IEEE Trans Med Imaging. 2018 Apr;37(4):977-987. doi: 10.1109/TMI.2018.2790962.
Attenuation correction for positron-emission tomography (PET)/magnetic resonance (MR) hybrid imaging systems and dose planning for MR-based radiation therapy remain challenging due to insufficient high-energy photon attenuation information. We present a novel approach that uses the learned nonlinear local descriptors and feature matching to predict pseudo computed tomography (pCT) images from T1-weighted and T2-weighted magnetic resonance imaging (MRI) data. The nonlinear local descriptors are obtained by projecting the linear descriptors into the nonlinear high-dimensional space using an explicit feature map and low-rank approximation with supervised manifold regularization. The nearest neighbors of each local descriptor in the input MR images are searched in a constrained spatial range of the MR images among the training dataset. Then the pCT patches are estimated through k-nearest neighbor regression. The proposed method for pCT prediction is quantitatively analyzed on a dataset consisting of paired brain MRI and CT images from 13 subjects. Our method generates pCT images with a mean absolute error (MAE) of 75.25 ± 18.05 Hounsfield units, a peak signal-to-noise ratio of 30.87 ± 1.15 dB, a relative MAE of 1.56 ± 0.5% in PET attenuation correction, and a dose relative structure volume difference of 0.055 ± 0.107% in , as compared with true CT. The experimental results also show that our method outperforms four state-of-the-art methods.
正电子发射断层扫描（PET）/磁共振（MR）混合成像系统的衰减校正和基于磁共振的放射治疗剂量规划仍然具有挑战性，这是由于高能光子衰减信息不足所致。我们提出了一种新的方法，该方法使用学习到的非线性局部描述符和特征匹配，从 T1 加权和 T2 加权磁共振成像（MRI）数据中预测伪计算机断层扫描（pCT）图像。通过将线性描述符投影到显式特征映射和具有监督流形正则化的低秩逼近的非线性高维空间中，获得非线性局部描述符。在训练数据集中，在 MR 图像的受限空间范围内搜索输入 MR 图像中每个局部描述符的最近邻。然后通过 k-最近邻回归估计 pCT 补丁。在由 13 名受试者的配对脑 MRI 和 CT 图像组成的数据集上，对用于 pCT 预测的方法进行了定量分析。我们的方法生成的 pCT 图像的平均绝对误差（MAE）为 75.25±18.05 亨氏单位，峰值信噪比为 30.87±1.15 dB，在 PET 衰减校正中的相对 MAE 为 1.56±0.5%，在 中，与真实 CT 相比，剂量相对结构体积差异为 0.055±0.107%。实验结果还表明，我们的方法优于四种最先进的方法。
