Magnetic resonance (MR) images collected in 2D scanning protocols typically have large inter-slice spacing, resulting in high in-plane resolution but reduced through-plane resolution. Super-resolution techniques can reduce the inter-slice spacing of 2D scanned MR images, facilitating the downstream visual experience and computer-aided diagnosis. However, most existing super-resolution methods are trained at a fixed scaling ratio, which is inconvenient in clinical settings where MR scanning may have varying inter-slice spacings. To solve this issue, we propose Hierarchical Feature Conditional Diffusion (HiFi-Diff)} for arbitrary reduction of MR inter-slice spacing. Given two adjacent MR slices and the relative positional offset, HiFi-Diff can iteratively convert a Gaussian noise map into any desired in-between MR slice. Furthermore, to enable fine-grained conditioning, the Hierarchical Feature Extraction (HiFE) module is proposed to hierarchically extract conditional features and conduct element-wise modulation. Our experimental results on the publicly available HCP-1200 dataset demonstrate the high-fidelity super-resolution capability of HiFi-Diff and its efficacy in enhancing downstream segmentation performance.

翻译：二维扫描协议采集的磁共振图像通常具有较大的层间距，导致平面内分辨率较高而穿透平面分辨率降低。超分辨率技术可缩减二维扫描MRI图像的层间距，有助于提升下游视觉体验和计算机辅助诊断。然而，现有超分辨率方法大多在固定缩放比下训练，难以适应临床场景中MRI扫描层间距多变的情况。针对此问题，我们提出层级特征条件扩散模型（HiFi-Diff），用于任意缩减MRI层间距。基于相邻两个MRI切片及其相对位置偏移，HiFi-Diff可将高斯噪声图迭代转换为任意所需的中间MRI切片。此外，为实现细粒度条件控制，我们设计了层级特征提取（HiFE）模块，通过分层提取条件特征并执行逐元素调制。在公开HCP-1200数据集上的实验结果表明，HiFi-Diff具有高保真超分辨率能力，并能有效提升下游分割性能。