Abstract #4792

Convolutional Neural Network for Real-Time High Spatial Resolution Functional Magnetic Resonance Imaging

Cagan Alkan¹, Zhongnan Fang², and Jin Hyung Lee^1,3,4,5

¹Electrical Engineering, Stanford University, Stanford, CA, United States, ²LVIS Corporation, Palo Alto, CA, United States, ³Neurology, Stanford University, Stanford, CA, United States, ⁴Bioengineering, Stanford University, Stanford, CA, United States, ⁵Neurosurgery, Stanford University, Stanford, CA, United States

We propose a convolutional neural network (CNN) based real-time high spatial resolution fMRI method that can reconstruct a 3D volumetric image (140x140x28 matrix size) in 150 ms. We achieved 4x spatial resolution improvement using variable density spiral (VDS) trajectory design. The proposed method achieves similar reconstruction performance as our earlier compressed sensing reconstructions while achieving 17x faster reconstruction time. We demonstrate that this method accurately detects cortical layer specific activity.

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