Joint reconstruction of PET-MRI by parallel level sets
Joint reconstruction of PET-MRI by parallel level sets
复制标题
平行水平集联合重建PET-MRI
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
S. Arridge
中科院分区：
文献类型：
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作者：
Matthias Joachim Ehrhardt;K. Thielemans;L. Pizarro;P. Markiewicz;D. Atkinson;S. Ourselin;B. Hutton;S. Arridge
联合正电子发射断层扫描（PET）和磁共振成像（MRI）扫描仪同时获得功能性PET和解剖或功能性MRI数据。由于两种模式的数据都可能显示相似的结构，我们的目标是通过PET和MRI的关节重建来利用这一点。在贝叶斯公式中，这可以通过添加两个模态的图像不是独立的先验信息编码来实现。结构相似性可以通过图像梯度的对齐或等价的水平集平行来建模。因此，我们可以结合两种模态的目标函数，并惩罚没有并行水平集的图像对。我们的研究结果表明，将严重欠采样的MRI和有噪声的PET数据的重建相结合，可以减少MRI图像中的欠采样伪影，并更好地定义PET图像。
Combined positron emission tomography (PET) and magnetic resonance imaging (MRI) scanners acquire simultaneously functional PET and anatomical or functional MRI data. As the data of both modalities are likely to show similar structures we aim to exploit this by joint reconstruction of PET and MRI. In a Bayesian formulation, this can be achieved by adding prior information encoding that the images of the two modalities are not independent. Structural similarity can be modeled by the alignment of the image gradients or equivalently their level sets being parallel. Therefore we can combine the objective functions of both modalities and penalize image pairs which do not have parallel level sets. Our results show that combining the reconstruction from heavily under-sampled MRI and noisy PET data can lead to less under-sampling artifacts in MRI images and better defined PET images.