Unifying Subsampling Pattern Variations for Compressed Sensing MRI with Neural Operators

Compressed Sensing MRI (CS-MRI) reconstructs images of the body's internal anatomy from undersampled and compressed measurements, thereby reducing scan times and minimizing the duration patients need to remain still. Recently, deep neural networks have shown great potential for reconstructing high-quality images from highly undersampled measurements. However, since deep neural networks operate on a fixed discretization, one needs to train multiple models for different measurement subsampling patterns and image resolutions. This approach is highly impractical in clinical settings, where subsampling patterns and image resolutions are frequently varied to accommodate different imaging and diagnostic requirements. We propose a unified model that is robust to different subsampling patterns and image resolutions in CS-MRI. Our model is based on neural operators, a discretization-agnostic architecture. We use neural operators in both image and measurement (frequency) space, which capture local and global image features for MRI reconstruction. Empirically, we achieve consistent performance across different subsampling rates and patterns, with up to 4x lower NMSE and 5 dB PSNR improvements over the state-of-the-art method. We also show the model is agnostic to image resolutions with zero-shot super-resolution results. Our unified model is a promising tool that is agnostic to measurement subsampling and imaging resolutions in MRI, offering significant utility in clinical settings where flexibility and adaptability are essential for efficient and reliable imaging.