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FlowMoDL: Model-Based Deep Learning with Conjugate-Gradient Data Consistency for Highly Accelerated 4D Flow MRI Reconstruction

Research Medical/Healthcare AI

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Overall 79
Content 95
Popularity 42

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Merged summary

TL;DR - FlowMoDL is an unrolled neural network for reconstructing highly accelerated 4D flow MRI while preserving anatomical detail and phase-derived blood-flow velocity accuracy. It supports acceleration factors from 10× to 50× with one model and outperforms classical and deep-learning baselines under a limited gradient-step budget.

  • Alternates a learned (3+1)D spatiotemporal denoiser with conjugate-gradient SENSE data-consistency updates.
  • Uses dual-pathway conditioning to adapt denoiser features and data-consistency weighting across acceleration factors.
  • Trains with deep supervision on magnitude, velocity magnitude, and angular errors, stabilized through curriculum learning.
  • On the multi-center CMRx4DFlow dataset, it leads all evaluated baselines across magnitude SSIM, nRMSE, relative velocity error, and angular error.

Sources (1)

FlowMoDL: Model-Based Deep Learning with Conjugate-Gradient Data Consistency for Highly Accelerated 4D Flow MRI Reconstruction

arXiv cs.CV Tristan Gottwald, Michelle Bruch, Mubashir-Ul Hassan, Fatma Alickovic, Milan Kloiber, Daniel Tenbrinck, Torsten Panholzer, Melanie Schaller, Jana Hutter 2026-08-26 arXiv:2608.25828
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-09-17 14:27:40.030658 UTC

TL;DR - FlowMoDL is an unrolled neural network for reconstructing highly accelerated 4D flow MRI while preserving anatomical detail and phase-derived blood-flow velocity accuracy. It supports acceleration factors from 10× to 50× with one model and outperforms classical and deep-learning baselines under a limited gradient-step budget.

  • Alternates a learned (3+1)D spatiotemporal denoiser with conjugate-gradient SENSE data-consistency updates.
  • Uses dual-pathway conditioning to adapt denoiser features and data-consistency weighting across acceleration factors.
  • Trains with deep supervision on magnitude, velocity magnitude, and angular errors, stabilized through curriculum learning.
  • On the multi-center CMRx4DFlow dataset, it leads all evaluated baselines across magnitude SSIM, nRMSE, relative velocity error, and angular error.
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