Abstract #2596

The Effect of Axial Reorientation on Deep Learning-Based Susceptibility Mapping

Fahad Salman^1,2, Thomas Jochmann^1,3, Ilyes Benslimane¹, Stuart D. Inglis⁴, Niels P. Bergsland¹, Michael G. Dwyer^1,5, Jens Haueisen^3,6, Robert Zivadinov^1,5, and Ferdinand Schweser^1,5

¹Buffalo Neuroimaging Analysis Center, Department of Neurology at the Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, United States, ²Department of Biomedical Engineering, University at Buffalo, The State University of New York, Buffalo, NY, United States, ³Department of Computer Science and Automation, Technische Universität Ilmenau, Ilmenau, Germany, ⁴Pathology and Anatomical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, United States, ⁵Center for Biomedical Imaging, Clinical and Translational Science Institute, University at Buffalo, The State University of New York, Buffalo, NY, United States, ⁶Department of Neurology, Jena University Hospital, Jena, Germany

Synopsis

Keywords: Image Reconstruction, Susceptibility, Rotation, axial orientation, oblique orientation, spline, sinc, trilinear, FSL FLIRT, deep learning, QSM, PRLs, paramagnetic rim lesions, magnitude, phase, BFR, background correction

Motivation: The rotation of oblique MRI acquisitions to axial orientation may degrade susceptibility maps’ quality depending on the interpolation method.

Goal(s): This study aimed to determine the optimal interpolation method and stage within the QSM pipeline to apply rotation, improving susceptibility outcomes and supporting the use of deep learning QSM in clinical applications.

Approach: On an obliquely acquired post-mortem brain scan, we applied trilinear, spline, and sinc interpolation at different stages in the QSM pipeline and compared outcomes against its axially acquired scan.

Results: Spline interpolation before background correction best preserved image clarity, achieving high SSIM scores and maintaining sharpness compared to trilinear.

Impact: Using spline-based interpolation before background correction in QSM improves the visibility of clinical features, aiding the accuracy and effectiveness of QSM applications in diagnosing brain disorders.

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