Ground-Truth Effects in Learning-Based Fiber Orientation Distribution Estimation in Neonatal Brains
Diffusion Magnetic Resonance Imaging (dMRI) is a non-invasive method for depicting brain microstructure in vivo. Fiber orientation distributions (FODs) are mathematical representations extensively used to map white matter fiber configurations. Recently, FOD estimation with deep neural networks has seen growing success, in particular, those of neonates estimated with fewer diffusion measurements. These methods are mostly trained on target FODs reconstructed with multi-shell multi-tissue constrained spherical deconvolution (MSMT-CSD), which might not be the ideal ground truth for developing brains. Here, we investigate this hypothesis by training a state-of-the-art model based on the U-Net architecture on both MSMT-CSD and single-shell three-tissue constrained spherical deconvolution (SS3T-CSD). Our results suggest that SS3T-CSD might be more suited for neonatal brains, given that the ratio between single and multiple fiber-estimated voxels with SS3T-CSD is more realistic compared to MSMT-CSD. Additionally, increasing the number of input gradient directions significantly improves performance with SS3T-CSD over MSMT-CSD. Finally, in an age domain-shift setting, SS3T-CSD maintains robust performance across age groups, indicating its potential for more accurate neonatal brain imaging.
2-s2.0-105003627857
École Polytechnique Fédérale de Lausanne
École Polytechnique Fédérale de Lausanne
Boston Children's Hospital
Tongji University
École Polytechnique Fédérale de Lausanne
Boston Children's Hospital
École Polytechnique Fédérale de Lausanne
2025-04-18
978-3-031-86920-4
Lecture Notes in Computer Science; 15171
1611-3349
0302-9743
24
34
REVIEWED
EPFL
Event name | Event acronym | Event place | Event date |
Marrakesh, Morocco | 2024-10-06 - 2024-10-06 | ||
Funder | Funding(s) | Grant Number | Grant URL |
Leenaards and Jeantet Foundations | |||
University of Lausanne | |||
University of Geneva | |||
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