Speaker
Description
MRI reconstruction from non-Cartesian k-space undersampling is naturally formulated as an inverse problem (y = Ax + n), where recovering the image x from data y critically depends on the accuracy and efficiency of the forward model A. MRI-NUFFT is an open-source Python library that provides a unified, high-performance implementation of this physics-based forward model for non-Cartesian MRI using the Non-Uniform Fast Fourier Transform (NUFFT). It additionally offers multi-coil sensitivity estimation (ESPIRIT), density compensation strategies, and off-resonance correction. Centered on the core operator A, MRI-NUFFT enables efficient computation of forward, adjoint, and pseudo-inverse mappings across multiple backends and hardware. It supports automatic differentiation with respect to both data and physics parameters (k-space trajectories, field maps, sensitivity maps), native interoperability with NumPy, CuPy, and PyTorch, and seamless CPU/GPU execution, making it well-suited for optimization-based reconstruction and learning-based frameworks. A distinctive feature is its tight integration between reconstruction and acquisition design: MRI-NUFFT includes a rich collection of parameterized 2D and 3D non-Cartesian trajectories, supports hardware-constrained trajectory projection, and enables gradient waveform design and optimization under magnetic gradient hardware constraints. Compatibility with Pulseq further facilitates the generation of executable sequences, bridging simulation and scanner deployment. By positioning the forward model as a central, reusable component, MRI-NUFFT enables rapid prototyping of inverse problem formulations, from variational methods to physics-informed machine learning and joint optimization of sampling and reconstruction.