Implicit Neural Models for Direct Multi-Shell Diffusion MRI Signal Estimation
Diffusion magnetic resonance imaging is a noninvasive technique that is sensitive to the motion of water molecules in human tissue. The image signal is commonly described in q-space; however, a complete coverage of this space is often infeasible due to hardware and acquisition time limitations. In this work, we propose to represent the entire diffusion signal using an implicit neural representation network with wavelet activation functions. The signal space is encoded using Fourier features and spherical harmonics basis functions for a continuous output distribution of the raw diffusion data, enabling both super-resolution and q-space interpolation. Additionally, we introduce a pretrainin...
| Authors | Sanna Persson, Fabian Leander Sinzinger, Christoffer Olsson et al. |
|---|---|
| Primary category | eess.IV |
| Published | 16 October 2026 |
| Last updated at source | 20 October 2026 |
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