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Abstract #1060

Deep learning reconstruction enables accelerated acquisitions with consistent volumetric measurements

R. Marc Lebel1, Suryanarayanan Kaushik2, Trevor Kolupar2, Nate White3, Weidong Luo3, and Suchandrima Banerjee4
1GE Healthcare, Calgary, AB, Canada, 2GE Healthcare, Waukesha, WI, United States, 3Cortechs, San Diego, CA, United States, 4GE Healthcare, Menlo Park, CA, United States


This work evaluates brain volumetry measurements obtained from T1w images with fast (PI) and rapid (PI+CS) protocols (< 3 mins) and reconstructed with AIR Recon DL 3D, a deep learning-based reconstruction to reduce noise and ringing. Images were segmented using FDA-cleared software NeuroQuantÒ for quantitative volumetric analysis. Segmentation was successful in all cases and key brain volumes are unchanged between fast and rapid protocols, and further between conventional and DL reconstructions. This work demonstrates that highly accelerated acquisitions and advanced reconstruction methods are suitable for segmentation and volumetric studies and can improve repeatability of measurements.

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