McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Canada.
Department of Biomedical Engineering, McGill University, Montreal, Canada.
Med Phys. 2019 May;46(5):2085-2102. doi: 10.1002/mp.13418. Epub 2019 Mar 12.
Post-reconstruction positron emission tomography (PET) image restoration methods that take advantage of available anatomical information can play an important role in accurate quantification of PET images. However, when using anatomical information, the resulting PET image may lose resolution in certain regions where the anatomy does not agree with the change in functional activity. In this work, this problem is addressed by using both magnetic resonance (MR) and filtered PET images to guide the denoising process.
In this work, two novel post-reconstruction methods for restoring PET images using the subject's registered T1-weighted MR image are proposed. The first method is based on a representation of the image using basis functions extracted from T1-weighted MR and filtered PET image. The coefficients for these basis functions are estimated using a sparsity-penalized least squares objective function. The second method is a noniterative fast method that uses guided kernel filtering in combination with twicing to restore the noisy PET image. When applied after conventional PVE correction, these methods can be considered as voxel-based MR-guided partial volume effect (PVE) correction methods.
Using simulation analyses of [ F]FDG PET images of the brain with lesions, the proposed methods are compared to other denoising methods through different figures of merit. The results show promising improvements in image quality as well as reduction in bias and variance of the lesions. We also show the application of the proposed methods on real [ F]FDG data.
Two methods for restoring PET images were proposed. The methods were evaluated on simulation and real brain images. Most MR-guided PVE correction methods are only based on segmented T1-weighted images and their accuracy is very sensitive to segmentation errors, especially in regions of abnormalities and lesions. However, both proposed methods can use the T1-weighted image without segmentation. The simplicity and the very low computational cost of the second method make it suitable for clinical applications and large data studies. The proposed methods can be naturally extended to PVE correction and denoising of other functional modalities using corresponding anatomical information.
利用现有解剖信息的重建后正电子发射断层扫描（PET）图像恢复方法在 PET 图像的精确量化中可以发挥重要作用。然而，在使用解剖信息时，由于解剖结构与功能活动的变化不一致，所得 PET 图像可能会在某些区域丢失分辨率。在这项工作中，通过使用磁共振（MR）和滤波 PET 图像来指导去噪过程来解决这个问题。
在这项工作中，提出了两种利用受试者注册的 T1 加权 MR 图像重建 PET 图像的新的重建后方法。第一种方法基于从 T1 加权 MR 和滤波 PET 图像中提取的基函数表示图像。这些基函数的系数使用具有稀疏惩罚的最小二乘目标函数来估计。第二种方法是一种非迭代快速方法，它结合两次使用引导核滤波来恢复噪声 PET 图像。当应用于常规 PVE 校正后，这些方法可以被认为是基于体素的 MR 引导部分容积效应（PVE）校正方法。
通过使用具有病变的脑[F]FDG PET 图像的模拟分析，通过不同的优劣指标将提出的方法与其他去噪方法进行比较。结果表明，在图像质量的改善以及病变的偏差和方差的降低方面都有了有希望的改进。我们还展示了在真实[F]FDG 数据上应用所提出方法的情况。
提出了两种用于恢复 PET 图像的方法。该方法在模拟和真实脑图像上进行了评估。大多数基于 MR 的 PVE 校正方法仅基于分割的 T1 加权图像，其准确性对分割误差非常敏感，尤其是在异常和病变区域。然而，所提出的两种方法都可以使用未经分割的 T1 加权图像。第二种方法的简单性和非常低的计算成本使其适用于临床应用和大型数据研究。所提出的方法可以自然地扩展到使用相应解剖信息的其他功能模式的 PVE 校正和去噪。
