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Model-based deep learning with fully connected neural networks for accelerated magnetic resonance parameter mapping
Naoto Fujita1, Suguru Yokosawa2, Toru Shirai2
1Institute of Pure and Applied Physics, University of Tsukuba, Tsukuba, Japan.
Purpose:
Quantitative magnetic resonance imaging (qMRI) enables imaging of physical parameters related to the nuclear spin of protons in tissue, and is poised to revolutionize clinical research. However, improving the accuracy and clinical relevance of qMRI is essential for its practical implementation. This requires significantly reducing the currently lengthy acquisition times to enable clinical examinations and provide an environment where clinical accuracy and reliability can be verified. Deep learning (DL) has shown promise in significantly reducing imaging time and improving image quality in recent years. This study introduces a novel approach, quantitative deep cascade of convolutional network (qDC-CNN), as a framework for accelerated quantitative parameter mapping, offering a potential solution to this challenge. This work aims to verify that the proposed model outperforms the competing methods.
Methods:
The proposed qDC-CNN is an integrated deep-learning framework combining an unrolled image reconstruction network and a fully connected neural network for parameter estimation. Training and testing utilized simulated multi-slice multi-echo (MSME) datasets generated from the BrainWeb database. The reconstruction error with ground truth was evaluated using normalized root mean squared error (NRMSE) and compared with conventional DL-based methods. Two validation experiments were performed: (Experiment 1) assessment of acceleration factor (AF) dependency (AF = 5, 10, 20) with fixed 16 echoes, and (Experiment 2) evaluation of the impact of reducing contrast images (16, 8, 4 images).
Results:
In most cases, the NRMSE values of S0 and T2 estimated from the proposed qDC-CNN were within 10%. In particular, the NRMSE values of T2 were much smaller than those of the conventional methods.
Conclusions:
The proposed model had significantly smaller reconstruction errors than the conventional models. The proposed method can be applied to other qMRI sequences and has the flexibility to replace the image reconstruction module to improve performance.
