通过填补任意量化代码实现脑部MRI的虚拟全栈扫描
Virtual Full-stack Scanning of Brain MRI via Imputing Any Quantised Code
AI总结：
本文提出CodeBrain框架，通过区域级全栈代码预测解决MRI模态缺失问题，提升数据完整性和临床实用性。
摘要：
磁共振成像（MRI）是一种强大的成像技术，通过不同采集模态提供丰富的解剖信息。然而，由于扫描时间和成本限制，在临床工作中无法收集所有相关模态。虚拟全栈扫描旨在从不完整的采集中填补缺失的MRI模态，提供一种成本效益高的解决方案以增强数据完整性和临床实用性。现有填补方法往往依赖全局条件或模态特定设计，限制了其在不同患者群体和成像协议中的泛化能力。为了解决这些限制，我们提出了CodeBrain，一个统一的框架，将各种“任何到任何”的填补任务重新表述为区域级全栈代码预测问题。CodeBrain采用两阶段流程：（1）通过将MRI模态集编码为区域级标量量化代码，学习其紧凑表示，解码这些代码并结合模态无关的通用特征可实现高保真图像重建；（2）训练一个投影编码器，通过分级设计预测从不完整模态到全栈代码图的映射，以适应多样化的填补场景。在两个公开的脑部MRI数据集（即IXI和BraTS 2023）上的大量实验表明，CodeBrain在性能上始终优于最先进的方法，建立了统一脑部MRI填补的新基准，并实现了虚拟全栈扫描。我们的代码将在https://github.com/ycwu1997/CodeBrain上发布。
英文摘要：
Magnetic resonance imaging (MRI) is a powerful and versatile imaging technique, offering a wide spectrum of information about the anatomy by employing different acquisition modalities. However, in the clinical workflow, it is impractical to collect all relevant modalities due to the scan time and cost constraints. Virtual full-stack scanning aims to impute missing MRI modalities from available but incomplete acquisitions, offering a cost-efficient solution to enhance data completeness and clinical usability. Existing imputation methods often depend on global conditioning or modality-specific designs, which limit their generalisability across patient cohorts and imaging protocols. To address these limitations, we propose CodeBrain, a unified framework that reformulates various ``any-to-any'' imputation tasks as a region-level full-stack code prediction problem. CodeBrain adopts a two-stage pipeline: (1) it learns the compact representation of a complete MRI modality set by encoding it into scalar-quantised codes at the region level, enabling high-fidelity image reconstruction after decoding these codes along with modality-agnostic common features; (2) it trains a projection encoder to predict the full-stack code map from incomplete modalities via a grading-based design for diverse imputation scenarios. Extensive experiments on two public brain MRI datasets, i.e., IXI and BraTS 2023, demonstrate that CodeBrain consistently outperforms state-of-the-art methods, establishing a new benchmark for unified brain MRI imputation and enabling virtual full-stack scanning. Our code will be released at https://github.com/ycwu1997/CodeBrain.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
