Deep MR parametric imaging with the learned L+S model and attention mechanism

Abstract

Abstract Magnetic resonance (MR) parametric imaging can help the assessment of some certain diseases with its various contrast mechanisms. However, the main issue of MR parametric imaging is the long acquisition time, introducing many problems such as uncomfortable experiences for patients and motion artefacts. With the deep learning methods developing, some unrolling ones have been introduced to MR imaging as solutions. The purpose of this study is to improve both the quality and the speed of parametric imaging. The proposed method, named as LSA‐Net, introduced a learned low rank plus sparsity model with attention mechanism to reconstruct highly under‐sampled data for MR parametric imaging. The L+S model was used to represent the shared image structure among the parameter‐weighted images as low‐rank part and the difference among images and the ideal model as sparse part. Besides, the attention block was introduced to effectively improved quality for the region of interest, which was most concerned in clinical applications. Then the parameter map was generated from the reconstructed parameter‐weighted images by its exponential model. The LSA‐Net was evaluated on the in‐vivo 3D T1ρ$T1 ho$ mapping, showing better performance on both image quality and time consumed than the comparing methods including LLR, SCOPE, L+S, and L+S‐Net.

Keywords

• biomedical imaging
• image reconstruction
• medical image processing