In structural magnetic resonance (MR) imaging, motion artefacts, low resolution, imaging noise and variability in acquisition protocols frequently degrade image quality and confound downstream analyses. Here we report a foundation model for the motion correction, resolution enhancement, denoising and harmonization of MR images. Specifically, we trained a tissue-classification neural network to predict tissue labels, which are then leveraged by a ‘tissue-aware’ enhancement network to generate high-quality MR images. We validated the model’s effectiveness on a large and diverse dataset comprising 2,448 deliberately corrupted images and 10,963 images spanning a wide age range (from foetuses to elderly individuals) acquired using a variety of clinical scanners across 19 public datasets. The model consistently outperformed state-of-the-art algorithms in improving the quality of MR images, handling pathological brains with multiple sclerosis or gliomas, generating 7-T-like images from 3 T scans and harmonizing images acquired from different scanners. The high-quality, high-resolution and harmonized images generated by the model can be used to enhance the performance of models for tissue segmentation, registration, diagnosis and other downstream tasks.

在结构磁共振 （MR） 成像中，运动伪影、低分辨率、成像噪声和采集方案的可变性经常会降低图像质量并混淆下游分析。在这里，我们报告了 MR 图像的运动校正、分辨率增强、去噪和协调的基础模型。具体来说，我们训练了一个组织分类神经网络来预测组织标签，然后 “组织感知 ”增强网络利用这些标签来生成高质量的 MR 图像。我们在一个大型且多样化的数据集上验证了该模型的有效性，该数据集包括 2,448 张故意损坏的图像和 10,963 张图像，涵盖广泛的年龄范围（从胎儿到老年人），这些图像是使用 19 个公共数据集中的各种临床扫描仪获取的。该模型在提高 MR 图像质量、处理患有多发性硬化症或神经胶质瘤的病理性大脑、从 3 T 扫描生成 7-T 样图像以及协调从不同扫描仪获取的图像方面始终优于最先进的算法。该模型生成的高质量、高分辨率和协调的图像可用于增强模型在组织分割、配准、诊断和其他下游任务方面的性能。