文献类型： 会议论文
第一作者： Emmanuel Vallee
作者： Emmanuel Vallee 1 ; Wenchuan Wu 1 ; Francesca Galassi 2 ; Saad Jbabdi 1 ; Stephen Smith 1 ;
作者机构： 1.FMRIB Analysis Group, NDCN, University of Oxford
2.INRIA, CNRS UMR6074, VISAGES: INSERM U1228, University of Rennes I
关键词： Diffusion MRI;Multi-compartment models;Machine learning
会议名称： International Workshop on Simulation and Synthesis in Medical Imaging
主办单位：
页码： 42-51
摘要： Tissue-type partial volume modelling is generally an ill-posed problem in single-shell diffusion MRI. On the other hand, T1w images are typically acquired along with the diffusion data, and allow for an accurate estimation of the tissue partial volume fractions (PVFs). We propose in this paper to compare different data driven approach to predict the T1w-derived PVFs from the diffusion data. The aim is to alleviate the within subject mis-registration between the two modalities. Our experiments show that the random forests is the most accurate and scalable method for predicting the tissue partial volume fractions. Additionally, such predictions can be used to inform the fitting of the two-compartment model to retrieve a diffusion tensor that is not biased by partial volume effects or constraints.
分类号： TP391.41-53
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