Artificial Neural Network Enhanced Bayesian PET Image Reconstruction.
Artificial Neural Network Enhanced Bayesian PET Image Reconstruction.
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DOI：
10.1109/tmi.2018.2803681
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发表时间：
2018-06
影响因子：
10.6
通讯作者：
Tang J
中科院分区：
文献类型：
--
作者：
Yang B;Ying L;Tang J
在PET图像重建中，已经实现了具有各种正则化项的贝叶斯框架来约束放射性示踪剂分布。改变最大后验（MAP）算法的正则化权重指定了从重建图像测量的方差和空间分辨率之间的折衷的下限。本研究的目的是建立一个基于块的图像增强方案，以减少低于边界的不可达区域的大小，从而定量地改善贝叶斯PET成像。我们投建议的增强作为一个回归问题，该模型的高度非线性和空间变化的映射之间的重建图像补丁和增强图像补丁。通过对实例的学习，采用一种具有反向传播功能的多层感知器（MLP）人工神经网络（ANN）模型来解决该回归问题。使用BrainWeb模型，我们模拟了不同受试者（有和无病变）在不同计数水平下的脑PET数据。MLP使用在一定计数水平下针对一个正常受试者用不同正则化参数的MAP算法重建的图像块来训练。为了评估训练的MLP的性能，处理了来自其他模拟的重建图像和两个患者脑PET成像数据集。在每一个测试的情况下，我们证明了MLP增强技术，提高了噪声和偏差的权衡相比，MAP重建使用不同的正则化权重，从而减少了无法实现的区域的大小定义的MAP算法在方差/分辨率平面。
In PET image reconstruction, the Bayesian framework with various regularization terms has been implemented to constrain the radio tracer distribution. Varying the regularizing weight of a maximum a posteriori (MAP) algorithm specifies a lower bound of the tradeoff between variance and spatial resolution measured from the reconstructed images. The purpose of this study is to build a patch-based image enhancement scheme to reduce the size of the unachievable region below the bound and thus to quantitatively improve the Bayesian PET imaging. We cast the proposed enhancement as a regression problem which models a highly nonlinear and spatial-varying mapping between the reconstructed image patches and an enhanced image patch. An artificial neural network (ANN) model named multilayer perceptron (MLP) with backpropagation was used to solve this regression problem through learning from examples. Using the BrainWeb phantoms, we simulated brain PET data at different count levels of different subjects with and without lesions. The MLP was trained using the image patches reconstructed with a MAP algorithm of different regularization parameters for one normal subject at a certain count level. To evaluate the performance of the trained MLP, reconstructed images from other simulations and two patient brain PET imaging datasets were processed. In every testing cases, we demonstrate that the MLP enhancement technique improves the noise and bias tradeoff compared with the MAP reconstruction using different regularizing weights thus decreasing the size of the unachievable region defined by the MAP algorithm in the variance/resolution plane.