Joint MR-PET Reconstruction Using a Multi-Channel Image Regularizer.
Joint MR-PET Reconstruction Using a Multi-Channel Image Regularizer.
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DOI：
10.1109/tmi.2016.2564989
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发表时间：
2017-01
影响因子：
10.6
通讯作者：
Sodickson DK
中科院分区：
文献类型：
--
作者：
Knoll F;Holler M;Koesters T;Otazo R;Bredies K;Sodickson DK
虽然目前最先进的MR-PET扫描仪可以同时进行MR和PET测量，但获得的数据集通常仍然是单独重建的。我们提出了一种新的多模态重建框架，使用二阶总广义变分（TGV）作为专用的多通道正则化函数，从两种模态联合重建图像。通过这种方式，在图像重建过程中共享有关底层解剖结构的信息，同时保留独特的差异。使用一系列加速MR采集和不同MR图像对比度的数值模拟和体内实验结果表明，PET图像质量、分辨率和定量精度得到了改善。
While current state of the art MR-PET scanners enable simultaneous MR and PET measurements, the acquired data sets are still usually reconstructed separately. We propose a new multi-modality reconstruction framework using second order Total Generalized Variation (TGV) as a dedicated multi-channel regularization functional that jointly reconstructs images from both modalities. In this way, information about the underlying anatomy is shared during the image reconstruction process while unique differences are preserved. Results from numerical simulations and in-vivo experiments using a range of accelerated MR acquisitions and different MR image contrasts demonstrate improved PET image quality, resolution, and quantitative accuracy.