Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, PA, USA; Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
Department of Neurology, University of California Los Angeles, Los Angeles, CA, USA.
Neuroimage. 2015 Jan 15;105:156-70. doi: 10.1016/j.neuroimage.2014.10.052. Epub 2014 Nov 1.
We present RIPMMARC (Rotation Invariant Patch-based Multi-Modality Analysis aRChitecture), a flexible and widely applicable method for extracting information unique to a given modality from a multi-modal data set. We use RIPMMARC to improve the interpretation of arterial spin labeling (ASL) perfusion images by removing the component of perfusion that is predicted by the underlying anatomy. Using patch-based, rotation invariant descriptors derived from the anatomical image, we learn a predictive relationship between local neuroanatomical structure and the corresponding perfusion image. This relation allows us to produce an image of perfusion that would be predicted given only the underlying anatomy and a residual image that represents perfusion information that cannot be predicted by anatomical features. Our learned structural features are significantly better at predicting brain perfusion than tissue probability maps, which are the input to standard partial volume correction techniques. Studies in test-retest data show that both the anatomically predicted and residual perfusion signals are highly replicable for a given subject. In a pediatric population, both the raw perfusion and structurally predicted images are tightly linked to age throughout adolescence throughout the brain. Interestingly, the residual perfusion also shows a strong correlation with age in selected regions including the hippocampi (corr = 0.38, p-value <10(-6)), precuneus (corr = -0.44, p < 10(-5)), and combined default mode network regions (corr = -0.45, p < 10(-8)) that is independent of global anatomy-perfusion trends. This finding suggests that there is a regionally heterogeneous pattern of functional specialization that is distinct from that of cortical structural development.
我们提出了 RIPMMARC（基于旋转不变补丁的多模态分析架构），这是一种从多模态数据集提取特定模态特有信息的灵活且广泛适用的方法。我们使用 RIPMMARC 来通过去除由基础解剖结构预测的灌注分量来改善动脉自旋标记（ASL）灌注图像的解释。我们使用基于补丁的、旋转不变的描述符从解剖图像中提取，这些描述符从局部神经解剖结构和相应的灌注图像之间学习预测关系。该关系使我们能够生成仅基于基础解剖结构和代表无法通过解剖特征预测的灌注信息的残留图像就可以预测的灌注图像。我们学习到的结构特征在预测大脑灌注方面明显优于组织概率图，组织概率图是标准部分体积校正技术的输入。在测试-重测数据的研究中，对于给定的受试者，预测的和残留的灌注信号都具有高度可重复性。在儿科人群中，原始灌注和结构预测图像在整个青春期整个大脑中都与年龄紧密相关。有趣的是，残留的灌注在包括海马（相关系数 = 0.38，p 值 <10(-6))、楔前叶（相关系数 = -0.44，p < 10(-5))和默认模式网络区域（相关系数 = -0.45，p < 10(-8))在内的选定区域中与年龄也具有很强的相关性，这与全局解剖-灌注趋势无关。这一发现表明，存在与皮质结构发育不同的功能特化的区域异质性模式。
