
Photoacoustic (PA) imaging provides molecular and functional information about tissue, such as blood oxygen saturation (sO2), yet its clinical translation is hindered by inaccurate quantification. A major source of error is the spectral colouring effect, in which wavelength-dependent optical attenuation distorts the local optical fluence. Monte Carlo (MC) simulation is the gold standard for modelling light transport, but its computational demand precludes real-time use. Here, GenMC is presented, a deep generative framework based on a conditional generative adversarial network that estimates optical fluence distributions from tissue anatomy and literature-derived optical properties, with anatomical priors obtained from co-registered ultrasound images. Trained on MC-generated synthetic datasets, GenMC produces high-fidelity fluence maps in under 30 ms per frame, a four-orders-of-magnitude speed-up over conventional MC simulation, and reaches peak signal-to-noise ratios of up to 36.24 dB in vivo, outperforming UNet and Pix2Pix baselines. Validation in blood-mimicking phantoms and in 37 human volunteers spanning Fitzpatrick skin types III-V demonstrates improved accuracy, robustness, and physiological consistency of sO2 estimation. By enabling real-time, accurate, and reproducible quantification of tissue oxygenation, GenMC addresses a critical barrier to quantitative PA imaging and offers a general strategy for rapid, high-fidelity approximation of light transport in tissue.
