当前位置:
X-MOL 学术
›
NMR Biomed.
›
论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
A novel bayesian approach with conditional autoregressive specification for intravoxel incoherent motion diffusion-weighted MRI.
NMR in Biomedicine ( IF 2.9 ) Pub Date : 2019-12-29 , DOI: 10.1002/nbm.4201 Ettore Lanzarone 1 , Alfonso Mastropietro 2, 3 , Elisa Scalco 2, 3 , Antonello Vidiri 4 , Giovanna Rizzo 2, 3
NMR in Biomedicine ( IF 2.9 ) Pub Date : 2019-12-29 , DOI: 10.1002/nbm.4201 Ettore Lanzarone 1 , Alfonso Mastropietro 2, 3 , Elisa Scalco 2, 3 , Antonello Vidiri 4 , Giovanna Rizzo 2, 3
Affiliation
The Intra-Voxel Incoherent Motion (IVIM) model is largely adopted to estimate slow and fast diffusion coefficients of water molecules in biological tissues, which are used in cancer applications. The most reported fitting approach is a voxel-wise segmented non-linear least square, whereas Bayesian approaches with a direct fit, also considering spatial regularization, were proposed too. In this work a novel segmented Bayesian method was proposed, also in combination with a spatial regularization through a Conditional Autoregressive (CAR) prior specification. The two segmented Bayesian approaches, with and without CAR specification, were compared with two standard least-square and a direct Bayesian fitting methods. All approaches were tested on simulated images and real data of patients with head-and-neck and rectal cancer. Estimation accuracy and maps noisiness were quantified on simulated images, whereas the coefficient of variation and the goodness of fit were evaluated for real data. Both versions of the segmented Bayesian approach outperformed the standard methods on simulated images for pseudo-diffusion (D∗ ) and perfusion fraction (f), whilst the segmented least-square fitting remained the less biased for the diffusion coefficient (D). On real data, Bayesian approaches provided the less noisy maps, and the two Bayesian methods without CAR generally estimated lower values for f and D∗ coefficients with respect to the other approaches. The proposed segmented Bayesian approaches were superior, in terms of estimation accuracy and maps quality, to the direct Bayesian model and the least-square fittings. The CAR method improved the estimation accuracy, especially for D∗ .
中文翻译:
一种新颖的具有条件自回归规范的贝叶斯方法,用于体素内非相干运动扩散加权MRI。
体内三维不相干运动(IVIM)模型被广泛用于估计生物组织中水分子的慢速扩散系数和快扩散系数,这些系数在癌症应用中使用。报道最多的拟合方法是按体素分割的非线性最小二乘,而同时也考虑了空间正则化的直接拟合的贝叶斯方法也被提出。在这项工作中,提出了一种新颖的分段贝叶斯方法,并且还与通过条件自回归(CAR)先验规范的空间正则化相结合。将两种分段的贝叶斯方法(具有和不具有CAR规范)与两种标准的最小二乘法和直接贝叶斯拟合方法进行了比较。所有方法均在头颈部和直肠癌患者的模拟图像和真实数据上进行了测试。在模拟图像上量化了估计准确性和地图噪声,而对真实数据则评估了变异系数和拟合优度。分割贝叶斯方法的两种版本均优于模拟图像的伪扩散(D ∗)和灌注分数(f)的标准方法,而分割最小二乘拟合仍然对扩散系数(D)的偏向较小。在实际数据上,贝叶斯方法提供的噪点较少,并且两种不使用CAR的贝叶斯方法通常估计相对于其他方法而言,f和D *系数的值较低。提出的分段贝叶斯方法在估计准确性和地图质量方面优于直接贝叶斯模型和最小二乘拟合。CAR方法提高了估计精度,
更新日期:2020-02-04
中文翻译:
一种新颖的具有条件自回归规范的贝叶斯方法,用于体素内非相干运动扩散加权MRI。
体内三维不相干运动(IVIM)模型被广泛用于估计生物组织中水分子的慢速扩散系数和快扩散系数,这些系数在癌症应用中使用。报道最多的拟合方法是按体素分割的非线性最小二乘,而同时也考虑了空间正则化的直接拟合的贝叶斯方法也被提出。在这项工作中,提出了一种新颖的分段贝叶斯方法,并且还与通过条件自回归(CAR)先验规范的空间正则化相结合。将两种分段的贝叶斯方法(具有和不具有CAR规范)与两种标准的最小二乘法和直接贝叶斯拟合方法进行了比较。所有方法均在头颈部和直肠癌患者的模拟图像和真实数据上进行了测试。在模拟图像上量化了估计准确性和地图噪声,而对真实数据则评估了变异系数和拟合优度。分割贝叶斯方法的两种版本均优于模拟图像的伪扩散(D ∗)和灌注分数(f)的标准方法,而分割最小二乘拟合仍然对扩散系数(D)的偏向较小。在实际数据上,贝叶斯方法提供的噪点较少,并且两种不使用CAR的贝叶斯方法通常估计相对于其他方法而言,f和D *系数的值较低。提出的分段贝叶斯方法在估计准确性和地图质量方面优于直接贝叶斯模型和最小二乘拟合。CAR方法提高了估计精度,