TL;DR
The paper introduces AFFIRM, a novel framework that iteratively corrects random fetal brain MRI motion artifacts by learning sequential motion and integrating features, significantly improving reconstruction accuracy and success rates.
Contribution
It presents AFFIRM, a new affinity fusion-based method that enhances motion correction in fetal MRI by combining slice-to-volume registration with iterative learning, outperforming existing methods.
Findings
48.4% reduction in mean absolute error for rotation
61.3% reduction in displacement error
Improved fetal brain super-resolution success rate from 77.2% to 91.9%
Abstract
Multi-slice magnetic resonance images of the fetal brain are usually contaminated by severe and arbitrary fetal and maternal motion. Hence, stable and robust motion correction is necessary to reconstruct high-resolution 3D fetal brain volume for clinical diagnosis and quantitative analysis. However, the conventional registration-based correction has a limited capture range and is insufficient for detecting relatively large motions. Here, we present a novel Affinity Fusion-based Framework for Iteratively Random Motion (AFFIRM) correction of the multi-slice fetal brain MRI. It learns the sequential motion from multiple stacks of slices and integrates the features between 2D slices and reconstructed 3D volume using affinity fusion, which resembles the iterations between slice-to-volume registration and volumetric reconstruction in the regular pipeline. The method accurately estimates the…
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