  Purpose: Off-resonance artifact correction by deep-learning, to facilitate
rapid pediatric body imaging with a scan time efficient 3D cones trajectory.
Methods: A residual convolutional neural network to correct off-resonance
artifacts (Off-ResNet) was trained with a prospective study of 30 pediatric
magnetic resonance angiography exams. Each exam acquired a short-readout scan
(1.18 ms +- 0.38) and a long-readout scan (3.35 ms +- 0.74) at 3T.
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 to short-readout scans by normalized
root-mean-square error (NRMSE), structural similarity index (SSIM), and peak
signal-to-noise ratio (PSNR). Scans were also compared by scoring on eight
anatomical features by two radiologists, using analysis of variance with
post-hoc Tukey's test. Reader agreement was determined with intraclass
correlation. Results: Long-readout scans were on average 59.3% shorter than
short-readout scans. Images from Off-ResNet had superior NRMSE, SSIM, and PSNR
compared to uncorrected images across +-1kHz off-resonance (P<0.01). The
proposed method had superior NRMSE over -677Hz to +1kHz and superior SSIM and
PSNR over +-1kHz compared to autofocus (P<0.01). Radiologic scoring
demonstrated that long-readout scans corrected with Off-ResNet were
non-inferior to short-readout scans (P<0.01). Conclusion: The proposed method
can correct off-resonance artifacts from rapid long-readout 3D cones scans to a
non-inferior image quality compared to diagnostically standard short-readout
scans.
