Diffusion models demonstrate state-of-the-art performance on image generation, and are gaining traction for sparse medical image reconstruction tasks. However, compared to classical reconstruction algorithms relying on simple analytical priors, diffusion models have the dangerous property of producing realistic looking results \emph{even when incorrect}, particularly with few observations. We investigate the utility of diffusion models as priors for image reconstruction by varying the number of observations and comparing their performance to classical priors (sparse and Tikhonov regularization) using pixel-based, structural, and downstream metrics. We make comparisons on low-dose chest wall computed tomography (CT) for fat mass quantification. First, we find that classical priors are superior to diffusion priors when the number of projections is ``sufficient''. Second, we find that diffusion priors can capture a large amount of detail with very few observations, significantly outperforming classical priors. However, they fall short of capturing all details, even with many observations. Finally, we find that the performance of diffusion priors plateau after extremely few ($\approx$10-15) projections. Ultimately, our work highlights potential issues with diffusion-based sparse reconstruction and underscores the importance of further investigation, particularly in high-stakes clinical settings.

翻译：扩散模型在图像生成领域展现出最先进的性能，并逐渐应用于稀疏医学图像重建任务。然而，与依赖简单解析先验的传统重建算法相比，扩散模型具有一个危险特性：即使在结果不正确时，仍可能生成视觉效果逼真的输出，这在观测数据极少时尤为明显。本研究通过改变观测数量，并采用像素级、结构性和下游任务指标，将扩散模型作为图像重建先验的性能与经典先验（稀疏正则化和Tikhonov正则化）进行对比。我们在用于脂肪量定量的低剂量胸壁计算机断层扫描（CT）数据上进行实验比较。首先，我们发现当投影数量"充足"时，经典先验优于扩散先验。其次，我们发现扩散先验能够在极少量观测下捕捉大量细节，显著超越经典先验。然而，即使观测数据较多，扩散先验仍无法完全捕获所有细节。最后，我们发现扩散先验的性能在极少量（约10-15个）投影后即达到平台期。本研究揭示了基于扩散的稀疏重建方法存在的潜在问题，并强调了在高风险临床环境中进一步深入研究的必要性。