MRI Motion Artefacts Simulation and Correction using Complex-Valued Deep Learning

Magnetic resonance imaging (MRI) has become an indispensable tool in modern medicine, providing unparalleled soft tissue contrast for diagnosis and treatment planning. However, MRI scans are notoriously sensitive to patient motion, which can severely degrade image quality. We set out to tackle this long-standing challenge by applying state-of-the-art deep learning techniques directly on raw k-space MRI data.

We were eager to test novel deep network architectures for simultaneous motion correction and high-fidelity image reconstruction. Our work leveraged the massive open-source fastMRI dataset from Facebook AI Research & NYU, comprising over 10,000 raw brain MRI scans, as training data. We modified the TorchIO library to simulate realistic motion artifacts on the raw complex k-space by applying randomized elastic transformations. This created motion-corrupted scans that formed critical augmented training data for our deep networks.

At the core of our methodology lies AFT-Net, a cutting-edge deep network combining artificial Fourier transform layers with convolutional units optimized for complex MRI data. We trained customized AFT-Net models end-to-end to learn mappings from motion-corrupted inputs directly to target motion-free image reconstructions.

The results have us thrilled about the possibilities: our networks reliably corrected simulated motion artifacts, improving structural similarity over 0.9 against ground truth on test cases. This is a very promising outcome that demonstrates the power of data-driven deep learning to combat this age-old MRI challenge.

While more work remains in validating on real motion-corrupted data, our study highlights the potential for efficient and robust motion correction through deep learning on raw k-space. This could truly prove to be a game changer in improving diagnostic reliability of MRI scans along with reducing costs. We are excited to further develop this technology and usher in a new frontier of motion-resilient MRI powered by deep learning!

Note: Due to an NDA with Columbia University, I cannot share the full paper or source code publicly at this time. Please contact me if you would like more details.