ArticleIEEE transactions on medical imaging2022
B-Spline Parameterized Joint Optimization of Reconstruction and K-Space Trajectories (BJORK) for Accelerated 2D MRI.
Article in IEEE transactions on medical imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Jointly Learned 3D Non-Cartesian Sampling With Wave Encoding and Reconstruction for Neurovascular Phase Contrast MRI.Magnetic resonance in medicine · 2026Article
- OPTIKS: Optimized Gradient Properties Through Timing in k-Space.IEEE transactions on medical imaging · 2026Article
- Well-designed k-space coverage is important for good MRI denoising.Magma (New York, N.Y.) · 2026Article
- Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO).IEEE transactions on computational imaging · 2026Article
- Dynamic contrast enhanced-magnetic resonance fingerprinting (DCE-MRF): A new quantitative MRI method to reliably assess tumor vascular perfusion.Magnetic resonance in medicine · 2025Article
- Optimized MR pulse sequence for high-resolution brain 3D-T1ρ mapping with weighted spin-lock acquisitions.Magnetic resonance in medicine · 2025Article
- Performance of MR learned pulse sequences for 3D bi-exponential, stretched-exponential, and mono-exponential TMagnetic resonance in medicine · 2025Article
- AutoSamp: Autoencoding k-Space Sampling via Variational Information Maximization for 3D MRI.IEEE transactions on medical imaging · 2025Article
- A critical assessment of artificial intelligence in magnetic resonance imaging of cancer.Npj imaging · 2025Review
- 3D MR fingerprinting for dynamic contrast-enhanced imaging of whole mouse brain.Magnetic resonance in medicine · 2025Article
- Article
- Artificial intelligence for neuro MRI acquisition: a review.Magma (New York, N.Y.) · 2024Review
- Deep learning for accelerated and robust MRI reconstruction.Magma (New York, N.Y.) · 2024Review
- Emerging Trends in Magnetic Resonance Fingerprinting for Quantitative Biomedical Imaging Applications: A Review.Bioengineering (Basel, Switzerland) · 2024Review
- PulPy: A Python Toolkit for MRI RF and Gradient Pulse Design.Journal of open source software · 2024Article
- HyperSLICE: HyperBand optimized spiral for low-latency interactive cardiac examination.Magnetic resonance in medicine · 2024Article
- Learning Task-Specific Strategies for Accelerated MRI.IEEE transactions on computational imaging · 2024Article
- Emerging Trends in Fast MRI Using Deep-Learning Reconstruction on Undersampled k-Space Data: A Systematic Review.Bioengineering (Basel, Switzerland) · 2023Review
- Stochastic optimization of three-dimensional non-Cartesian sampling trajectory.Magnetic resonance in medicine · 2023Article
- AI in MRI: Computational Frameworks for a Faster, Optimized, and Automated Imaging Workflow.Bioengineering (Basel, Switzerland) · 2023Article
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Authors and funding
5 authors.
Funding
Abstract
Optimizing k-space sampling trajectories is a promising yet challenging topic for fast magnetic resonance imaging (MRI). This work proposes to optimize a reconstruction method and sampling trajectories jointly concerning image reconstruction quality in a supervised learning manner. We parameterize trajectories with quadratic B-spline kernels to reduce the number of parameters and apply multi-scale optimization, which may help to avoid sub-optimal local minima. The algorithm includes an efficient non-Cartesian unrolled neural network-based reconstruction and an accurate approximation for backpropagation through the non-uniform fast Fourier transform (NUFFT) operator to accurately reconstruct and back-propagate multi-coil non-Cartesian data. Penalties on slew rate and gradient amplitude enforce hardware constraints. Sampling and reconstruction are trained jointly using large public datasets. To correct for possible eddy-current effects introduced by the curved trajectory, we use a pencil-beam trajectory mapping technique. In both simulations and in- vivo experiments, the learned trajectory demonstrates significantly improved image quality compared to previous model-based and learning-based trajectory optimization methods for 10× acceleration factors. Though trained with neural network-based reconstruction, the proposed trajectory also leads to improved image quality with compressed sensing-based reconstruction.
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