高能数据缺失下的双能CT重建算法实验研究

Experimental study of DECT reconstruction algorithm from incomplete higher energy data

随着全球恐怖主义事件数量的逐年递增,公共安全问题越来越受到各国的重视,对安检系统的需求也越来越高。具有高精度物质识别特性的双能CT检测系统一直被认为是防范恐怖事件发生的最有效工具之一。但目前最典型的伪双能CT成像系统是由高低能量不同的两层全采样探测器单元组成。大量探测器单元的使用大大提升了系统成本,阻碍了大规模商用。2013年,我们提出了一种基于压缩感知理论的双能CT不完备数据重建算法,该算法仅利用少数高能探测器缺失数据即可实现图像质量相当的重建过程,模拟实验初步验证了算法的有效性。本文进一步使用在双能CT物品机上采集的真实数据验证算法的有效性。实验结果表明,在确保双能CT重建图像质量的前提下,使用基于压缩感知理论的双能CT不完备数据重建算法可以将双能CT探测器成本降低25%。

Facing an increasing anlotult of terrorist attacks, all countries highly concern on public security issues and demand inspection equipments for dangerous articles. Dual energy CT （DECT） system with high material discrimination capability is regarded as a powerful candidate. However, the typical pseudo DECT system uses two layers of detectors and the high cost becomes a bottle neck for wide commercial deployment. In 2013, we proposed a DECT reconstruction algorithm from incomplete data based on compressed sensing. This algorithm can only use incomplete higher energy detector bins to reconstruct without losing the quality of reconstructions and the availability of the algorithm had been validated by preliminary sinmlations. In this paper, we use real data from DECT system to further validate the algorithm. Experimental results demonstrate that, under the same situation of construction quality, the cost of detector bins can be greatly redueed by 25% by using DECT reconstruction algorithm from incomplete data based on compressed sensing.