CoCoLIT：基于ControlNet条件控制的潜在图像翻译用于MRI到淀粉样蛋白PET合成
CoCoLIT: ControlNet-Conditioned Latent Image Translation for MRI to Amyloid PET Synthesis
AI总结：
针对3D神经影像数据高维度与结构复杂性对MRI到淀粉样蛋白PET翻译的挑战，提出基于扩散的潜在生成框架CoCoLIT，通过加权图像空间损失、潜在平均稳定化分析及ControlNet条件控制，显著提升合成质量与分类性能。
摘要：
从更广泛可用且易获取的结构MRI模态合成淀粉样蛋白PET扫描，为大规模阿尔茨海默病（AD）筛查提供了一种有前景的、具成本效益的方法。这一研究的动机是有证据表明，尽管MRI无法直接检测淀粉样蛋白病理，但它可能编码了与淀粉样蛋白沉积相关的信息，这些信息可通过先进建模揭示。然而，3D神经影像数据的高维度和结构复杂性对现有MRI到PET的翻译方法构成了重大挑战。在低维潜在空间中建模跨模态关系可简化学习任务并实现更有效的翻译。为此，我们提出CoCoLIT（ControlNet条件控制的潜在图像翻译），这是一种基于扩散的潜在生成框架，包含三项主要创新：（1）一种新颖的加权图像空间损失（WISL），可改善潜在表示学习和合成质量；（2）对潜在平均稳定化（LAS）的理论与实证分析，LAS是类似生成模型中用于增强推理一致性的现有技术；（3）引入基于ControlNet的条件控制用于MRI到PET的翻译。我们在公开可用数据集上评估CoCoLIT的性能，发现我们的模型在基于图像的指标和淀粉样蛋白相关指标上均显著优于现有最佳方法。值得注意的是，在淀粉样蛋白阳性分类中，CoCoLIT优于第二优方法，在内部数据集上提升了+10.5%，在外部数据集上提升了+23.7%。我们的方法的代码和模型可在https://github.com/brAIn-science/CoCoLIT获取。
英文摘要：
Synthesizing amyloid PET scans from the more widely available and accessible structural MRI modality offers a promising, cost-effective approach for large-scale Alzheimer's Disease (AD) screening. This is motivated by evidence that, while MRI does not directly detect amyloid pathology, it may nonetheless encode information correlated with amyloid deposition that can be uncovered through advanced modeling. However, the high dimensionality and structural complexity of 3D neuroimaging data pose significant challenges for existing MRI-to-PET translation methods. Modeling the cross-modality relationship in a lower-dimensional latent space can simplify the learning task and enable more effective translation. As such, we present CoCoLIT (ControlNet-Conditioned Latent Image Translation), a diffusion-based latent generative framework that incorporates three main innovations: (1) a novel Weighted Image Space Loss (WISL) that improves latent representation learning and synthesis quality; (2) a theoretical and empirical analysis of Latent Average Stabilization (LAS), an existing technique used in similar generative models to enhance inference consistency; and (3) the introduction of ControlNet-based conditioning for MRI-to-PET translation. We evaluate CoCoLIT's performance on publicly available datasets and find that our model significantly outperforms state-of-the-art methods on both image-based and amyloid-related metrics. Notably, in amyloid-positivity classification, CoCoLIT outperforms the second-best method with improvements of +10.5% on the internal dataset and +23.7% on the external dataset. The code and models of our approach are available at https://github.com/brAIn-science/CoCoLIT.
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。
