FlowMoDL: Model-Based Deep Learning with Conjugate-Gradient Data Consistency for Highly Accelerated 4D Flow MRI Reconstruction
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 10Ă— to 50Ă— 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.