Knoll Florian, Holler Martin, Koesters Thomas, Otazo Ricardo, Bredies Kristian, Sodickson Daniel K
Bernard and Irene Schwartz Center for Biomedical Imaging, and the Center for Advanced Imaging Innovation and Research (CAIR), in the Department of Radiology at NYU School of Medicine, New York, NY, United States.
Institute of Mathematics and Scientific Computing, University of Graz, Graz, Austria. The Institute of Mathematics and Scientific Computing is a member of NAWI Graz (www.nawigraz.at) and BioTechMed Graz (www.biotechmed.at).
IEEE Trans Med Imaging. 2017 Jan;36(1):1-16. doi: 10.1109/TMI.2016.2564989.
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.
虽然当前最先进的MR-PET扫描仪能够同时进行MR和PET测量，但采集到的数据集通常仍分别进行重建。我们提出了一种新的多模态重建框架，使用二阶全广义变分（TGV）作为专用的多通道正则化泛函，对两种模态的图像进行联合重建。通过这种方式，在图像重建过程中共享了有关基础解剖结构的信息，同时保留了独特的差异。使用一系列加速MR采集和不同MR图像对比度进行的数值模拟和体内实验结果表明，PET图像质量、分辨率和定量准确性均有所提高。
