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
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
Non-Cartesian CMR acquisition helps to lessen the scan time but have artifacts. This work proposes Transfer-learning approach with NUFFT (NUFFT TL-Net) to reconstruct artifact-free whole heart, radial CMR images.
Figure 2: Schematic illustration of the pre-trained network for the Proposed NUFFT TL-Net framework at AF=7 & 13
Figure 4: Middle slice (short axis) End-diastole and End-Systole reconstructed images of a patient for whole heart cine Radial MR at acceleration factor 13 with 24 radial lines per image. Reference cine: Fully sampled image: Undersampled Image: NUFFT with iFFT image. NUFFT U-Net: Reconstructed image of the fully trained NUFFT U-Net at AF=13, NUFFT TL-Net: Reconstructed image of NUFFT LT-Net at AF=13. Corresponding edge images provide reconstructed image quality assessment. Arrows show the myocardial wall distortion that is less pronounced in the NUFFT TL-Net.
