Meeting Banner
Abstract #4438

Towards motion-robust MRI – Autonomous motion timing and correction during MR scanning using multi-coil data and a deep-learning neural network

Rafi Brada1, Michael Rotman1, Ron Wein1, Sangtae Ahn2, Itzik Malkiel1, and Christopher J. Hardy2

1GE Global Research, Herzliya, Israel, 2GE Global Research, Niskayuna, NY, United States

We propose a method for timing and correcting for rigid-body in-plane patient motion during an MRI scan. The motion is detected using differences between coil-intensity-corrected images from different coils in the receiver array together with the scan-order information. The method allows for the detection and timing of multiple movements during the scan. For each scan where motion was detected, k-space data are divided into different motion states, which are used as input to a deep neural network whose output is a motion-corrected image. The system shows promising results on MR data containing simulated and real motion.

This abstract and the presentation materials are available to members only; a login is required.

Join Here