MRI-aided kernel PET image reconstruction method based on texture features.
Dongfang GaoXu ZhangChao ZhouWei FanTianyi ZengQian YangJianmin YuanQiang HeDong LiangXin LiuYongfeng YangHairong ZhengZhanli HuPublished in: Physics in medicine and biology (2021)
We investigate the reconstruction of low-count positron emission tomography (PET) projection, which is an important, but challenging, task. Using the texture feature extraction method of radiomics, i.e. the gray-level co-occurrence matrix (GLCM), texture features can be extracted from magnetic resonance imaging images with high-spatial resolution. In this work, we propose a kernel reconstruction method combining autocorrelation texture features derived from the GLCM. The new kernel function includes the correlations of both the intensity and texture features from the prior image. By regarding the GLCM as a discrete approximation of a probability density function, the asymptotically gray-level-invariant autocorrelation texture feature is generated, which can maintain the accuracy of texture features extracted from small image regions by reducing the number of quantized image gray levels. A computer simulation shows that the proposed method can effectively reduce the noise in the reconstructed image compared to the maximum likelihood expectation maximum method and improve the image quality and tumor region accuracy compared to the original kernel method for low-count PET reconstruction. A simulation study on clinical patient images also shows that the proposed method can improve the whole image quality and that the reconstruction of a high-uptake lesion is more accurate than that achieved by the original kernel method.
Keyphrases
- deep learning
- computed tomography
- contrast enhanced
- positron emission tomography
- magnetic resonance imaging
- image quality
- pet ct
- machine learning
- magnetic resonance
- case report
- optical coherence tomography
- high resolution
- convolutional neural network
- mass spectrometry
- lymph node metastasis
- diffusion weighted imaging