联合估计磁场与图像,提升高b值高加速下的扩散MRI畸变校正精度。
Distortion-Corrected Diffusion MRI Using Rotated-View EPI and Joint Field-Map/Image Estimation with Gaussian Primitives

- 直接从k空间联合估计磁场和图像,避免中间重建误差
- 在高b值、高加速下边界对齐最接近无畸变参考图像
- 支持旋转视角EPI,增强畸变约束,提升细节保真度
回波平面成像(EPI)是扩散与功能脑成像的标准采集方法,虽实现快速成像,但受B0场不均匀性影响产生几何畸变。现有校正方法先通过并行成像重建畸变图像,再估计B0场并在图像域进行校正。该流程中,高加速因子下的重建伪影及高扩散b值时的低信噪比会劣化B0估计,限制整体校正质量。本文提出一种物理引导框架,直接从k空间联合估计B0场与无畸变图像,无需依赖并行成像的中间重建。图像与B0场均表示为嵌入磁共振物理前向模型的高斯基元叠加。显式连续参数化可同时捕捉平滑区域与组织边界,并支持旋转视角EPI而无需插值。扩散加权图像建模为实数且非负,相位信息整合为每段采集的相位因子。旋转视角使畸变分布于多个相位编码方向,改善点扩散函数各向同性,增强对B0场估计的约束。在活体脑扩散EPI数据上,所提方法在高b值与高加速条件下,与无畸变结构参考图像的脑边界对齐最为精确,相较顺序方法提升显著。大量视觉对比显示其具备更优细节保真度与噪声抑制能力。
原文摘要 · Abstract (English)
Echo Planar Imaging (EPI) is the standard acquisition technique for diffusion and functional neuroimaging, enabling rapid imaging but suffering from geometric distortions caused by B0 field inhomogeneities. Existing correction methods first reconstruct distorted images using parallel imaging, then estimate the B0 field and correct the distortion in the image domain. In this sequential process, reconstruction artifacts at high acceleration factors and low SNR at high diffusion b-values degrade B0 estimation and limit the overall correction quality. We propose a physics-informed framework that jointly estimates the B0 field and distortion-free image directly from k-space data, without depending on an intermediate parallel-imaging reconstruction for the correction. The image and the B0 field are each represented as a superposition of Gaussian primitives embedded within an MRI physics forward model. The explicit, continuous parameterization captures both smooth regions and tissue boundaries and supports rotated-view EPI acquisitions without interpolation. The diffusion-weighted image is modeled as real and non-negative, with the image phase absorbed into a per-shot phase factor. Rotated views distribute distortions across multiple phase-encoding orientations, improving point spread function isotropy and providing stronger constraints for B0 estimation. On in vivo brain diffusion EPI, the proposed method attains the closest brain-boundary agreement with a distortion-free structural reference, with the largest improvement over sequential methods at high b-value and high acceleration. Extensive visual comparisons further show improved detail fidelity and noise suppression.
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