用于快速张量值扩散MRI的累积量展开旋转不变量（RICE）的约束估计
Constrained estimation of rotational invariants of the cumulant expansion (RICE) for rapid tensor-valued diffusion MRI
- German Cancer Research Center (DKFZ)（德国癌症研究中心）
- Heidelberg University（海德堡大学）
- Uniklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)（埃尔朗根大学附属综合医院，弗里德里希·亚历山大·埃尔朗根-纽伦堡大学）
- University Hospital Heidelberg（海德堡大学附属医院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出用约束加权线性最小二乘（CWLLS）稳定快速累积量展开旋转不变量（RICE）的拟合，其速度比约束q空间轨迹成像（QTI）快160倍，可生成高质量dMRI参数图谱，提升组织表征可靠性。
AI中文摘要：
目的：通过快速约束拟合补充常见张量值扩散MRI（dMRI）标记物的1.5分钟测量。方法：将获取累积量展开旋转不变量（RICE）的快速dMRI协议与约束加权线性最小二乘（CWLLS）配对，以稳定更脆弱的加权线性最小二乘（WLLS）拟合。构建了紧凑约束集，包括总扩散方差的新型均值相关上界。评估使用扩散张量分布（DTD）模拟、具有分辨率依赖SNR实验的健康志愿者数据以及神经胶质瘤患者数据集；采用5分钟q空间轨迹成像（QTI）协议作为参考。结果：在所有实验中，CWLLS减少了微观各向异性分数（FA）、各向同性扩散方差等参数的非物理解和拟合异常值。模拟中，其在以脑脊液（CSF）为主的病例中最明显地缩小了误差分布，而部分指标存在偏差-方差权衡。体内实验中，CWLLS消除了负方差估计，截断了超出范围的尾部，减少了受流体污染体素中的伪影，同时保留了解剖对比度；在更高分辨率下，其生成的图谱比WLLS更稳定，尽管两种估计器在最低SNR设置下均性能下降。值得注意的是，患者数据集中有15.4%的体素违反了新型均值相关方差约束，占违反至少一项约束的体素总数（32.7%）的近一半。健康志愿者基准测试显示，CWLLS的计算时间不到30秒，而约束QTI拟合需72分钟，CWLLS速度快160倍。结论：用于快速RICE的CWLLS以可在线使用的计算成本生成了高质量参数图谱，可提高dMRI组织表征的可靠性，推动其临床转化。
英文摘要：
Purpose: To complement 1.5-minute measurements of common tensor-valued diffusion MRI (dMRI) markers with rapid constrained fitting. Methods: Fast dMRI protocols for obtaining rotational invariants of the cumulant expansion (RICE) were paired with constrained weighted linear least squares (CWLLS) to stabilize the more fragile WLLS fit. A compact constraint set was formulated, including a novel mean-dependent upper bound on total diffusional variance. Evaluation used diffusion tensor distribution (DTD) simulations, healthy-volunteer data with a resolution-dependent SNR experiment, and a glioma patient dataset. A 5-minute q-space trajectory imaging (QTI) protocol served as a reference. Results: Across experiments, CWLLS reduced unphysical estimates and fit outliers in parameters such as microscopic FA and isotropic diffusivity variance. In simulations, it narrowed error distributions most clearly in the CSF-dominant case, while some metrics showed a bias-variance trade-off. In vivo, CWLLS removed negative variance estimates, truncated out-of-bounds tails, and reduced artifacts in fluid-contaminated voxels while preserving anatomical contrast. It also retained more stable maps than WLLS at higher resolution, although both estimators degraded in the lowest-SNR setting. Notably, the new mean-dependent variance bound was violated in 15.4% of voxels in the patient dataset, accounting for nearly half of the 32.7% that violated at least one constraint. Healthy-volunteer benchmarking showed that CWLLS completed in under 30 seconds. The constrained QTI fit required 72 minutes, making CWLLS 160 times faster. Conclusion: CWLLS for fast RICE yielded high-quality parameter maps at an online-ready computational cost. This may enhance the reliability of dMRI tissue characterization and strengthen the path toward clinical translation.
