A scan-specific unsupervised method for parallel MRI reconstruction via
implicit neural representation
- URL: http://arxiv.org/abs/2210.10439v1
- Date: Wed, 19 Oct 2022 10:16:03 GMT
- Title: A scan-specific unsupervised method for parallel MRI reconstruction via
implicit neural representation
- Authors: Ruimin Feng, Qing Wu, Yuyao Zhang and Hongjiang Wei
- Abstract summary: implicit neural representation (INR) has emerged as a new deep learning paradigm for learning the internal continuity of an object.
The proposed method outperforms existing methods by suppressing the aliasing artifacts and noise.
The high-quality results and scanning specificity make the proposed method hold the potential for further accelerating the data acquisition of parallel MRI.
- Score: 9.388253054229155
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Parallel imaging is a widely-used technique to accelerate magnetic resonance
imaging (MRI). However, current methods still perform poorly in reconstructing
artifact-free MRI images from highly undersampled k-space data. Recently,
implicit neural representation (INR) has emerged as a new deep learning
paradigm for learning the internal continuity of an object. In this study, we
adopted INR to parallel MRI reconstruction. The MRI image was modeled as a
continuous function of spatial coordinates. This function was parameterized by
a neural network and learned directly from the measured k-space itself without
additional fully sampled high-quality training data. Benefitting from the
powerful continuous representations provided by INR, the proposed method
outperforms existing methods by suppressing the aliasing artifacts and noise,
especially at higher acceleration rates and smaller sizes of the
auto-calibration signals. The high-quality results and scanning specificity
make the proposed method hold the potential for further accelerating the data
acquisition of parallel MRI.
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