面向多对比度MRI任意尺度超分辨率的解剖引导自适应各向异性高斯泼溅
Adaptive Anisotropic Gaussian Splatting for Multi-contrast MRI Arbitrary-Scale Super-Resolution with Anatomy Guidance
把各向异性2D高斯核参数化引入MRI任意尺度超分辨率，用可学习的高斯中心与协方差替代INR的连续映射，绕开频谱偏置导致的高频解剖边界丢失；其中基于梯度稀疏注意力的高斯参数化器与双域跨注意力模块可迁移到其他需要高频细节恢复的医学图像复原任务。
原文依据 converts the challenging task of HF anatomical reconstruction into a smoother parameter optimization problem by learning the parameters of anisotropic 2D Gaussian kernels
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论基于隐式神经表示（INR）的方法通过学习从低分辨率（LR）目标磁共振（MR）图像与高分辨率（HR）参考图像到连续映射，实现任意尺度超分辨率（SR）。然而，其固有的频谱偏置倾向于学习低频（LF）成分，往往无法捕捉解剖边界处的锐利跃变，导致高频（HF）细节丢失。受3D高斯泼溅启发，我们提出GaussM2ASR（高斯多对比度MRI任意尺度超分辨率），通过学习各向异性2D高斯核的参数，将困难的高频解剖重建任务转化为更平滑的参数优化问题。为处理对比度间差异，我们引入一条解剖引导流程，包含三个核心模块：用于特征增强的结构先验调制融合（SPMF）模块；用于空间-频率联合建模的解剖引导双域跨注意力（AG-DDCA）模块；以及利用基于梯度的稀疏注意力将高斯中心集中于关键解剖结构的解剖引导高斯参数化器（AGGP）。在多个数据集上的大量实验表明，GaussM2ASR在恢复精细解剖细节方面优于最优方法。源代码已发布于 https://github.com/Qiuhai-CV/GaussM2ASR。
英文原文摘要 1425 字符
Implicit neural representation (INR) based methods learn a continuous mapping from a low-resolution (LR) target magnetic resonance (MR) image and a high-resolution (HR) reference image to achieve arbitrary-scale super-resolution (SR). However, their inherent spectral bias favors learning low-frequency (LF) components, often failing to capture the sharp transitions at anatomical boundaries and resulting in the loss of high-frequency (HF) details. Inspired by 3D Gaussian splatting, we propose GaussM2ASR (Gaussian Multi-contrast MRI Arbitrary-scale Super-Resolution), which converts the challenging task of HF anatomical reconstruction into a smoother parameter optimization problem by learning the parameters of anisotropic 2D Gaussian kernels. To handle inter-contrast discrepancies, we introduce an anatomy-guided pipeline comprising three core modules: a Structure Prior Modulation Fusion (SPMF) module for feature enhancement; an Anatomy-Guided Dual-Domain Cross Attention (AG-DDCA) module for joint spatial-frequency modeling; and an Anatomy-Guided Gaussian Parametrizer (AGGP) that leverages gradient-based sparse attention to concentrate Gaussian centers on critical anatomical structures. Extensive experiments on multiple datasets demonstrate that GaussM2ASR surpasses state-of-the-art methods in recovering fine anatomical details. Our source codes have been released at https://github.com/Qiuhai-CV/GaussM2ASR.