Tomographic reconstruction using prior with adaptively binary neighborhood

作者：Wu Hao; Dong Yinmei; Yin Xindao^*; Wu Qianzhi; Gu Jianping

来源：Optik, 2013, 124(21): 4841-4846.

DOI：10.1016/j.ijleo.2013.02.029

• Bayesian reconstruction
• Markov Random Fields (MRF)
• ABN prior (prior)
• Computerized tomography (CT)

Bayesian approaches can be used to improve ill-posed image reconstruction by regularizing the inverse solution using spacial or temporal neighborhood information. This paper proposes a prior with adaptively binary neighborhood (ABN prior) for statistical tomographic reconstruction. With binary weight map adapted to local image features, the proposed prior can lead to improved reconstruction by including relevant pixels belonging to similar structures and excluding those not. A two-step algorithm is also put forward for tomographic reconstruction using the proposed prior. Experiments using both simulated and clinical computerized tomography (CT) data are performed to validate the reconstructions with the proposed ABN prior.

• 出版日期2013
• 单位南京医科大学; 中国人民解放军第二军医大学