FOD-mixer: an MLP-based framework for fiber orientation distribution reconstruction

Lingmei Ai; Hanyang Yu; Yunfan Shi

doi:10.1117/12.3048323

3 October 2024 FOD-mixer: an MLP-based framework for fiber orientation distribution reconstruction

Lingmei Ai, Hanyang Yu, Yunfan Shi

Author Affiliations +

Proceedings Volume 13272, Fifth International Conference on Computer Vision and Data Mining (ICCVDM 2024); 132721M (2024) https://doi.org/10.1117/12.3048323
Event: 5th International Conference on Computer Vision and Data Mining (ICCVDM 2024), 2024, Changchun, China

Abstract

Fiber orientation distribution (FOD) is crucial for resolving complex fiber structures. Existing methods fail to fully utilize global context when reconstructing FOD, leading to inductive bias. Furthermore, they typically ignore the spatial positional relationships of FOD, resulting in limited global continuity captured. This study addresses these issues by introducing a novel network called FOD-Mixer. The FOD-Mixer uses the capability of the multi-layer perceptron to extract long-range dependencies, thereby circumventing inductive biases. In addition, we introduce a dynamic-mixer attention mechanism, which simulates more intricate spatial relationships by dynamically computing the mixing matrix. Furthermore, we propose an attention-based pooling module to filter out noise information. Experimental results demonstrate that the global contextual and spatial information provided by the FOD-Mixer can significantly enhance reconstruction tasks. The attention-based pooling module also effectively filters noise, further augmenting the reconstruction accuracy of the FOD-Mixer. Our research substantiates the advantages and feasibility of the FOD-Mixer in reconstructing high-quality FOD, offering a reliable reference for clinical applications.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Lingmei Ai, Hanyang Yu, and Yunfan Shi "FOD-mixer: an MLP-based framework for fiber orientation distribution reconstruction", Proc. SPIE 13272, Fifth International Conference on Computer Vision and Data Mining (ICCVDM 2024), 132721M (3 October 2024); https://doi.org/10.1117/12.3048323

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