1b:I[76749,["/_next/static/chunks/bf18fea59be6d5b1.js","/_next/static/chunks/1ba21a8e081e29d1.js","/_next/static/chunks/0383663887945ae7.js","/_next/static/chunks/2060b26370dc6c0a.js","/_next/static/chunks/c89b4435b8c37fcf.js","/_next/static/chunks/8ddf2550f38c2f2a.js","/_next/static/chunks/5bf36002c846d2d8.js"],"default"] 1c:I[3486,["/_next/static/chunks/bf18fea59be6d5b1.js","/_next/static/chunks/1ba21a8e081e29d1.js","/_next/static/chunks/0383663887945ae7.js","/_next/static/chunks/2060b26370dc6c0a.js","/_next/static/chunks/c89b4435b8c37fcf.js","/_next/static/chunks/8ddf2550f38c2f2a.js","/_next/static/chunks/5bf36002c846d2d8.js"],"default"] 1d:I[17707,["/_next/static/chunks/bf18fea59be6d5b1.js","/_next/static/chunks/1ba21a8e081e29d1.js","/_next/static/chunks/0383663887945ae7.js","/_next/static/chunks/2060b26370dc6c0a.js","/_next/static/chunks/c89b4435b8c37fcf.js","/_next/static/chunks/8ddf2550f38c2f2a.js","/_next/static/chunks/5bf36002c846d2d8.js"],"default"] 1e:I[66528,["/_next/static/chunks/bf18fea59be6d5b1.js","/_next/static/chunks/1ba21a8e081e29d1.js","/_next/static/chunks/0383663887945ae7.js","/_next/static/chunks/2060b26370dc6c0a.js","/_next/static/chunks/c89b4435b8c37fcf.js","/_next/static/chunks/8ddf2550f38c2f2a.js","/_next/static/chunks/5bf36002c846d2d8.js"],"default"] 1f:I[63176,["/_next/static/chunks/bf18fea59be6d5b1.js","/_next/static/chunks/1ba21a8e081e29d1.js","/_next/static/chunks/0383663887945ae7.js","/_next/static/chunks/2060b26370dc6c0a.js","/_next/static/chunks/c89b4435b8c37fcf.js","/_next/static/chunks/8ddf2550f38c2f2a.js","/_next/static/chunks/5bf36002c846d2d8.js"],"default"] 20:I[87099,["/_next/static/chunks/bf18fea59be6d5b1.js","/_next/static/chunks/1ba21a8e081e29d1.js","/_next/static/chunks/0383663887945ae7.js","/_next/static/chunks/2060b26370dc6c0a.js","/_next/static/chunks/c89b4435b8c37fcf.js","/_next/static/chunks/8ddf2550f38c2f2a.js","/_next/static/chunks/5bf36002c846d2d8.js"],"default"] 21:I[26200,["/_next/static/chunks/bf18fea59be6d5b1.js","/_next/static/chunks/1ba21a8e081e29d1.js","/_next/static/chunks/0383663887945ae7.js","/_next/static/chunks/2060b26370dc6c0a.js","/_next/static/chunks/c89b4435b8c37fcf.js","/_next/static/chunks/8ddf2550f38c2f2a.js","/_next/static/chunks/5bf36002c846d2d8.js"],"Group"] 22:I[23365,["/_next/static/chunks/bf18fea59be6d5b1.js","/_next/static/chunks/1ba21a8e081e29d1.js","/_next/static/chunks/0383663887945ae7.js","/_next/static/chunks/2060b26370dc6c0a.js","/_next/static/chunks/c89b4435b8c37fcf.js","/_next/static/chunks/8ddf2550f38c2f2a.js","/_next/static/chunks/5bf36002c846d2d8.js"],"default"] 23:T40d,RoAR is trained to recognize the macroscopic magnetic field inhomogeneities directly from the input magnitude-only mGRE data and eliminate their effect on R2∗ measurements. RoAR training is based on the biophysical model and does not require ground-truth R2* maps. Since RoAR utilizes signal information not just from individual voxels but also accounts for spatial patterns of the signals in the images, it reduces the sensitivity of R2* maps to the noise in the data. These features plus high computational speed provide significant benefits for the potential usage of RoAR in clinical settings.

Max Torop

Publications