Purpose: The aim of this work is to introduce a single model-based deep
network that can provide high-quality reconstructions from undersampled
parallel MRI data acquired with multiple sequences, acquisition settings and
field strengths.
Methods: A single unrolled architecture, which offers good reconstructions
for multiple acquisition settings, is introduced. The proposed scheme adapts
the model to each setting by scaling the CNN features and the regularization
parameter with appropriate weights. The scaling weights and regularization
parameter are derived using a multi-layer perceptron model from conditional
vectors, which represents the specific acquisition setting. The perceptron
parameters and the CNN weights are jointly trained using data from multiple
acquisition settings, including differences in field strengths, acceleration,
and contrasts. The conditional network is validated using datasets acquired
with different acquisition settings.
Results: The comparison of the adaptive framework, which trains a single
model using the data from all the settings, shows that it can offer
consistently improved performance for each acquisition condition. The
comparison of the proposed scheme with networks that are trained independently
for each acquisition setting shows that it requires less training data per
acquisition setting to offer good performance.
Conclusion: The Ada-MoDL framework enables the use of a single model-based
unrolled network for multiple acquisition settings. In addition to eliminating
the need to train and store multiple networks for different acquisition
settings, this approach reduces the training data needed for each acquisition
setting.

此研究介绍了一种针对不同磁场强度、加速和对比度等多种采集条件的 MRI 数据的单一基于模型的深度神经网络，可提供高质量的重建。通过使用一个可滚动的体系结构，基于每个捕获条件调整 CNN 特征和正则化参数的权重，利用来自多个采集条件的数据共同训练模型，证明该模型可以始终提供更好的性能，同时减少了每个采集设置所需的训练数据。