Robust automated preclinical fMRI preprocessing via a multi-stage dilated convolutional Swin Transformer affine...

Accurate preprocessing of functional magnetic resonance imaging (fMRI) data is crucial for effective analysis in preclinical studies. Key steps such as denoising, skull-stripping, and affine registration are essential to align fMRI data with a standard atlas. However, challenges such as low resolution, variations in brain geometry, and limited dataset sizes often hinder the performance of traditional and deep learning-based methods.

To address these challenges, we propose a preclinical fMRI preprocessing pipeline that integrates advanced deep learning modules, with a particular focus on a newly developed Swin Transformer-based affine registration method. The pipeline incorporates our previously established modules for 3D Generative Adversarial Network (GAN)-based denoising and Transformer-based skull stripping, followed by the proposed Multi-stage Dilated Convolutional Swin Transformer (MsDCSwinT) for affine registration. This new registration method captures both local and global spatial misalignments, ensuring accurate alignment with a standard atlas even in challenging preclinical datasets.

We validate the pipeline across multiple preclinical fMRI studies and demonstrate that our affine registration module achieves higher average Dice similarity coefficients compared to state-of-the-art methods.

By leveraging GANs and Transformers, our pipeline offers a robust, accurate, and fully automated solution for preclinical fMRI.