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Abstract #3437

Precision with Simplicity: Refining Susceptibility maps with minimal iterative effort

Mathias Lambert1,2 and Carlos Milovic1,2
1Biomedical Imaging Center, Pontificia Universidad Católica de Chile, Santiago, Chile, 2Department of Electrical Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile

Synopsis

Keywords: Susceptibility/QSM, Quantitative Susceptibility mapping

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.

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