ArticleMagnetic resonance in medicine2019
Deep residual network for off-resonance artifact correction with application to pediatric body MRA with 3D cones.
Article in Magnetic resonance in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 31 citations in OpenAlex.
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- Off-resonance artifact correction for MRI: A review.NMR in biomedicine · 2023Review
- Deep Learning and Its Application to Function Approximation for MR in Medicine: An Overview.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2022Review
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- Free-breathing Accelerated Cardiac MRI Using Deep Learning: Validation in Children and Young Adults.Radiology · 2021Article
- Prospective Deployment of Deep Learning in MRI: A Framework for Important Considerations, Challenges, and Recommendations for Best Practices.Journal of magnetic resonance imaging : JMRI · 2021Review
- A multispeaker dataset of raw and reconstructed speech production real-time MRI video and 3D volumetric images.Scientific data · 2021Article
- Free-breathingMagnetic resonance in medicine · 2021Article
- Diffusion-weighted double-echo steady-state with a three-dimensional cones trajectory for non-contrast-enhanced breast MRI.Journal of magnetic resonance imaging : JMRI · 2021Article
- Deblurring for spiral real-time MRI using convolutional neural networks.Magnetic resonance in medicine · 2020Article
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
purposeTo enable rapid imaging with a scan time-efficient 3D cones trajectory with a deep-learning off-resonance artifact correction technique.
methodsA 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 similarity index, and peak SNR. Scans were also compared by scoring on 8 anatomical features by two radiologists, using analysis of variance with post hoc Tukey's test and two one-sided t-tests. Reader agreement was determined with intraclass correlation.
resultsThe total scan time for long readout scans was on average 59.3% shorter than short readout scans. Images from Off-ResNet had superior normalized RMS error, structural similarity index, and peak SNR compared with uncorrected images across ±1 kHz off-resonance (P < .01). The proposed method had superior normalized RMS error over -677 Hz to +1 kHz and superior structural similarity index and peak SNR over ±1 kHz compared with autofocus (P < .01). Radiologic scoring demonstrated that long readout scans corrected with Off-ResNet were noninferior to short readout scans (P < .05).
conclusionThe proposed method can correct off-resonance artifacts from rapid long-readout 3D cones scans to a noninferior image quality compared with diagnostically standard short readout scans.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.