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首页> 外文期刊>Medical image analysis >Reliable estimation of incoherent motion parametric maps from diffusion-weighted MRI using fusion bootstrap moves
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Reliable estimation of incoherent motion parametric maps from diffusion-weighted MRI using fusion bootstrap moves

机译：使用融合自举移动从扩散加权MRI可靠估计非相干运动参数图

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Diffusion-weighted MRI has the potential to provide important new insights into physiological and microstructural properties of the body. The Intra-Voxel Incoherent Motion (IVIM) model relates the observed DW-MRI signal decay to parameters that reflect blood flow in the capillaries (D*), capillaries volume fraction (f), and diffusivity (D). However, the commonly used, independent voxel-wise fitting of the IVIM model leads to imprecise parameter estimates, which has hampered their practical usage.In this work, we improve the precision of estimates by introducing a spatially-constrained Incoherent Motion (IM) model of DW-MRI signal decay. We also introduce an efficient iterative " fusion bootstrap moves" (FBM) solver that enables precise parameter estimates with this new IM model. This solver updates parameter estimates by applying a binary graph-cut solver to fuse the current estimate of parameter values with a new proposal of the parameter values into a new estimate of parameter values that better fits the observed DW-MRI data. The proposals of parameter values are sampled from the independent voxel-wise distributions of the parameter values with a model-based bootstrap resampling of the residuals.We assessed both the improvement in the precision of the incoherent motion parameter estimates and the characterization of heterogeneous tumor environments by analyzing simulated and in vivo abdominal DW-MRI data of 30 patients, and in vivo DW-MRI data of three patients with musculoskeletal lesions. We found our IM-FBM reduces the relative root mean square error of the D* parameter estimates by 80%, and of the f and D parameter estimates by 50% compared to the IVIM model with the simulated data. Similarly, we observed that our IM-FBM method significantly reduces the coefficient of variation of parameter estimates of the D* parameter by 43%, the f parameter by 37%, and the D parameter by 17% compared to the IVIM model (paired Student's t-test, p0.0001). In addition, we found our IM-FBM method improved the characterization of heterogeneous musculoskeletal lesions by means of increased contrast-to-noise ratio of 19.3%.The IM model and FBM solver combined, provide more precise estimate of the physiological model parameter values that describing the DW-MRI signal decay and a better mechanism for characterizing heterogeneous lesions than does the independent voxel-wise IVIM model.
机译：扩散加权MRI有可能为人体的生理和微结构特性提供重要的新见解。体内部不相干运动（IVIM）模型将观察到的DW-MRI信号衰减与反映毛细血管（D *），毛细血管体积分数（f）和扩散率（D）中的血流的参数相关。但是，IVIM模型的常用，独立的体素方向拟合导致参数估计不精确，从而妨碍了其实际使用。在这项工作中，我们通过引入空间约束的非相干运动（IM）模型来提高估计的精度。 DW-MRI信号衰减。我们还将介绍一种有效的迭代“融合自举移动”（FBM）求解器，该求解器可以使用此新IM模型进行精确的参数估计。该求解器通过应用二进制图割求解器将参数值的当前估算值与新的参数值建议融合为新的参数值估算值来更新参数估算值，以更好地拟合观察到的DW-MRI数据。通过对残差进行基于模型的自举重采样，从参数值的独立体素方向分布中采样参数值的建议，我们评估了非相干运动参数估计精度的提高以及异质性肿瘤环境的表征通过分析30例患者的模拟和体内腹部DW-MRI数据以及3例肌肉骨骼病变患者的体内DW-MRI数据。我们发现，与具有模拟数据的IVIM模型相比，我们的IM-FBM将D *参数估计的相对均方根误差降低了80％，将f和D参数估计的相对均方根误差降低了50％。类似地，我们观察到，与IVIM模型相比，我们的IM-FBM方法将D *参数的参数估计值的变异系数显着降低了43％，f参数的参数估计值的变异系数降低了37％，D参数的参数估计值降低了17％ t检验，p <0.0001）。此外，我们发现我们的IM-FBM方法通过增加19.3％的对比噪声比改善了异质性肌肉骨骼病变的表征.IM模型和FBM求解器相结合，可以更准确地估算生理模型参数值，从而描述了DW-MRI信号衰减，以及比独立体素式IVIM模型更好的表征异质性病变的机制。

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