Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  proposes an efficient algorithm to simultaneously reconstruct multiple T1/T2-ighted images of the same anatomical cross section from partially sampled k-space data. The simultaneous reconstruction problem is formulated as minimizing a linear combination of three terms corresponding to a least square data fitting, joint total-variation (TV) and group wavelet-sparsity regularization. It is rooted in two observations: 1) the variance of image gradients should be similar for the same spatial position across multiple contrasts; 2) the wavelet coefficients of all images from the same anatomical cross section should have similar sparse modes. To efficiently solve this formulation,  decompose it into group sparsity and joint TV regularization subproblems, respectively. Finally, the reconstructed image is obtained from the ighted average of solutions from two subproblems in an iterative framework.  compare the proposed algorithm with previous methods on SRT24 multi-channel Brain Atlas Data. Experiments demonstrate its superior performance for multi-contrast MR image reconstruction.
['Junzhou Huang', 'Chen Chen', 'Leon Axel']
I am writing a research paper on a idea Fast multi-contrast MRI reconstruction
[{"from": "human", "value": "I am writing a research paper on a idea Fast multi-contrast MRI reconstruction"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  proposes an efficient algorithm to simultaneously reconstruct multiple T1/T2-ighted images of the same anatomical cross section from partially sampled k-space data. The simultaneous reconstruction problem is formulated as minimizing a linear combination of three terms corresponding to a least square data fitting, joint total-variation (TV) and group wavelet-sparsity regularization. It is rooted in two observations: 1) the variance of image gradients should be similar for the same spatial position across multiple contrasts; 2) the wavelet coefficients of all images from the same anatomical cross section should have similar sparse modes. To efficiently solve this formulation,  decompose it into group sparsity and joint TV regularization subproblems, respectively. Finally, the reconstructed image is obtained from the ighted average of solutions from two subproblems in an iterative framework.  compare the proposed algorithm with previous methods on SRT24 multi-channel Brain Atlas Data. Experiments demonstrate its superior performance for multi-contrast MR image reconstruction."}]