Susceptibility anisotropy imaging from single-orientation MRI with a training-free physics-informed autoencoder

Motivation: To overcome the clinical limitations of susceptibility tensor imaging (STI) due to the requirement for multiple head orientations. Goal(s): Develop a method to isolate χ13 and χ23 of the magnetic susceptibility tensor, from a single head orientation, enhancing the clinical viability of STI. Approach: Employing a deep learning-based autoencoder, calibrated via STI and optimized for each dataset to separate the tensor components without the need for training or data rotation. Results: The method successfully extracted χ13 and χ23 components comparable to the gold standard multi-orientation STI, showing potential for improved brain tissue characterization in conditions like multiple sclerosis. Impact: We present a simplified STI approach, extracting critical tensor components from a single orientation scan. The new technique allows to assess structural tissue integrity, particularly in white matter. Requiring only a single orientation renders it clinically feasible.