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Abstract #0698

Asymmetric Fiber Orientation Distribution Estimation and Response Function Calibration with an Unsupervised Deconvolution Network

Di Zhang1, Honglin Fang1, Yong Liu1, and Fangrong Zong1
1Beijing University of Posts and Telecommunications, Beijing, China

Synopsis

Keywords: Diffusion Modeling, Diffusion Reconstruction, Fiber Orientation Distribution

Motivation: Fiber orientation distribution function (fODF) and response function (RF) influence each other from diffusion MRI. To capture more detailed fiber patterns, estimating asymmetric fODFs (a-fODFs) and accurate RF helps further exploration on white matter microstructure.

Goal(s): Utilize the spatial information and RF calibration to estimate a-FODs and RF synchronously.

Approach: We construct an unsupervised spherical deconvolution network to estimate the a-fODFs, and apply it within a recursive framework to calibrate both RF and a-fODFs simultaneously.

Results: The fiber connectivity from a-fODFs shows stronger correlations with the labeled neuron connectivity from neuronal tracing data, and exhibits great robustness on test-retest data.

Impact: Our method conducts the unsupervised estimation of a-fODFs and RF without the histological data. Compared with symmetric fODFs, the a-fODFs capture complex fiber patterns, providing a broader perspective for downstream tractography and white matter microstructure analysis.

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