论文标题
学习采样和基于模型的信号恢复以进行压缩感测MRI
Learning Sampling and Model-Based Signal Recovery for Compressed Sensing MRI
论文作者
论文摘要
压缩传感（CS）MRI依赖于足够的k空间采样来加速采集而不会损害图像质量。因此，这些K空间系数的最佳采样模式的设计受到了极大的关注，许多CS MRI方法利用了可变密度概率分布。意识到最佳采样模式可能取决于下游任务（例如图像重建，分割或分类），我们在这里提出了任务自适应K-Space采样和后续模型基于模型的近端近端分子恢复网络的联合学习。前者是通过概率的生成模型来实现的，该模型利用Gumbel-Softmax放松来跨越可训练的信念进行样品，同时保持可怜性。提出的高度柔性采样模型和基于模型的（采样自动）图像重建网络的组合促进了探索和有效的训练，与其他采样碱基相比，MR图像质量提高了。
Compressed sensing (CS) MRI relies on adequate undersampling of the k-space to accelerate the acquisition without compromising image quality. Consequently, the design of optimal sampling patterns for these k-space coefficients has received significant attention, with many CS MRI methods exploiting variable-density probability distributions. Realizing that an optimal sampling pattern may depend on the downstream task (e.g. image reconstruction, segmentation, or classification), we here propose joint learning of both task-adaptive k-space sampling and a subsequent model-based proximal-gradient recovery network. The former is enabled through a probabilistic generative model that leverages the Gumbel-softmax relaxation to sample across trainable beliefs while maintaining differentiability. The proposed combination of a highly flexible sampling model and a model-based (sampling-adaptive) image reconstruction network facilitates exploration and efficient training, yielding improved MR image quality compared to other sampling baselines.
