扩散MRI数据的空间正则化超分辨约束球面反卷积（SR$^2$-CSD）
Spatially Regularized Super-Resolved Constrained Spherical Deconvolution (SR$^2$-CSD) of Diffusion MRI Data
AI总结：
本文提出SR$^2$-CSD方法，通过自校准全变分去噪器估计的空间FOD先验正则化Super-CSD，在四个数据集上验证其能降低角度误差、改善空间相干性、增强重测可重复性并提高纤维追踪准确性。
摘要：
约束球面反卷积（CSD）被广泛用于从扩散MRI数据中估计白质纤维取向分布（FOD）。其角度分辨率取决于最大球谐阶数（$l_{max}$）：低$l_{max}$产生平滑但分辨率差的FOD，而高$l_{max}$（如Super-CSD）能分辨小角度的纤维交叉，但增加对噪声的敏感性。在这项概念验证研究中，我们引入了空间正则化超分辨CSD（SR$^2$-CSD），这是一种通过自校准全变分去噪器估计的空间FOD先验来正则化Super-CSD的新方法。我们在四个数据集上评估了SR$^2$-CSD与CSD和Super-CSD的表现：(i)HARDI-2013挑战数值体模，评估多种信噪比（SNR）水平和CSD变体（单/多壳层，单/多组织）下的角度和峰值数误差；(ii)Sherbrooke活体数据集，评估FOD的空间相干性；(iii)六名受试者的重测数据集，使用完整（96个梯度方向）和子采样（45个方向）协议采集，评估可重复性；(iv)DiSCo体模，评估不同SNR水平和多次噪声重复下的纤维追踪准确性。在所有评估中，SR$^2$-CSD持续降低了角度和峰值数误差，改善了空间相干性，增强了重测可重复性，并产生了与真实值相关性更强的连接矩阵。在多重比较校正下，大多数改进具有统计学显著性。这些结果表明，将空间先验纳入CSD是可行的，能减轻估计不稳定性，并提高FOD重建精度。
英文摘要：
Constrained Spherical Deconvolution (CSD) is widely used to estimate the white matter fiber orientation distribution (FOD) from diffusion MRI data. Its angular resolution depends on the maximum spherical harmonic order ($l_{max}$): low $l_{max}$ yields smooth but poorly resolved FODs, while high $l_{max}$, as in Super-CSD, enables resolving fiber crossings with small inter-fiber angles but increases sensitivity to noise. In this proof-of-concept study, we introduce Spatially Regularized Super-Resolved CSD (SR$^2$-CSD), a novel method that regularizes Super-CSD using a spatial FOD prior estimated via a self-calibrated total variation denoiser. We evaluated SR$^2$-CSD against CSD and Super-CSD across four datasets: (i) the HARDI-2013 challenge numerical phantom, assessing angular and peak number errors across multiple signal-to-noise ratio (SNR) levels and CSD variants (single-/multi-shell, single-/multi-tissue); (ii) the Sherbrooke in vivo dataset, evaluating spatial coherence of FODs; (iii) a six-subject test-retest dataset acquired with both full (96 gradient directions) and subsampled (45 directions) protocols, assessing reproducibility; and (iv) the DiSCo phantom, evaluating tractography accuracy under varying SNR levels and multiple noise repetitions. Across all evaluations, SR$^2$-CSD consistently reduced angular and peak number errors, improved spatial coherence, enhanced test-retest reproducibility, and yielded connectivity matrices more strongly correlated with ground-truth. Most improvements were statistically significant under multiple-comparison correction. These results demonstrate that incorporating spatial priors into CSD is feasible, mitigates estimation instability, and improves FOD reconstruction accuracy.
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