Autoencoder for 4-Dimensional Fiber Orientation Distributions from Diffusion MRI

Fiber orientation distributions (FODs) are widely used in connectome analysis based on diffusion MRI. Spherical harmonics (SPHARMs) are often used for the efficient representation of FODs; however, SPHARMs over the 3-D image volume are in essence four-dimensional. This makes it highly memory-consuming for applying advanced deep learning methods, such as the transformer and diffusion model, to FODs represented by high order SPHARMs. In this work, we present an order-balanced order-level (OBOL) autoencoder to compress the FODs with high accuracy after decoding. Our OBOL method uses separate encoders for FODs in each SPHARM order to balance the feature map size of FODs in different orders. This helps the encoder to better preserve information from the low-order coefficients that have more information but a smaller number of volumes. In our experiments, we demonstrated that the decoded FODs of our OBOL autoencoder have better accuracy than the spatial-level or order-level autoencoder without order balance. We also tested the encoded latent space of the OBOL autoencoder in FOD super-resolution. Results show high accuracy with feasible memory usage in commonly available GPUs.