Recovery of damped exponentials using structured low rank matrix completion
This work addresses the challenge of efficient image recovery in medical imaging, specifically MR parameter mapping, with incremental improvements in computational efficiency.
The paper tackles the problem of recovering images from under-sampled measurements where signals are damped exponentials, by introducing a structured low rank matrix completion algorithm that exploits exponential behavior and spatial smoothness to derive a low-rank property, and it shows improvement over state-of-the-art methods in MR parameter mapping.
We introduce a structured low rank matrix completion algorithm to recover a series of images from their under-sampled measurements, where the signal along the parameter dimension at every pixel is described by a linear combination of exponentials. We exploit the exponential behavior of the signal at every pixel, along with the spatial smoothness of the exponential parameters to derive an annihilation relation in the Fourier domain. This relation translates to a low-rank property on a structured matrix constructed from the Fourier samples. We enforce the low rank property of the structured matrix as a regularization prior to recover the images. Since the direct use of current low rank matrix recovery schemes to this problem is associated with high computational complexity and memory demand, we adopt an iterative re-weighted least squares (IRLS) algorithm, which facilitates the exploitation of the convolutional structure of the matrix. Novel approximations involving two dimensional Fast Fourier Transforms (FFT) are introduced to drastically reduce the memory demand and computational complexity, which facilitates the extension of structured low rank methods to large scale three dimensional problems. We demonstrate our algorithm in the MR parameter mapping setting and show improvement over the state-of-the-art methods.