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End-to-end Adaptive Dynamic Subsampling and Reconstruction for Cardiac MRI

arXiv 2403.10346 ↗First posted 15 Mar 2024eess.IV cs.CV physics.med-phcited by 2

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Abstract (arXiv)

Background: Accelerating dynamic MRI is vital for advancing clinical applications and improving patient comfort. Commonly, deep learning (DL) methods for accelerated dynamic MRI reconstruction typically rely on uniformly applying non-adaptive predetermined or random subsampling patterns across all temporal frames of the dynamic acquisition. This approach fails to exploit temporal correlations or optimize subsampling on a case-by-case basis. Purpose: To develop an end-to-end approach for adaptive dynamic MRI subsampling and reconstruction, capable of generating customized sampling patterns maximizing at the same time reconstruction quality. Methods: We introduce the End-to-end Adaptive Dynamic Sampling and Reconstruction (E2E-ADS-Recon) for MRI framework, which integrates an adaptive dynamic sampler (ADS) that adapts the acquisition trajectory to each case for a given acceleration factor with a state-of-the-art dynamic reconstruction network, vSHARP, for reconstructing the adaptively sampled data into a dynamic image. The ADS can produce either frame-specific patterns or unified patterns applied to all temporal frames. E2E-ADS-Recon is evaluated under both frame-specific and unified 1D or 2D sampling settings, using dynamic cine cardiac MRI data and compared with vSHARP models employing standard subsampling trajectories, as well as pipelines where ADS was replaced by parameterized samplers optimized for dataset-specific schemes. Results: E2E-ADS-Recon exhibited superior reconstruction quality, especially at high accelerations, in terms of standard quantitative metrics (SSIM, pSNR, NMSE). Conclusion: The proposed framework improves reconstruction quality, highlighting the importance of case-specific subsampling optimization in dynamic MRI applications.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 24 of its 51 references that are in the map. First posted on arXiv 15 Mar 2024.

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24 of 51 references.

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12 more
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Citing papers per year: 2024: 1; 2025: 1.

2 papers.

  1. 2024Deep End-to-end Adaptive k-Space Sampling, Reconstruction, and Registration for Dynamic MRIcited by 0
  2. 2025T1-PILOT: Optimized Trajectories for T1 Mapping Accelerationcited by 0