ArticleMagnetic resonance in medicine2023
Stochastic optimization of three-dimensional non-Cartesian sampling trajectory.
Article in Magnetic resonance in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 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
- Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO).IEEE transactions on computational imaging · 2026Article
- Improved Spiral Projection MR Fingerprinting via Memory-Efficient Synergic Optimization of 3D Spiral Trajectory, Image Reconstruction and Parameter Estimation (SOTIP).IEEE transactions on medical imaging · 2025Article
- Advancing MRI Reconstruction: A Systematic Review of Deep Learning and Compressed Sensing Integration.ArXiv · 2025Article
- 3D MR fingerprinting for dynamic contrast-enhanced imaging of whole mouse brain.Magnetic resonance in medicine · 2025Article
- AutoSamp: Autoencoding k-Space Sampling via Variational Information Maximization for 3D MRI.IEEE transactions on medical imaging · 2025Article
- Electric potential energy optimized 3D radial sampling trajectories for MRI.Scientific reports · 2024Article
- Efficient 3D cone trajectory design for improved combined angiographic and perfusion imaging using arterial spin labeling.Magnetic resonance in medicine · 2024Article
- 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
- Model-based reconstruction for looping-star MRI.Magnetic resonance in medicine · 2024Article
- New clinical opportunities of low-field MRI: heart, lung, body, and musculoskeletal.Magma (New York, N.Y.) · 2024Review
- Learning Task-Specific Strategies for Accelerated MRI.IEEE transactions on computational imaging · 2024Article
- Jointly Learning Non-CartesianBioengineering (Basel, Switzerland) · 2023Article
- Efficient Approximation of Jacobian Matrices Involving a Non-Uniform Fast Fourier Transform (NUFFT).IEEE transactions on computational imaging · 2023Article
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4 authors.
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Abstract
purposeOptimizing three-dimensional (3D) k-space sampling trajectories is important for efficient MRI yet presents a challenging computational problem. This work proposes a generalized framework for optimizing 3D non-Cartesian sampling patterns via data-driven optimization.
methodsWe built a differentiable simulation model to enable gradient-based methods for sampling trajectory optimization. The algorithm can simultaneously optimize multiple properties of sampling patterns, including image quality, hardware constraints (maximum slew rate and gradient strength), reduced peripheral nerve stimulation (PNS), and parameter-weighted contrast. The proposed method can either optimize the gradient waveform (spline-based freeform optimization) or optimize properties of given sampling trajectories (such as the rotation angle of radial trajectories). Notably, the method can optimize sampling trajectories synergistically with either model-based or learning-based reconstruction methods. We proposed several strategies to alleviate the severe nonconvexity and huge computation demand posed by the large scale. The corresponding code is available as an open-source toolbox.
resultsWe applied the optimized trajectory to multiple applications including structural and functional imaging. In the simulation studies, the image quality of a 3D kooshball trajectory was improved from 0.29 to 0.22 (NRMSE) with Stochastic optimization framework for 3D NOn-Cartesian samPling trajectorY (SNOPY) optimization. In the prospective studies, by optimizing the rotation angles of a stack-of-stars (SOS) trajectory, SNOPY reduced the NRMSE of reconstructed images from 1.19 to 0.97 compared to the best empirical method (RSOS-GR). Optimizing the gradient waveform of a rotational EPI trajectory improved participants' rating of the PNS from "strong" to "mild."
conclusionSNOPY provides an efficient data-driven and optimization-based method to tailor non-Cartesian sampling trajectories.
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