Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction
Motivation: Characterizing noise is crucial for MRI reconstruction to ensure accurate SNR and image quality. However, current deep learning-based methods often neglect noise propagation.
Goal(s): Develop a tool to analyze noise propagation of physics-based deep unrolled network reconstructions without relying on computationally intensive Monte Carlo simulations.
Approach: Express image covariance via neural network's Jacobian, imaging operator, and coil covariance. Introduce a memory-efficient iterative algorithm that computes variance maps, avoiding full Jacobian storage.
Results: Our technique accurately computes noise variance maps for state-of-the-art unrolled MRI reconstruction techniques, closely matching Monte-Carlo simulation results. Extremely low SNR levels and high acceleration factors reduce variance estimation accuracy.
Impact: A practical tool facilitates noise analysis in physics-based deep learning reconstructions, so that image SNR can be assessed more objectively between reconstructions and patient scans. This would encourage researchers to develop more robust and trustworthy algorithms.
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