mlsanityData to 7 Oct 2026

HyperSLICE: HyperBand optimized Spiral for Low-latency Interactive Cardiac Examination

arXiv 2302.02688 ↗First posted 6 Feb 2023cs.CVcited by 3

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Abstract (arXiv)

PURPOSE: Interactive cardiac magnetic resonance imaging is used for fast scan planning and MR guided interventions. However, the requirement for real-time acquisition and near real-time visualization constrains the achievable spatio-temporal resolution. This study aims to improve interactive imaging resolution through optimization of undersampled spiral sampling and leveraging of deep learning for low-latency reconstruction (deep artifact suppression). METHODS: A variable density spiral trajectory was parametrized and optimized via HyperBand to provide the best candidate trajectory for rapid deep artifact suppression. Training data consisted of 692 breath-held CINEs. The developed interactive sequence was tested in simulations and prospectively in 13 subjects (10 for image evaluation, 2 during catheterization, 1 during exercise). In the prospective study, the optimized framework -HyperSLICE- was compared to conventional Cartesian real-time, and breath-hold CINE imaging in terms quantitative and qualitative image metrics. Statistical differences were tested using Friedman chi-squared tests with post-hoc Nemenyi test (p<0.05). RESULTS: In simulations the NRMSE, pSNR, SSIM and LAPE were all statistically significantly higher using optimized spiral compared to radial and uniform spiral sampling, particularly after scan plan changes (SSIM: 0.71 vs 0.45 and 0.43). Prospectively, HyperSLICE enabled a higher spatial and temporal resolution than conventional Cartesian real-time imaging. The pipeline was demonstrated in patients during catheter pull back showing sufficiently fast reconstruction for interactive imaging. CONCLUSION: HyperSLICE enables high spatial and temporal resolution interactive imaging. Optimizing the spiral sampling enabled better overall image quality and superior handling of image transitions compared to radial and uniform spiral trajectories.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 13 of its 40 references that are in the map. First posted on arXiv 6 Feb 2023.

Builds on

13 of 40 references.

References · 13

  1. 2016Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimizationcited by 688
  2. 2017A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstructioncited by 231
  3. 2019FastDVDnet: Towards Real-Time Deep Video Denoising Without Flow Estimationcited by 128
  4. 2017Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstructioncited by 105
  5. 2020Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challengecited by 59
  6. 2021B-spline Parameterized Joint Optimization of Reconstruction and K-space Trajectories (BJORK) for Accelerated 2D MRIcited by 29
  7. 2020Extending LOUPE for K-space Under-sampling Pattern Optimization in Multi-coil MRIcited by 22
  8. 2018Real-time Cardiovascular MR with Spatio-temporal Artifact Suppression using Deep Learning - Proof of Concept in Congenital Heart Diseasecited by 18
  9. 2021Dynamic imaging using a deep generative SToRM (Gen-SToRM) modelcited by 17
  10. 2019Spatio-Temporal Deep Learning-Based Undersampling Artefact Reduction for 2D Radial Cine MRI with Limited Datacited by 14
  11. 2020Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstructioncited by 14
  12. 2022Learning Task-Oriented Flows to Mutually Guide Feature Alignment in Synthesized and Real Video Denoisingcited by 5
1 more
  1. 2017Accelerated Computing in Magnetic Resonance Imaging -- Real-Time Imaging Using Non-Linear Inverse Reconstructioncited by 2

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Citing papers per year: 2023: 1; 2024: 2.

3 papers.

  1. 2023Investigating the use of publicly available natural videos to learn Dynamic MR image reconstructioncited by 0
  2. 2024Joint image reconstruction and segmentation of real-time cardiac MRI in free-breathing using a model based on disentangled representation learningcited by 0
  3. 2024Real-time cardiac cine MRI -- A comparison of a diffusion probabilistic model with alternative state-of-the-art image reconstruction techniques for undersampled spiral acquisitionscited by 0