Technical Note: Simultaneous segmentation and relaxometry for MRI through multitask learning.

ABSTRACT:

Purpose

This study demonstrated a magnetic resonance (MR) signal multitask learning method for three-dimensional (3D) simultaneous segmentation and relaxometry of human brain tissues.

Materials and methods

A 3D inversion-prepared balanced steady-state free precession sequence was used for acquiring in vivo multicontrast brain images. The deep neural network contained three residual blocks, and each block had 8 fully connected layers with sigmoid activation, layer norm, and 256 neurons in each layer. Online-synthesized MR signal evolutions and labels were used to train the neural network batch-by-batch. Empirically defined ranges of T1 and T2 values for the normal gray matter, white matter, and cerebrospinal fluid (CSF) were used as the prior knowledge. MRI brain experiments

SUBMITTER: Cao P 

PROVIDER: S-EPMC6800607 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature