UCLA

Optogenetic functional magnetic resonance imaging (ofMRI) [1] is a powerful new technology that enables precise control of brain circuit elements while monitoring their causal outputs. To bring ofMRI to its full potential, it is essential to achieve high-spatial resolution with minimal distortions. With our proposed compressed sensing (CS) enabled method, high-spatial resolution ofMRI images can be obtained with a large field of view (FOV) without increasing spatial distortions and the amount of acquired data. The ofMRI data were sampled with passband balanced steady-state free precession (b-SSFP) [8, 17] fast stack-of-spiral sequence in order to achieve ultra-high-spatial resolution images in a short amount of time. Interleaves of data were randomly collected. The images were recovered from the undersampled k-space data by solving an unconstrained convex optimization problem, which balances the trade-off between data consistency and sparsity. The optimization problem can be solved by gradient descent combined with backtracking line search algorithms. Discrete cosine transform (DCT) were chosen as a sparsifying transform. The ofMRI image reconstruction was processed in parallel on a graphics processing unit (GPU) using C/C++ language supported by NVIDIA CUDA engine in order to achieve short reconstruction time. An existing nonequispaced fast Fourier transform (NFFT) algorithm [13, 14] was modified for our GPU parallel processing purpose. The results demonstrate that the compressed sensing reconstructed image has higher resolution while maintaining a precise activation map, compared to a fully sampled low-resolution image with the same amount of data and scan time. A 4-D image can be reconstructed in less than fifteen minutes, which allows compressed sensing ofMRI to become a practical application.