Fast Automatic Parameter Selection for MRI Reconstruction
Fast Automatic Parameter Selection for MRI Reconstruction
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DOI：
10.1109/isbi45749.2020.9098569
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发表时间：
2020-04
期刊：
影响因子：
--
通讯作者：
T. T. Toma-T.;D. Weller
中科院分区：
文献类型：
--
作者：
T. T. Toma-T.;D. Weller
为优化参数依赖的正则化重建算法的性能，提出了一种自动参数选择框架。该方法利用卷积神经网络从采集的成像数据中直接估计正则化参数。这种方法能够以计算高效的方式提供非常可靠的参数估计。基于变换学习的磁共振图像重建方法在两个不同的公开数据集上得到了验证。与现有的参数选择方案和有限训练数据的完全深度学习重建相比，本实验定性和定量地衡量了所提出的参数选择策略在图像重建质量方面的改善。基于实验结果，在一定的子采样因子和输入噪声水平范围内，该方法在大脑和膝关节数据集上的平均重建图像峰值信噪比比所有竞争方法都提高了1d B或更多。
This paper proposes an automatic parameter selection framework for optimizing the performance of parameter-dependent regularized reconstruction algorithms. The proposed approach exploits a convolutional neural network for direct estimation of the regularization parameters from the acquired imaging data. This method can provide very reliable parameter estimates in a computationally efficient way. The effectiveness of the proposed approach is verified on transform-learning-based magnetic resonance image reconstructions of two different publicly available datasets. This experiment qualitatively and quantitatively measures improvement in image reconstruction quality using the proposed parameter selection strategy versus both existing parameter selection solutions and a fully deep-learning reconstruction with limited training data. Based on the experimental results, the proposed method improves average reconstructed image peak signal-to-noise ratio by a dB or more versus all competing methods in both brain and knee datasets, over a range of subsampling factors and input noise levels.