Joint reconstruction of PET-MRI by exploiting structural similarity
Joint reconstruction of PET-MRI by exploiting structural similarity
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DOI：
10.1088/0266-5611/31/1/015001
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发表时间：
2015-01-01
期刊：
影响因子：
2.1
通讯作者：
Arridge, Simon R.
中科院分区：
文献类型：
--
作者：
Ehrhardt, Matthias J.;Thielemans, Kris;Arridge, Simon R.
最近的技术进步使得正电子发射断层扫描（PET）与磁共振成像（MRI）相结合成为可能。这些PET-MRI扫描仪同时采集功能性PET和解剖或功能性MRI数据。由于功能和解剖结构不是彼此独立的，因此要重建的图像可能具有共享的结构。我们的目标是利用这种内在的结构相似性，重建从两种方式在联合重建框架。两种模态之间的结构相似性可以以两种不同的方式建模：边缘更可能处于相似的位置和/或具有相似的方向。我们分析了最小化封装这些不同的模型的先验所产生的扩散过程。结果表明，这类并行水平集先验总是对应于各向异性扩散，即有时向前，有时向后扩散。我们进行数值实验，我们共同重建模糊氡数据与泊松噪声（PET）和欠采样傅立叶数据与高斯噪声（MRI）。我们的研究结果表明，这两种方式受益于彼此的共享边缘信息的领域。与单独的重建相比，联合重建具有更少的伪影和更尖锐的边缘，并且在所有考虑的欠采样情况下可以减少l（2）误差。
Recent advances in technology have enabled the combination of positron emission tomography (PET) with magnetic resonance imaging (MRI). These PET-MRI scanners simultaneously acquire functional PET and anatomical or functional MRI data. As function and anatomy are not independent of one another the images to be reconstructed are likely to have shared structures. We aim to exploit this inherent structural similarity by reconstructing from both modalities in a joint reconstruction framework. The structural similarity between two modalities can be modelled in two different ways: edges are more likely to be at similar positions and/or to have similar orientations. We analyse the diffusion process generated by minimizing priors that encapsulate these different models. It turns out that the class of parallel level set priors always corresponds to anisotropic diffusion which is sometimes forward and sometimes backward diffusion. We perform numerical experiments where we jointly reconstruct from blurred Radon data with Poisson noise (PET) and under-sampled Fourier data with Gaussian noise (MRI). Our results show that both modalities benefit from each other in areas of shared edge information. The joint reconstructions have less artefacts and sharper edges compared to separate reconstructions and the l(2)-error can be reduced in all of the considered cases of under-sampling.