论文标题
用深对照的脑部MRI代替gadolinium
Substituting Gadolinium in Brain MRI Using DeepContrast
论文作者
论文摘要
脑血容量（CBV）是氧代谢的血液动力学相关性，反映了大脑的活性和功能。高分辨率CBV地图可以使用稳态gadolinium增强的MRI技术生成。这样的技术需要静脉注射外源性的基于gadolinium的对比剂（GBCA），并且最近的研究表明，经常使用后，GBCA可以在大脑中积累。我们假设内源性对比来源可能存在于最常规和通常获得的结构MRI中，从而有可能消除对外源性对比的需求。在这里，我们通过在小鼠中开发和优化一种我们称为DeepContrast的深度学习算法来检验这一假设。我们发现，在映射正常脑组织的CBV并增强胶质母细胞瘤时，深对比度的性能与外源GBCA同样良好。这些研究共同证明了我们的假设，即深度学习方法可以取代大脑MRI中对GBCA的需求。
Cerebral blood volume (CBV) is a hemodynamic correlate of oxygen metabolism and reflects brain activity and function. High-resolution CBV maps can be generated using the steady-state gadolinium-enhanced MRI technique. Such a technique requires an intravenous injection of exogenous gadolinium based contrast agent (GBCA) and recent studies suggest that the GBCA can accumulate in the brain after frequent use. We hypothesize that endogenous sources of contrast might exist within the most conventional and commonly acquired structural MRI, potentially obviating the need for exogenous contrast. Here, we test this hypothesis by developing and optimizing a deep learning algorithm, which we call DeepContrast, in mice. We find that DeepContrast performs equally well as exogenous GBCA in mapping CBV of the normal brain tissue and enhancing glioblastoma. Together, these studies validate our hypothesis that a deep learning approach can potentially replace the need for GBCAs in brain MRI.
