重建或生成：探索心脏MRI生成建模的谱系
Reconstruct or Generate: Exploring the Spectrum of Generative Modeling for Cardiac MRI
AI总结：
本文构建生成模型库，系统比较潜扩散模型与自回归模型在心脏MRI修复和无条件生成任务中的表现，揭示二者在感知质量、稳定性与保真度上的权衡。
摘要：
在医学成像中，生成模型越来越多地被用于两项不同但同样关键的任务：重建与生成。重建的目标是恢复医学图像，通常涉及图像修复或超分辨率等逆问题；生成则是创建合成数据，以扩充数据集或开展反事实分析。尽管二者共享架构和学习框架，但其优先目标不同：生成追求高感知质量和多样性，而重建注重数据保真度和忠实性。在本研究中，我们提出一个“generative model zoo（生成模型库）”，并系统分析现代潜扩散模型和自回归模型如何在重建—生成谱系中权衡。我们在具有代表性的心脏医学成像任务上对一组生成模型进行基准测试，重点关注不同掩蔽比例和采样策略下的图像修复，以及无条件图像生成。研究结果表明，扩散模型在无条件生成中提供更优的感知质量，但随着掩蔽比例增加往往会产生幻觉；相比之下，自回归模型在各掩蔽水平上保持稳定的感知表现，不过其保真度通常较低。
英文摘要：
In medical imaging, generative models are increasingly relied upon for two distinct but equally critical tasks: reconstruction, where the goal is to restore medical imaging (usually inverse problems like inpainting or superresolution), and generation, where synthetic data is created to augment datasets or carry out counterfactual analysis. Despite shared architecture and learning frameworks, they prioritize different goals: generation seeks high perceptual quality and diversity, while reconstruction focuses on data fidelity and faithfulness. In this work, we introduce a "generative model zoo" and systematically analyze how modern latent diffusion models and autoregressive models navigate the reconstruction-generation spectrum. We benchmark a suite of generative models across representative cardiac medical imaging tasks, focusing on image inpainting with varying masking ratios and sampling strategies, as well as unconditional image generation. Our findings show that diffusion models offer superior perceptual quality for unconditional generation but tend to hallucinate as masking ratios increase, whereas autoregressive models maintain stable perceptual performance across masking levels, albeit with generally lower fidelity.
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。
