Self-Constrained Magnetization Vector Inversion in the Data Space

Key Points

• The innovative method improves inversion accuracy for the magnetization vector, enhancing data fidelity.
• A notable reduction of errors up to 30% was observed, showcasing the algorithm's effectiveness compared to existing techniques.
• This analysis uses advanced inversion algorithms to explore magnetization vectors in data space for better imaging outcomes.
• The findings may lead to more precise magnetic imaging applications, warranting further exploration in clinical contexts.