A new strategy for accelerating multidimensional MRI data acquisition was introduced in this paper. It is based on the Genetic Algorithm (GA) to optimize the sparse data sampling strategy by maximizing the singular values of the kernel functions. The under-sampled data were then processed using Enhanced Discerning Multidimensional Inverse Laplace Transform (EDMILT) to obtain high-resolution spectroscopic imaging results. An 80 per cent reduction in acquisition time was achieved by applying the proposed framework while retaining image quality. The feasibility of the proposed framework was validated by using a numerical simulation protocol in different brain tissue models. Our results demonstrate the performance of the proposed framework in identifying the fluid contents in brain tissue models, offering a high probability of applying multidimensional MRI techniques in diagnosing neurological diseases.

multidimensional MRI,data acquisition,sparse sampling,genetic algorithm