Leslie Ying

TL;DR: This paper proposes a deep learning approach for accelerating magnetic resonance imaging (MRI) using a large number of existing high quality MR images as the training datasets and an off-line convolutional neural network to identify the mapping relationship between the MR images obtained from zero-filled and fully-sampled k-space data.

TL;DR: A novel method to combine sensitivity encoding (SENSE), one of the standard methods for parallel MRI, and compressed sensing for rapid MR imaging (SparseMRI), a recently proposed method for applying CS in MR imaging with Cartesian trajectories is proposed.

TL;DR: The problem of error propagation in the sequential procedure of sensitivity estimation followed by image reconstruction in existing methods, such as sensitivity encoding (SENSE) and simultaneous acquisition of spatial harmonics (SMASH), is considered and reformulates the image reconstruction problem as a joint estimation of the coil sensitivities and the desired image, which is solved by an iterative optimization algorithm.

TL;DR: Several signal processing issues for maximizing the potential of deep reconstruction in fast MRI are discussed, which may facilitate further development of the networks and performance analysis from a theoretical point of view.

TL;DR: A Beamlet transform‐based approach to automatically detect and classify pavement cracks in digital images to correct the nonuniform background illumination by calculating the multiplicative factors that eliminate the background lighting variation is presented.