Precision with Simplicity: Refining Susceptibility maps with minimal iterative effort

Motivation: The research addresses limitations in QSM susceptibility maps, where over-regularization suppresses high-frequency details critical for accuracy, and deep learning models frequently produce artifacts due to inconsistent data handling. Goal(s): The study aims to recover high-frequency components in susceptibility maps, improving data consistency and accuracy with minimal computational demand. Approach: By initializing with an over-regularized map, the study applies a few gradient descent iterations to impose data consistency, balancing noise reduction with detail preservation. Results: NDI with limited gradient descent iterations effectively recovers high-frequency details, improving structural clarity and consistency, enhancing susceptibility map quality in simulated and in vivo contexts. Impact: This study's refinement method for QSM could empower clinicians with higher-quality susceptibility maps, improving diagnostic accuracy in neurological assessments. It encourages further exploration of minimal-iteration techniques, promoting efficient approaches to enhance imaging fidelity without intensive computational resources.