Magnetic resonance imaging (MRI) tasks often involve multiple contrasts. Recently, numerous deep learning-based multi-contrast MRI super-resolution (SR) and reconstruction methods have been proposed to explore the complementary information from the multi-contrast images. However, these methods either construct parameter-sharing networks or manually design fusion rules, failing to accurately model the correlations between multi-contrast images and lacking certain interpretations. In this paper, we propose a multi-contrast convolutional dictionary (MC-CDic) model under the guidance of the optimization algorithm with a well-designed data fidelity term. Specifically, we bulid an observation model for the multi-contrast MR images to explicitly model the multi-contrast images as common features and unique features. In this way, only the useful information in the reference image can be transferred to the target image, while the inconsistent information will be ignored. We employ the proximal gradient algorithm to optimize the model and unroll the iterative steps into a deep CDic model. Especially, the proximal operators are replaced by learnable ResNet. In addition, multi-scale dictionaries are introduced to further improve the model performance. We test our MC-CDic model on multi-contrast MRI SR and reconstruction tasks. Experimental results demonstrate the superior performance of the proposed MC-CDic model against existing SOTA methods. Code is available at https://github.com/lpcccc-cv/MC-CDic.

翻译：磁共振成像任务常涉及多种对比度。近年来，大量基于深度学习的多对比度MRI超分辨率与重建方法被提出，以探索多对比度图像中的互补信息。然而，这些方法或构建参数共享网络、或手动设计融合规则，未能精确建模多对比度图像间的相关性，且缺乏一定可解释性。本文提出一种在优化算法引导下、具有精心设计的数据保真项的多对比度卷积字典模型。具体而言，我们为多对比度MR图像构建观测模型，将多对比度图像显式建模为共有特征与独有特征。通过此方式，仅参考图像中的有用信息可传递至目标图像，而不一致信息将被忽略。我们采用近端梯度算法优化该模型，并将迭代步骤展开为深度CDic模型。特别地，近端算子被替换为可学习的ResNet。此外，引入多尺度字典以进一步提升模型性能。我们在多对比度MRI超分辨率与重建任务上测试所提出的MC-CDic模型。实验结果表明，该模型相较于现有最先进方法具有优越性能。代码开源地址：https://github.com/lpcccc-cv/MC-CDic。