Kavli Affiliate: Li Xin Li
| First 5 Authors: Jiangjie Wu, Lixuan Chen, Zhenghao Li, Xin Li, Saban Ozturk
| Summary:
High-quality 3D fetal brain MRI reconstruction from motion-corrupted 2D
slices is crucial for clinical diagnosis. Reliable slice-to-volume registration
(SVR)-based motion correction and super-resolution reconstruction (SRR) methods
are essential. Deep learning (DL) has demonstrated potential in enhancing SVR
and SRR when compared to conventional methods. However, it requires large-scale
external training datasets, which are difficult to obtain for clinical fetal
MRI. To address this issue, we propose an unsupervised iterative SVR-SRR
framework for isotropic HR volume reconstruction. Specifically, SVR is
formulated as a function mapping a 2D slice and a 3D target volume to a rigid
transformation matrix, which aligns the slice to the underlying location in the
target volume. The function is parameterized by a convolutional neural network,
which is trained by minimizing the difference between the volume slicing at the
predicted position and the input slice. In SRR, a decoding network embedded
within a deep image prior framework is incorporated with a comprehensive image
degradation model to produce the high-resolution (HR) volume. The deep image
prior framework offers a local consistency prior to guide the reconstruction of
HR volumes. By performing a forward degradation model, the HR volume is
optimized by minimizing loss between predicted slices and the observed slices.
Comprehensive experiments conducted on large-magnitude motion-corrupted
simulation data and clinical data demonstrate the superior performance of the
proposed framework over state-of-the-art fetal brain reconstruction frameworks.
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