The discovery of the theory of compressed sensing brought the realisation that many inverse problems can be solved even when measurements are “incomplete”. This is particularly interesting in magnetic resonance imaging (MRI), where long acquisition times can limit its use. In this work, we consider the problem of learning a sparse sampling pattern that can be used to optimally balance acquisition time versus quality of the reconstructed image. We use a supervised learning approach, making the assumption that our training data is representative enough of new data acquisitions. We demonstrate that this is indeed the case, even if the training data consists of just 7 training pairs of measurements and ground-truth images; with a training set of brain images of size 192 by 192, for instance, one of the learned patterns samples only 35% of k-space, however results in reconstructions with mean SSIM 0.914 on a test set of similar images. The proposed framework is general enough to learn arbitrary sampling patterns, including common patterns such as Cartesian, spiral and radial sampling.

中文翻译：

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学习MRI的采样模式。

压缩感测理论的发现带来了这样的认识：即使测量“不完整”，许多逆问题也可以解决。这在磁共振成像（MRI）中特别有趣，在磁共振成像中，较长的采集时间可能会限制其使用。在这项工作中，我们考虑了学习稀疏采样模式的问题，该模式可用于最佳地平衡采集时间与重建图像的质量。我们使用监督学习方法，并假设我们的训练数据足以代表新数据的获取。我们证明，即使训练数据仅由7个训练对测量值和真实图像组成，情况确实如此。例如，通过训练一组大小为192 x 192的大脑图像，其中一个学习模式仅采样了35％的k空间，但是，在相似图像的测试集上的平均SSIM为0.914。提出的框架足够通用，可以学习任意采样模式，包括笛卡尔，螺旋和径向采样等常见模式。