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

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