X-ray computed tomography (X-CT) is often used to examine organs, but the reconstructed images can only be used for structural identification. Whether the organs are healthy or not requires a professional doctor to examine the reconstructed image and judge from his or her own experience. The purpose of this paper is to identify the cirrhotic mouse liver and normal mouse liver with hyperspectral x-ray CT (HXCT) and machine learning. HXCT is proposed to reconstruct the x-ray absorption spectrum (XAS) characteristics of a single pixel in the reconstructed mouse liver images. HXCT uses a cadmium telluride photon counter as the x-ray detector, which can improve the spectral resolution and separate spectral lines. Filtered back-projection and algebra reconstruction technique reconstruction algorithms are used for image and XAS reconstruction. In the machine learning model, principal component analysis is utilized to reduce the dimensionality of XAS. Besides, the neural network algorithm Artificial Neural Network (ANN) is used to train and identify the reconstructed XAS of two different kinds of livers. These two different mouse livers can be well recognized since the accuracy goes to almost 100% based on ANN. It is feasible to employ the machine learning algorithm to identify the XAS of different mouse livers.

中文翻译：

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基于高光谱X射线计算机断层扫描重建X射线吸收光谱的小鼠肝脏机器学习识别

X射线计算机断层扫描（X-CT）通常用于检查器官，但重建的图像只能用于结构识别。器官是否健康，需要专业的医生检查重建的图像并根据自己的经验进行判断。本文的目的是通过高光谱X射线CT（HXCT）和机器学习来鉴定肝硬化小鼠肝脏和正常小鼠肝脏。提出HXCT来重建重建的小鼠肝脏图像中单个像素的X射线吸收光谱（XAS）特性。HXCT使用碲化镉光子计数器作为X射线检测器，可以提高光谱分辨率并分离光谱线。滤波反投影和代数重建技术重建算法用于图像和XAS重建。在机器学习模型中，主成分分析被用来减少XAS的维数。此外，神经网络算法人工神经网络（ANN）用于训练和识别两种不同类型肝脏的重建XAS。由于基于ANN的准确性几乎达到100％，因此可以很好地识别出这两种不同的小鼠肝脏。采用机器学习算法来识别不同小鼠肝脏的XAS是可行的。