Deep residual network for off-resonance artifact correction with application to pediatric body MRA with 3D cones.

ABSTRACT: PURPOSE:To enable rapid imaging with a scan time-efficient 3D cones trajectory with a deep-learning off-resonance artifact correction technique. METHODS:A residual convolutional neural network to correct off-resonance artifacts (Off-ResNet) was trained with a prospective study of pediatric MRA exams. Each exam acquired a short readout scan (1.18 ms ± 0.38) and a long readout scan (3.35 ms ± 0.74) at 3 T. Short readout scans, with longer scan times but negligible off-resonance blurring, were used as reference images and augmented with additional off-resonance for supervised training examples. Long readout scans, with greater off-resonance artifacts but shorter scan time, were corrected by autofocus and Off-ResNet and compared with short readout scans by normalized RMS error, structural simi

SUBMITTER: Zeng DY 

PROVIDER: S-EPMC6626585 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature