Pixelwise Adaptive Polynomial Subspace Reconstruction for Accelerated CMR Perfusion Imaging

Motivation: Subspace methods have been extensively applied for MRI reconstruction. However, most methods assume the subspace holds for all pixels, which may be invalid. Additional prior information can be adopted to further regularize each pixel to reduce the degrees of freedom. Goal(s): Develop a fast, data-driven, pixelwise subspace reconstruction method for high-quality, accelerated CMR perfusion imaging. Approach: Temporal variability is assessed per pixel using SENSE reconstruction, and pixels are clustered by temporal variance. A polynomial subspace is then applied to each pixel with dimensions based on clustering. Results: The method has been successfully applied to reconstruct single band and multiband SMILE first-pass perfusion imaging. Impact: This pixelwise subspace approach enhances various subspace-regularized reconstructions, with the polynomial basis offering a versatile, predefined subspace for numerous MRI applications.