Speaker
Description
A 3D whole-heart MR acquisition with high spatial resolution can take several minutes. During this time, the heart is constantly moving due to breathing and the heartbeat, which can cause severe motion artefacts in the final image. To minimize these artefacts, motion correction approaches have been proposed. In this work, we utilise a motion-corrected image reconstruction (MCIR) approach. For this the acquired data is first split into n parts, corresponding to n motion states. Image registration is then used to determine the spatial transformation of each state with respect to a reference motion state. The obtained motion vector fields are subsequently used during image reconstruction to obtain a single motion-corrected MR image. While this approach leads to good image quality, the reconstruction times scale with the number of motion states. Especially for cardiac MRI, where both cardiac and respiratory motion need to be corrected, a high number of motion states might be necessary for an accurate result. Moreover, to achieve high image quality, iterative optimisation algorithms involving regularisation are used, which increases the reconstruction time. In this presentation, we demonstrate a stochastic optimisation approach which requires very few epochs and hence shorter reconstruction times compared to commonly used optimisation schemes. We evaluate the improved convergence in phantom experiments with simulated motion and demonstrate it on a cardiac 3D whole-heart scan obtained in-vivo.
