Fast Multi-class Dictionaries Learning with Geometrical Directions in MRI Reconstruction

Understand

Objective: Improve the reconstructed image with fast and multi-class dictionaries learning when magnetic resonance imaging is accelerated by undersampling the k-space data.

• Methods: A fast orthogonal dictionary learning method is introduced into magnetic resonance image reconstruction to providing adaptive sparse representation of images.
• To enhance the sparsity, image is divided into classified patches according to the same geometrical direction and dictionary is trained within each class.
• A new sparse reconstruction model with the multi-class dictionaries is proposed and solved using a fast alternating direction method of multipliers.