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Abstract #2775

Accelerating High Resolution 3D EPI with Deep Learning Reconstruction

Nastaren Abad1, Sangtae Ahn1, Rafi Brada2, Tim Sprenger3, Brice Fernandez4, Suchandrima Banerjee5, Teck Beng Desmond Yeo1, and Thomas K.F. Foo1
1GE HealthCare, Technology & Innovation Center, Niskayuna, NY, United States, 2GE HealthCare, Technology & Innovation Center, Herzliya, Israel, 3GE HealthCare, Munich, Germany, 4GE HealthCare, Buc, France, 5GE HealthCare, Menlo Park, CA, United States

Synopsis

Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction, Deep Learning, 3D, Echo Planar Imaging, Quantitative Imaging, Data Acquisition, Image Reconstruction, Susceptibility/QSM, Acquisition, Reconstruction, Acceleration, Neuro

Motivation: To increase access to clinically relevant features disambiguated from partial volume based confounds by enabling ultra-high spatial resolution without clinically restrictive scan times.

Goal(s): To investigate the feasibility of accelerating ultra-high resolution 3D EPI for rapid brain imaging, with DL based image reconstruction.

Approach: Multi-shot and single-shot 3D EPI data were retrospectively undersampled with tiled variable-density Poisson-disc (VDPD) sampling patterns and then reconstructed by DL Speed, a DL image reconstruction method that we developed.

Results: We demonstrated DL Speed can achieve an acceleration factor of 10 for 3D EPI while maintaining image quality compared to fully sampled data.

Impact: Deep learning based sparse image reconstruction can accelerate ultra-high resolution 3D EPI scans for brain imaging with acceleration factors ranging from 3-10, enabling disambiguation of clinically relevant fine features in various neuro imaging applications such as SWI, DWI and fMRI.

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Keywords