快速的多对比度磁共振成像对于临床医学和科学研究有着重要作用。本项目拟从信号处理的角度，提出基于图像结构化稀疏表示的多对比度磁共振图像的欠采样重建方法，并设计适合多对比度成像的欠采样轨迹，尽可能缩短成像时间的前提下改善成像的质量。研究内容包括：1) 通过约束图像结构相似性训练结构化稀疏表示方法，结合欠采样数据的稀疏重建去除图像的伪影及噪声；2) 建立图像低维结构相似性约束的单/多对比度图像稀疏重建模型，实现双重结构相似性约束的结构化稀疏重建，进一步改善重建质量；3) 通过分析多对比度图像的结构相关性及冗余性，设计快速的多对比度图像k空间采样轨迹，加速成像过程，并结合重建模型及算法设计得到高质量的重建图像。本项目的实施可以促进磁共振成像理论的发展，推广磁共振成像在临床医学中的应用。

Achieving fast multi-contrast magnetic resonance imaging (MRI) is very important for the clinical diagnosis and scientific research. In this project, structured sparse representations are adaptively trained and sparsity regularized multi-contrast MR image reconstruction methods are proposed to reconstruct images from under-sampled k-space data. Besides, appropriate k-space sampling trajectories underlying the correlation analysis of multi-contrast MRI images are designed to accelerate MR imaging without compromising image qualities. Research work in the project will be conducted as follows: a) learn structured sparse representation with structure similarity constraint on images, realize sparsity-regularized reconstruction with the trained sparse representations; b) add similarity constraint to low dimension structures to form double structure similarity constraint in joint reconstruction models of single/multi –contrast MRI images, and then improve image qualities; c) the correlation or redundancy analysis among multi-contrast MRI images will guide the trajectory design of multi-contrast MRI, and speed up data acquiring process. The sampled data then combine the previous designed method to reconstruct images. The project can improve fast MRI theory and sparsity-based image reconstruction methods, which can greatly benefit the application of MRI imaging in clinical diagnosis.