Anisotropic field-of-views in radial imaging

1 code implementation • 12 Jan 2021 • Peder E. Z. Larson, Paul T. Gurney, Dwight G. Nishimura

One drawback is that standard implementations do not support anisotropic field-of-view (FOV) shapes, which are used to match the imaging parameters to the object or region-of-interest.

Reconstruction of Undersampled 3D Non-Cartesian Image-Based Navigators for Coronary MRA Using an Unrolled Deep Learning Model

no code implementations • 24 Oct 2019 • Mario O. Malavé, Corey A. Baron, Srivathsan P. Koundinyan, Christopher M. Sandino, Frank Ong, Joseph Y. Cheng, Dwight G. Nishimura

Reconstruction with the unrolled network completes in a fraction of the time compared to CPU and GPU implementations of $\textit{l}_{1}$-ESPIRiT (20x and 3x speed increases, respectively).

Deep Residual Network for Off-Resonance Artifact Correction with Application to Pediatric Body Magnetic Resonance Angiography with 3D Cones

no code implementations • 28 Sep 2018 • David Y Zeng, Jamil Shaikh, Dwight G. Nishimura, Shreyas S. Vasanawala, Joseph Y. Cheng

Long-readout scans, with greater off-resonance artifacts but shorter scan time, were corrected by autofocus and Off-ResNet and compared to short-readout scans by normalized root-mean-square error (NRMSE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR).

SSIM