Meeting Banner
Abstract #1957

Reconstruction of Whole-Heart Cardiac Radial MRI using Neural Network Transfer Learning Approach

Ibtisam Aslam1,2, Fariha Aamir2, Lindsey A CROWE1, Miklos KASSAI1, Hammad Omer2, and Jean-Paul VALLEE1
1Service of Radiology, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland, 2Medical Image Processing Research Group (MIPRG), Deptt. of Electrical & Computer Engineering, COMSATS University Islamabad, Islamabad, Pakistan

In a clinical setting, multiple breath-hold, multi-slice, ECG-gated cine MR (CMR) Cartesian acquisition is a gold standard. Multiple breath-holds in standard CMR acquisition can result in slice-misalignment due to inconsistent breath-hold positions and it forces long exam time. To reduce CMR scan time and to avoid slice-misalignment, under-sampled non-Cartesian (NC) trajectories are useful but lead to artifacts. This paper proposes U-Net based transfer-learning approach with NUFFT (NUFFT TL-Net) to reconstruct artifact-free whole heart, radial CMR images. The preliminary experiments show improved performance of the proposed NUFFT TL-Net both visually and in terms of evaluation parameters than contemporary NUFFT U-Net.

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

Join Here