超急性期脑缺血影像量化困难但却是实现个性化诊疗的关键。本项目基于图像处理理论，利用对脑缺血表征好的磁敏感加权图像SWI与弥散加权成像DWI(包括弥散加权图像和表观弥散系数图)，研究病变组织的分割及多模态图像信息融合。基于我们在DWI量化的研究基础，本项目将通过深度学习获取病变组织的灰度特征、构造概率图谱进行位置约束、添加边缘约束以分割小的目标物、将这些特征约束集成为条件随机场分割框架并分割SWI中的低信号静脉血管；研究基于稀疏理论分割DWI中的初始缺血区域；研究基于直线灰度分布模式检测SWI中的低信号并添加位置与形状约束识别微出血与动脉内血栓；结合中矢状面对应、最大化灰度变化显著区域的互信息实现同一病人脑影像的非线性配准；探索SWI与DWI缺血信息融合并识别、量化良性缺血半暗带。研究成果为建立基于影像量化特征的诊疗提供方法支持，对病变组织的分割、多模态图像的非线性配准及信息融合有参考价值。

Quantification of ischemia from hyper-acute ischemic stroke neuro-imaging is challenging to be explored as it is the prerequisite for patient-specific therapy. Based on theories of image processing, the study is to investigate the segmentation of pathological tissues and fusion of multi-modal images from two imaging modalities which are both sensitive and specific to hyper-acute ischemia, i.e., susceptibility weighted image (SWI) and diffusion weighted imaging (DWI) (including diffusion weighted image and apparent diffusion coefficient map). Starting from what we have achieved on ischemia quantification from DWI, we are going to carry out the following studies. We will learn the grayscale characteristics from deep learning, to add location constraints from building probabilistic atlases, to segment small objects from adding edge constraints, and to aggregate these features and constraints into the framework of a conditional random field, as a new way to solve the long-existing problem of segmenting pathological tissues from images and applying the framework to segment hypointense veins from SWIs. We will employ sparse theory for initial segmentation of ischemia from DWI. We will explore line-shaped profiles to detect hypointense regions from SWI and recognize intracranial thrombus and cerebral microbleeds by virtue of shape and location constraints. We will constrain non-linear registration by adding correspondence between midsagittal planes and maximizing the mutual information in image regions with moderate to significant local grayscale variations. We will fuse the initial ischemia from DWI and veins from SWI to well represent the characteristics of ischemia, and recognize and quantify favorable ischemic penumbra. The study is to provide mathematical and theoretical support for diagnosis and therapy of hyper-acute ischemia based on quantitative imaging features. The theories and methods explored will be valuable for segmenting pathological tissues, nonlinear image registration, and information fusion from multi-modal images.