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.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.