ArticleMagnetic resonance in medicine2021
Super-resolution head and neck MRA using deep machine learning.
Article in Magnetic resonance in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 34 citations in OpenAlex.
- Performance of a GPU- and time-efficient pseudo-3D network for magnetic resonance image super-resolution and motion artifact reduction.Scientific reports · 2026Article
- High Resolution TOF-MRA Using Compressed Sensing-based Deep Learning Image Reconstruction for the Visualization of Lenticulostriate Arteries: A Preliminary Study.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Article
- Review
- Simultaneous Luminal and Hemodynamic Evaluation of the Cervical Arteries Using Nonenhanced 3D Quantitative Quiescent-Interval Slice-Selective Magnetic Resonance Angiography.Journal of magnetic resonance imaging : JMRI · 2025Article
- Accelerated intracranial time-of-flight MR angiography with image-based deep learning image enhancement reduces scan times and improves image quality at 3-T and 1.5-T.Neuroradiology · 2025Article
- Robust resolution improvement of 3D UTE-MR angiogram of normal vasculatures using super-resolution convolutional neural network.Scientific reports · 2025Article
- Sub-1-min relaxation-enhanced non-contrast non-triggered cervical MRA using compressed SENSE with deep learning reconstruction in healthy volunteers.European radiology experimental · 2025Article
- Deep learning improves quality of intracranial vessel wall MRI for better characterization of potentially culprit plaques.Scientific reports · 2024Article
- Deep Learning-Based High-Resolution Magnetic Resonance Angiography (MRA) Generation Model for 4D Time-Resolved Angiography with Interleaved Stochastic Trajectories (TWIST) MRA in Fast Stroke Imaging.Diagnostics (Basel, Switzerland) · 2024Article
- Exploring the impact of super-resolution deep learning on MR angiography image quality.Neuroradiology · 2024Article
- Current State of Artificial Intelligence in Clinical Applications for Head and Neck MR Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2023Review
- Application of deep learning-based super-resolution to T1-weighted postcontrast gradient echo imaging of the chest.La Radiologia medica · 2023Article
- 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
- Deep Learning Applications in Magnetic Resonance Imaging: Has the Future Become Present?Diagnostics (Basel, Switzerland) · 2021Review
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
purposeTo probe the feasibility of deep learning-based super-resolution (SR) reconstruction applied to nonenhanced MR angiography (MRA) of the head and neck.
methodsHigh-resolution 3D thin-slab stack-of-stars quiescent interval slice-selective (QISS) MRA of the head and neck was obtained in eight subjects (seven healthy volunteers, one patient) at 3T. The spatial resolution of high-resolution ground-truth MRA data in the slice-encoding direction was reduced by factors of 2 to 6. Four deep neural network (DNN) SR reconstructions were applied, with two based on U-Net architectures (2D and 3D) and two (2D and 3D) consisting of serial convolutions with a residual connection. SR images were compared to ground-truth high-resolution data using Dice similarity coefficient (DSC), structural similarity index measure (SSIM), arterial diameter, and arterial sharpness measurements. Image review of the optimal DNN SR reconstruction was done by two experienced neuroradiologists.
resultsDNN SR of up to twofold and fourfold lower-resolution (LR) input volumes provided images that resembled those of the original high-resolution ground-truth volumes for intracranial and extracranial arterial segments, and improved DSC, SSIM, arterial diameters, and arterial sharpness relative to LR volumes (P < .001). The 3D DNN SR outperformed 2D DNN SR reconstruction. According to two neuroradiologists, 3D DNN SR reconstruction consistently improved image quality with respect to LR input volumes (P < .001).
conclusionDNN-based SR reconstruction of 3D head and neck QISS MRA offers the potential for up to fourfold reduction in acquisition time for neck vessels without the need to commensurately sacrifice spatial resolution.
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