Recovering high‐quality fiber orientation distributions from a reduced number of diffusion‐weighted images using a model‐driven deep learning architecture

• Published in: Magnetic resonance in medicine 2024-11, Vol.92 (5), p.2193-2206
• Main Authors: Bartlett, Joseph J., Davey, Catherine E., Johnston, Leigh A., Duan, Jinming
• Format: Article
• Language: English
• Subjects: Angular correlation; Classification; Correlation coefficients; Deconvolution; Deep learning; diffusion MRI; Fiber orientation; fixel‐based analysis; FOD reconstruction; Human performance; Image acquisition; Image quality; Image reconstruction; model‐based deep learning; Performance measurement
• ISSN: 0740-3194; 1522-2594; 1522-2594
• DOI: 10.1002/mrm.30187

Purpose The aim of this study was to develop a model‐based deep learning architecture to accurately reconstruct fiber orientation distributions (FODs) from a reduced number of diffusion‐weighted images (DWIs), facilitating accurate analysis with reduced acquisition times. Methods Our proposed architecture, Spherical Deconvolution Network (SDNet), performed FOD reconstruction by mapping 30 DWIs to fully sampled FODs, which have been fit to 288 DWIs. SDNet included DWI‐consistency blocks within the network architecture, and a fixel‐classification penalty within the loss function. SDNet was trained on a subset of the Human Connectome Project, and its performance compared with FOD‐Net, and multishell multitissue constrained spherical deconvolution. Results SDNet achieved the strongest results with respect to angular correlation coefficient and sum of squared errors. When the impact of the fixel‐classification penalty was increased, we observed an improvement in performance metrics reliant on segmenting the FODs into the correct number of fixels. Conclusion Inclusion of DWI‐consistency blocks improved reconstruction performance, and the fixel‐classification penalty term offered increased control over the angular separation of fixels in the reconstructed FODs.