Accelerated Real time Cardiac CINE using Kernel PCA based Spatio-temporal Denoising

Standard Compressed Sensing (CS) techniques require signal/image to be a linear combination of very few coefficients in a transform representation. For dynamic cardiac MRI, examples of commonly used linear transforms are Wavelets, finite differences, temporal Fourier Transform and Principal Component Analysis (PCA). Nonlinear data reduction techniques such as Kernel PCA (KPCA) have the advantage over linear methods that these can detect nonlinearity or higher order moments within the given data set. By using appropriate nonlinear basis, complex features in the signal are expected to become separable that can be exploited for better signal classification or more compact representation of the signal. For MRI, this could be useful for a) better signal sparsity for CS and/or b) separation of signal content from artifacts in the undersampled reconstruction. Recently for retrospectively undersampled Cartesian cardiac CINE, compared to standard CS techniques, KPCA has been shown to more efficiently represent intra-frame spatial correlations for frame by frame reconstruction. In this work, we propose to accelerate real time dynamic cardiac CINE by exploiting both spatial and temporal denoising using kernel PCA. Prospective golden angle radial MR acquisitions, performed in 3 volunteers, demonstrate the feasibility of proposed framework for up to 8 fold accelerated real time CINE.

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