Automatic segmentation of white matter lesions on multi-parametric MRI: convolutional neural network versus vision transformer.

Background And Purpose: White matter hyperintensities in brain MRI are key indicators of various neurological conditions, and their accurate segmentation is essential for assessing disease progression. This study aims to evaluate the performance of a 3D convolutional neural network and a 3D Transformer-based model for white matter hyperintensities segmentation, focusing on their efficacy with limited datasets and similar computational resources.

Materials And Methods: We implemented a convolution-based model (3D ResNet-50 U-Net with spatial and channel squeeze & excitation) and a Transformer-based model (3D Swin Transformer with a convolutional stem).

Functional quantitative susceptibility mapping (fQSM) of rat brain during flashing light stimulation.

Functional magnetic resonance imaging (fMRI) based on the blood oxygenation level-dependent (BOLD) contrast has become an indispensable tool in neuroscience. However, the BOLD signal is nonlocal, lacking quantitative measurement of oxygenation fluctuation. This preclinical study aimed to introduced functional quantitative susceptibility mapping (fQSM) to complement BOLD-fMRI to quantitatively assess the local susceptibility and venous oxygen saturation (SvO).