近年来,机器学习技术广泛用于从功能磁共振成像(functional magnetic resonance imaging,f MRI)数据中解码视觉信息、精神状态、情绪和其它感兴趣的大脑感知和认知功能。然而,由于fMRI数据样本维数高,样本量少,一般需要利用特征提取方...近年来,机器学习技术广泛用于从功能磁共振成像(functional magnetic resonance imaging,f MRI)数据中解码视觉信息、精神状态、情绪和其它感兴趣的大脑感知和认知功能。然而,由于fMRI数据样本维数高,样本量少,一般需要利用特征提取方法去除多余的预测变量和实验噪声等信息,避免机器学习模型出现过拟合问题,提高模型的预测准确率和泛化能力。介绍和讨论了常用fMRI数据有监督特征提取方法的一般原理和研究现状,并着重分析其性能和可能改进方向,最后对特征提取方法在fMRI中的研究方向进行了展望。展开更多
To evaluae small animal imaging with individual different high voltage, filter thickness and tube current, an animal X-ray micro-computed tomography （micro-CT） system based on panel detector is developed and a rat i...To evaluae small animal imaging with individual different high voltage, filter thickness and tube current, an animal X-ray micro-computed tomography （micro-CT） system based on panel detector is developed and a rat is scanned by using the system with individual high voltage, tube current, filter thickness, and exposure time. A model is presented based on the Monte Carlo code PENELOPE for generating the X-ray spectra of X-ray tube used in the micro-CT system. A platform developed based on Matlab allows for calculating beam quality parameters, including the average energy of X-ray beam, the change of transmition rate and the input X-ray fluence. The factors affecting the signal difference to noise ratio （SDNR） of micro-CT are investigated and the relationship between SDNR and scan combinations is analyzed. A series of tools and methods are developed for small animal imaging and imaging performance evaluation in the field of small animal imaging.展开更多
文摘近年来,机器学习技术广泛用于从功能磁共振成像(functional magnetic resonance imaging,f MRI)数据中解码视觉信息、精神状态、情绪和其它感兴趣的大脑感知和认知功能。然而,由于fMRI数据样本维数高,样本量少,一般需要利用特征提取方法去除多余的预测变量和实验噪声等信息,避免机器学习模型出现过拟合问题,提高模型的预测准确率和泛化能力。介绍和讨论了常用fMRI数据有监督特征提取方法的一般原理和研究现状,并着重分析其性能和可能改进方向,最后对特征提取方法在fMRI中的研究方向进行了展望。
基金Supported by the National Natural Science Foundation of China (60672104,10527003)the Nation-al Basic Research Program of China ("973"Program)(2006CB705705)the Joint Research Foundation of Beijing Mu-nicipal Commission of Education (JD100010607)~~
文摘To evaluae small animal imaging with individual different high voltage, filter thickness and tube current, an animal X-ray micro-computed tomography （micro-CT） system based on panel detector is developed and a rat is scanned by using the system with individual high voltage, tube current, filter thickness, and exposure time. A model is presented based on the Monte Carlo code PENELOPE for generating the X-ray spectra of X-ray tube used in the micro-CT system. A platform developed based on Matlab allows for calculating beam quality parameters, including the average energy of X-ray beam, the change of transmition rate and the input X-ray fluence. The factors affecting the signal difference to noise ratio （SDNR） of micro-CT are investigated and the relationship between SDNR and scan combinations is analyzed. A series of tools and methods are developed for small animal imaging and imaging performance evaluation in the field of small animal imaging.
