当前位置： SCI文献检索 > MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING期刊下所有文献 > A machine learning approach for magnetic resonance image-based mouse brain modeling and fast computation in controlled cortical impact.

A machine learning approach for magnetic resonance image-based mouse brain modeling and fast computation in controlled cortical impact.

Abstract：

:Computational modeling of the brain is crucial for the study of traumatic brain injury. An anatomically accurate model with refined details could provide the most accurate computational results. However, computational models with fine mesh details could take prolonged computation time that impedes the clinical translation of the models. Therefore, a way to construct a model with low computational cost while maintaining a computational accuracy comparable with that of the high-fidelity model is desired. In this study, we constructed magnetic resonance (MR) image-based finite element (FE) models of a mouse brain for simulations of controlled cortical impact. The anatomical details were kept by mapping each image voxel to a corresponding FE mesh element. We constructed a super-resolution neural network that could produce computational results of a refined FE model with a mesh size of 70 μm from a coarse FE model with a mesh size of 280 μm. The peak signal-to-noise ratio of the reconstructed results was 33.26 dB, while the computational speed was increased by 50-fold. This proof-of-concept study showed that using machine learning techniques, MR image-based computational modeling could be applied and evaluated in a timely fashion. This paved ways for fast FE modeling and computation based on MR images. Results also support the potential clinical applications of MR image-based computational modeling of the human brain in a variety of scenarios such as brain impact and intervention.Graphical abstract MR image-based FE models with different mesh sizes were generated for CCI. The training and testing data sets were computed with 5 different impact locations and 3 different impact velocities. High-resolution strain maps were estimated using a SR neural network with greatly reduced computational cost.

journal_name

Med Biol Eng Comput

journal_title

Medical & biological engineering & computing

authors

Lai C,Chen Y,Wang T,Liu J,Wang Q,Du Y,Feng Y

doi

10.1007/s11517-020-02262-1

subject

Has Abstract

pub_date

2020-11-01 00:00:00

pages

2835-2844

issue

eissn

0140-0118

issn

1741-0444

pii

10.1007/s11517-020-02262-1

journal_volume

pub_type

杂志文章

在线工具

A machine learning approach for magnetic resonance image-based mouse brain modeling and fast computation in controlled cortical impact.