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Reference-Driven Undersampled MR Image Reconstruction Using Wavelet Sparsity-Constrained Deep Image Prior.

Di Zhao^1,2, Yanhu Huang², Feng Zhao¹

¹Key Laboratory of Complex System Optimization and Big Data Processing, Guangxi Colleges and Universities, Yulin Normal University, Yulin 537000, China.

Computational and Mathematical Methods in Medicine

|February 8, 2021

Summary

This summary is machine-generated.

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This study introduces a novel deep learning method for faster magnetic resonance (MR) image reconstruction without needing large training datasets. The reference-driven wavelet sparsity-constrained deep image prior (RWS-DIP) method accurately reconstructs MR images, preserving crucial details.

Area of Science:

Medical Imaging
Artificial Intelligence
Signal Processing

Background:

Deep learning significantly enhances undersampled magnetic resonance (MR) image reconstruction.
Clinical application of deep learning is hindered by the need for extensive, high-quality patient datasets for training.

Purpose of the Study:

To propose a novel deep learning method for undersampled MR image reconstruction that eliminates the need for pre-training datasets.
To introduce a method that reduces data dependence while improving reconstruction accuracy and efficiency.

Main Methods:

Developed a reference-driven method using wavelet sparsity-constrained deep image prior (RWS-DIP), based on the Deep Image Prior (DIP) framework.
Incorporated structural information using a high-resolution reference image as network input.

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Utilized wavelet sparsity and formulated network training as a constrained optimization problem solved with the alternating direction method of multipliers (ADMM).

Main Results:

The RWS-DIP method demonstrated accurate MR image reconstruction from undersampled k-space measurements.
The method effectively preserved essential image features and textures.
Experiments on in vivo MR scans validated the performance of RWS-DIP.

Conclusions:

The RWS-DIP method offers a viable deep learning solution for undersampled MR image reconstruction, overcoming data dependency limitations.
This approach enhances reconstruction quality and efficiency by integrating structural and sparsity priors.
The findings suggest a promising direction for improving clinical MR imaging workflows.