Purpose: To develop a deep-learning-based image reconstruction framework for reproducible research in MRI. Methods: The BART toolbox offers a rich set of implementations of calibration and reconstruction algorithms for parallel imaging and compressed sensing. In this work, BART was extended by a non-linear operator framework that provides automatic differentiation to allow computation of gradients. Existing MRI-specific operators of BART, such as the non-uniform fast Fourier transform, are directly integrated into this framework and are complemented by common building blocks used in neural networks. To evaluate the use of the framework for advanced deep-learning-based reconstruction, two state-of-the-art unrolled reconstruction networks, namely the Variational Network [1] and MoDL [2], were implemented. Results: State-of-the-art deep image-reconstruction networks can be constructed and trained using BART's gradient based optimization algorithms. The BART implementation achieves a similar performance in terms of training time and reconstruction quality compared to the original implementations based on TensorFlow. Conclusion: By integrating non-linear operators and neural networks into BART, we provide a general framework for deep-learning-based reconstruction in MRI.
翻译:在这项工作中,通过非线性操作者框架扩展了BART, 提供自动差异,以便计算梯度。BART的现有具体MRI操作者,如非统一快速Fourier变换,直接融入这一框架,并辅之以神经网络中使用的共同建筑构件。评价先进深层重建框架的使用情况,两个最先进的非现代化非滚动重建网络,即动态网络[1]和MDL[2]已经实施。结果:最先进的深层图像重建网络可以使用基于BART的梯度优化算法建造和培训。BART的实施在培训时间和重建质量方面与基于TensorFlow的原始实施方面业绩相似。结论:通过将非线性运营者和神经网络纳入BART的深层重建框架,我们为深层重建提供了一个总体框架。