Jalnefjord Oscar, Andersson Mats, Montelius Mikael, Starck Göran, Elf Anna-Karin, Johanson Viktor, Svensson Johanna, Ljungberg Maria
Department of Radiation Physics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden.
MAGMA. 2018 Dec;31(6):715-723. doi: 10.1007/s10334-018-0697-5. Epub 2018 Aug 16.
Intravoxel incoherent motion (IVIM) shows great potential in many applications, e.g., tumor tissue characterization. To reduce image-quality demands, various IVIM analysis approaches restricted to the diffusion coefficient (D) and the perfusion fraction (f) are increasingly being employed. In this work, the impact of estimation approach for D and f is studied.
Four approaches for estimating D and f were studied: segmented IVIM fitting, least-squares fitting of a simplified IVIM model (sIVIM), and Bayesian fitting of the sIVIM model using marginal posterior modes or posterior means. The estimation approaches were evaluated in terms of bias and variability as well as ability for differentiation between tumor and healthy liver tissue using simulated and in vivo data.
All estimation approaches had similar variability and ability for differentiation and negligible bias, except for the Bayesian posterior mean of f, which was substantially biased. Combined use of D and f improved tumor-to-liver tissue differentiation compared with using D or f separately.
The similar performance between estimation approaches renders the segmented one preferable due to lower numerical complexity and shorter computational time. Superior tissue differentiation when combining D and f suggests complementary biologically relevant information.
体素内不相干运动（IVIM）在许多应用中显示出巨大潜力，例如肿瘤组织特征分析。为降低对图像质量的要求，越来越多地采用各种仅限于扩散系数（D）和灌注分数（f）的IVIM分析方法。在本研究中，探讨了D和f估计方法的影响。
研究了四种估计D和f的方法：分段IVIM拟合、简化IVIM模型（sIVIM）的最小二乘拟合以及使用边际后验模式或后验均值的sIVIM模型的贝叶斯拟合。使用模拟数据和体内数据，从偏差、变异性以及区分肿瘤与健康肝组织的能力方面对估计方法进行评估。
所有估计方法具有相似的变异性和区分能力，偏差可忽略不计，但f的贝叶斯后验均值存在明显偏差。与单独使用D或f相比，联合使用D和f可改善肿瘤与肝组织的区分。
由于数值复杂度较低和计算时间较短，分段估计方法在性能上与其他方法相似，因此更为可取。联合使用D和f时具有更好的组织区分能力，表明存在互补的生物学相关信息。
