Super-resolution is widely used in medical imaging to enhance low-quality data, reducing scan time and improving abnormality detection. Conventional super-resolution approaches typically rely on paired datasets of downsampled and original high resolution images, training models to reconstruct high resolution images from their artificially degraded counterparts. However, in real-world clinical settings, low resolution data often arise from acquisition mechanisms that differ significantly from simple downsampling. As a result, these inputs may lie outside the domain of the training data, leading to poor model generalization due to domain shift. To address this limitation, we propose a distributional deep learning framework that improves model robustness and domain generalization. We develop this approch for enhancing the resolution of 4D Flow MRI (4DF). This is a novel imaging modality that captures hemodynamic flow velocity and clinically relevant metrics such as vessel wall stress. These metrics are critical for assessing aneurysm rupture risk. Our model is initially trained on high resolution computational fluid dynamics (CFD) simulations and their downsampled counterparts. It is then fine-tuned on a small, harmonized dataset of paired 4D Flow MRI and CFD samples. We derive the theoretical properties of our distributional estimators and demonstrate that our framework significantly outperforms traditional deep learning approaches through real data applications. This highlights the effectiveness of distributional learning in addressing domain shift and improving super-resolution performance in clinically realistic scenarios.

翻译：超分辨率技术在医学成像中被广泛用于提升低质量数据，从而减少扫描时间并改善异常检测。传统的超分辨率方法通常依赖于下采样图像与原始高分辨率图像构成的配对数据集，训练模型从人工降质图像中重建高分辨率图像。然而，在实际临床场景中，低分辨率数据往往源自与简单下采样显著不同的采集机制。因此，这些输入可能超出训练数据的分布范围，导致模型因域偏移而泛化能力下降。为克服这一局限，我们提出了一种分布深度学习框架，以提升模型的鲁棒性与域泛化能力。我们将该方法应用于增强4D Flow MRI（4DF）的分辨率。4DF是一种新型成像模态，能够捕捉血流动力学流速及血管壁应力等临床相关指标，这些指标对于评估动脉瘤破裂风险至关重要。我们的模型首先在高分辨率计算流体动力学（CFD）模拟数据及其下采样版本上进行训练，随后在少量经过协调的4D Flow MRI与CFD配对样本数据集上进行微调。我们推导了分布估计器的理论性质，并通过实际数据应用证明，该框架显著优于传统深度学习方法。这凸显了分布学习在应对域偏移及提升临床现实场景中超分辨率性能方面的有效性。