标题： 大规模多序列预训练用于多样化临床应用中的可泛化MRI分析 显示英文标题

标题： Large-scale Multi-sequence Pretraining for Generalizable MRI Analysis in Versatile Clinical Applications

摘要： 多序列磁共振成像（MRI）提供了显著的灵活性，能够实现不同组织类型的独特可视化。 然而，MRI序列之间的固有异质性对深度学习模型的泛化能力构成了重大挑战。 这些挑战在面对不同的采集参数时会削弱模型性能，从而严重限制了其临床实用性。 在本研究中，我们提出了PRISM，一个使用大规模多序列MRI预训练的基础模型。 我们从公共和私人来源收集了总共64个数据集，涵盖了广泛的全身解剖结构，扫描涵盖了多种MRI序列。 其中，来自34个数据集（8个公共和26个私人）的336,476个体积MRI扫描被整理成迄今为止最大的多器官多序列MRI预训练语料库。 我们提出了一种新的预训练范式，能够将解剖学不变特征与MRI中的序列特定变化分离，同时保留高层语义表示。 我们建立了一个包含44个下游任务的基准，包括疾病诊断、图像分割、配准、进展预测和报告生成。 这些任务在32个公共数据集和5个私人队列上进行了评估。 PRISM始终优于非预训练模型和现有基础模型，在44个下游基准中有39个取得了第一名的结果，并且有统计学意义的提升。 这些结果证明了它在不同MRI协议下获取的未见数据上学习稳健和泛化表示的能力。 PRISM提供了一个可扩展的多序列MRI分析框架，从而增强了放射学中人工智能的转化潜力。 它在各种成像协议中表现出一致的性能，强化了其临床适用性。 显示英文摘要

摘要： Multi-sequence Magnetic Resonance Imaging (MRI) offers remarkable versatility, enabling the distinct visualization of different tissue types. Nevertheless, the inherent heterogeneity among MRI sequences poses significant challenges to the generalization capability of deep learning models. These challenges undermine model performance when faced with varying acquisition parameters, thereby severely restricting their clinical utility. In this study, we present PRISM, a foundation model PRe-trained with large-scale multI-Sequence MRI. We collected a total of 64 datasets from both public and private sources, encompassing a wide range of whole-body anatomical structures, with scans spanning diverse MRI sequences. Among them, 336,476 volumetric MRI scans from 34 datasets (8 public and 26 private) were curated to construct the largest multi-organ multi-sequence MRI pretraining corpus to date. We propose a novel pretraining paradigm that disentangles anatomically invariant features from sequence-specific variations in MRI, while preserving high-level semantic representations. We established a benchmark comprising 44 downstream tasks, including disease diagnosis, image segmentation, registration, progression prediction, and report generation. These tasks were evaluated on 32 public datasets and 5 private cohorts. PRISM consistently outperformed both non-pretrained models and existing foundation models, achieving first-rank results in 39 out of 44 downstream benchmarks with statistical significance improvements. These results underscore its ability to learn robust and generalizable representations across unseen data acquired under diverse MRI protocols. PRISM provides a scalable framework for multi-sequence MRI analysis, thereby enhancing the translational potential of AI in radiology. It delivers consistent performance across diverse imaging protocols, reinforcing its clinical applicability.