The traditional finite element method-based fluorescence molecular tomography (FMT)/ X-ray computed tomography (XCT) imaging reconstruction suffers from complicated mesh generation and dual-modality image data fusion, which limits the application of in vivo imaging. To solve this problem, a novel standardized imaging space reconstruction (SISR) method for the quantitative determination of fluorescent probe distributions inside small animals was developed. In conjunction with a standardized dual-modality image data fusion technology, and novel reconstruction strategy based on Laplace regularization and L1-fused Lasso method, the in vivo distribution can be calculated rapidly and accurately, which enables standardized and algorithm-driven data process. We demonstrated the method’s feasibility through numerical simulations and quantitatively monitored in vivo programmed death ligand 1 (PD-L1) expression in mouse tumor xenografts, and the results demonstrate that our proposed SISR can increase data throughput and reproducibility, which helps to realize the dynamically and accurately in vivo imaging.

中文翻译：

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基于标准化成像空间的快速自动化FMT/XCT重建策略

传统的基于有限元方法的荧光分子断层扫描（FMT）/X射线计算机断层扫描（XCT）成像重建存在复杂的网格生成和双模态图像数据融合等问题，限制了该技术的应用。体内成像。为了解决这个问题，开发了一种新的标准化成像空间重建（SISR）方法，用于定量测定小动物体内的荧光探针分布。结合标准化的双模态图像数据融合技术，以及基于拉普拉斯正则化和 L1 融合 Lasso 方法的新型重建策略，体内分布可以快速准确地计算出来，实现标准化和算法驱动的数据处理。我们通过数值模拟和定量监测证明了该方法的可行性体内程序性死亡配体 1 (PD-L1) 在小鼠肿瘤异种移植物中的表达，结果表明我们提出的 SISR 可以提高数据吞吐量和可重复性，有助于实现动态和准确体内成像。