Unsupervised 4D-flow MRI reconstruction based on partially-independent generative modeling and complex-difference sparsity constraint

Abstract

4D-flow MRI can provide spatiotemporal quantification of in-vivo blood flow velocity, which holds significant diagnostic value for various vascular diseases. Due to the large data size, 4D-flow MRI typically requires undersampling to shorten the scan time and employs reconstruction algorithms to recover images. Recently, deep learning methods have emerged for 4D-flow MRI reconstruction, but most of them are supervised algorithms, which have two major problems. First, supervised methods require high-quality fully sampled data for network training, which is usually very limited for 4D-flow MRI. Second, concerns are raised about the algorithm’s generalization ability since the morphology and velocity distribution vary in different vascular beds. In this work, we propose an unsupervised method for 4D-flow MRI reconstruction based on the deep image prior framework, which exploits the structural prior of convolutional neural networks for generative image recovery. Our method has three central components. First, we design a partially-independent network to improve the parameter efficiency and reduce the model size for 4D-flow MRI generation. Second, we incorporate the complex difference sparsity constraint to improve the accuracy of image phase recovery. Third, we introduce a joint generative and sparse optimization goal, and propose a “pretraining + ADMM finetuning” optimization algorithm for solution. Comprehensive experiments were conducted on two in-house acquired 4D-flow MRI datasets: an aorta dataset and a brain vessel dataset, compared with compressed-sensing algorithms and supervised deep-learning methods. The results demonstrate the superior reconstruction performance and generalization capability of the proposed method.