In recent years, convolutional neural networks (CNNs) have shown their advantages on MR image super-resolution (SR) tasks. Many current SR models, however, have heavy demands on computation and memory, which are not friendly to magnetic resonance imaging (MRI) where computing resource is usually constrained. On the other hand, a basic consideration in most MRI experiments is how to reduce scanning time to improve patient comfort and reduce motion artifacts. In this work, we ease the problem by presenting an effective and lightweight model that supports fast training and accurate SR inference. The proposed network is inspired by the lateral inhibition mechanism, which assumes that there exist inhibitory effects between adjacent neurons. The backbone of our network consists of several lateral inhibition blocks, where the inhibitory effect is explicitly implemented by a battery of cascaded local inhibition units. When model scale is small, explicitly inhibiting feature activations is expected to further explore model representational capacity. For more effective feature extraction, several parallel dilated convolutions are also used to extract shallow features directly from the input image. Extensive experiments on typical MR images demonstrate that our lateral inhibition network (LIN) achieves better SR performance than other lightweight models with similar model scale.

近年来，卷积神经网络（CNN）在MR图像超分辨率（SR）任务上显示了其优势。但是，许多当前的SR模型对计算和内存有很高的要求，这对通常会限制计算资源的磁共振成像（MRI）不友好。另一方面，大多数MRI实验的基本考虑是如何减少扫描时间以提高患者舒适度并减少运动伪影。在这项工作中，我们通过提供有效且轻量级的模型来缓解问题，该模型支持快速训练和准确的SR推理。拟议的网络受到侧向抑制机制的启发，该机制假定相邻神经元之间存在抑制作用。我们网络的主干由几个侧向抑制块组成，其中抑制作用是由一系列级联的局部抑制单元明确实现的。当模型规模较小时，显式抑制特征激活有望进一步探索模型的表示能力。为了更有效地提取特征，还使用了几个并行的扩展卷积来直接从输入图像中提取浅层特征。在典型MR图像上进行的大量实验表明，与其他具有类似模型规模的轻型模型相比，我们的横向抑制网络（LIN）具有更好的SR性能。几个平行的膨胀卷积也用于直接从输入图像中提取浅层特征。在典型MR图像上进行的大量实验表明，与其他具有类似模型规模的轻型模型相比，我们的横向抑制网络（LIN）具有更好的SR性能。几个并行的膨胀卷积也用于直接从输入图像中提取浅层特征。在典型MR图像上进行的大量实验表明，与其他具有类似模型规模的轻型模型相比，我们的横向抑制网络（LIN）具有更好的SR性能。