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Abstract #3435

Performance and Sensitivity of QSM Deep Learning algorithm to Background Field residuals using a Realistic Numerical Head Phantom

Carlos Milovic1,2, Mathias Lambert1,2, and Cristian Tejos1,2,3
1Electrical Engineering, Pontificia Universidad Catolica de Chile, Santiago, Chile, 2Biomedical Imaging Center, Pontificia Universidad Catolica de Chile, Santiago, Chile, 3Millennium Institute Millennium Institute for Intelligent Healthcare Engineering (iHEALTH), Santiago, Chile

Synopsis

Keywords: Susceptibility/QSM, Susceptibility, background fields

Motivation: Residual background fields in QSM reduce the accuracy of susceptibility reconstructions, making it crucial to evaluate the performance of different algorithms in managing these artifacts.

Goal(s): Compares the robustness of traditional iterative and Deep Learning-based QSM algorithms in the presence of residual background fields to determine which provides the most reliable reconstructions.

Approach: We applied three background field removal methods to simulated data and tested various QSM algorithms, measuring reconstruction error and variance across methods.

Results: Iterative methods, especially WH-QSM, outperformed Deep Learning approaches, showing lower error and more consistent results across local field estimations. Across methods, LBV produced the best reconstructions.

Impact: This study highlights the limitations of current Deep Learning-based QSM algorithms in handling residual background fields, suggesting a need for improved training strategies. It provides insights into which methods offer more reliable susceptibility maps, guiding future QSM development and applications.

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