Pontabry J, Rousseau F, Studholme C, Koob M, Dietemann J-L
Institute for Epigenetics and Stem cells, Helmholtz Zentrum München, Germany.
Institut Mines Télécom, Télécom Bretagne, INSERM, LaTIM U1101, Brest, France.
Med Image Anal. 2017 Jan;35:313-326. doi: 10.1016/j.media.2016.07.005. Epub 2016 Jul 25.
The development of post-processing reconstruction techniques has opened new possibilities for the study of in-utero fetal brain MRI data. Recent cortical surface analysis have led to the computation of quantitative maps characterizing brain folding of the developing brain. In this paper, we describe a novel feature selection-based approach that is used to extract the most discriminative and sparse set of features of a given dataset. The proposed method is used to sparsely characterize cortical folding patterns of an in-utero fetal MR dataset, labeled with heterogeneous gestational age ranging from 26 weeks to 34 weeks. The proposed algorithm is validated on a synthetic dataset with both linear and non-linear dynamics, supporting its ability to capture deformation patterns across the dataset within only a few features. Results on the fetal brain dataset show that the temporal process of cortical folding related to brain maturation can be characterized by a very small set of points, located in anatomical regions changing across time. Quantitative measurements of growth against time are extracted from the set selected features to compare multiple brain regions (e.g. lobes and hemispheres) during the considered period of gestation.
后处理重建技术的发展为子宫内胎儿脑磁共振成像（MRI）数据的研究开辟了新的可能性。最近的皮质表面分析已实现对发育中大脑的脑折叠特征进行定量图谱计算。在本文中，我们描述了一种基于特征选择的新方法，该方法用于提取给定数据集最具判别力且稀疏的特征集。所提出的方法用于稀疏表征子宫内胎儿MR数据集的皮质折叠模式，该数据集的孕周范围从26周至34周不等。所提出的算法在具有线性和非线性动力学的合成数据集上得到验证，这支持了其仅通过少量特征就能捕捉整个数据集变形模式的能力。胎儿脑数据集的结果表明，与大脑成熟相关的皮质折叠的时间过程可以由位于随时间变化的解剖区域中的非常少量的点来表征。从所选特征集中提取随时间的生长定量测量值，以比较所考虑妊娠期内的多个脑区（例如脑叶和半球）。
