PMID- 30577033
OWN - NLM
STAT- MEDLINE
DCOM- 20191218
LR  - 20191218
IS  - 1361-6560 (Electronic)
IS  - 0031-9155 (Linking)
VI  - 64
IP  - 3
DP  - 2019 Jan 31
TI  - Multi-energy computed tomography reconstruction using a nonlocal spectral 
      similarity model.
PG  - 035018
LID - 10.1088/1361-6560/aafa99 [doi]
AB  - Multi-energy computed tomography (MECT) is able to acquire simultaneous 
      multi-energy measurements from one scan. In addition, it allows material 
      differentiation and quantification effectively. However, due to the limited 
      energy bin width, the number of photons detected in an energy-specific channel is 
      smaller than that in traditional CT, which results in image quality degradation. 
      To address this issue, in this work, we develop a statistical iterative 
      reconstruction algorithm to acquire high-quality MECT images and high-accuracy 
      material-specific images. Specifically, this algorithm fully incorporates 
      redundant self-similarities within nonlocal regions in the MECT image at one bin 
      and rich spectral similarities among MECT images at all bins. For simplicity, the 
      presented algorithm is referred to as 'MECT-NSS'. Moreover, an efficient 
      optimization algorithm is developed to solve the MECT-NSS objective function. 
      Then, a comprehensive evaluation of parameter selection for the MECT-NSS 
      algorithm is conducted. In the experiment, the datasets include images from three 
      phantoms and one patient to validate and evaluate the MECT-NSS reconstruction 
      performance. The qualitative and quantitative results demonstrate that the 
      presented MECT-NSS can successfully yield better MECT image quality and more 
      accurate material estimation than the competing algorithms.
FAU - Yao, Lisha
AU  - Yao L
AD  - School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, 
      People's Republic of China. Guangzhou Key Laboratory of Medical Radiation Imaging 
      and Detection Technology, Southern Medical University, Guangzhou 510515, People's 
      Republic of China. These authors contributed equally.
FAU - Zeng, Dong
AU  - Zeng D
FAU - Chen, Gaofeng
AU  - Chen G
FAU - Liao, Yuting
AU  - Liao Y
FAU - Li, Sui
AU  - Li S
FAU - Zhang, Yuanke
AU  - Zhang Y
FAU - Wang, Yongbo
AU  - Wang Y
FAU - Tao, Xi
AU  - Tao X
FAU - Niu, Shanzhou
AU  - Niu S
FAU - Lv, Qingwen
AU  - Lv Q
FAU - Bian, Zhaoying
AU  - Bian Z
FAU - Ma, Jianhua
AU  - Ma J
FAU - Huang, Jing
AU  - Huang J
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20190131
PL  - England
TA  - Phys Med Biol
JT  - Physics in medicine and biology
JID - 0401220
SB  - IM
MH  - Algorithms
MH  - Humans
MH  - Image Processing, Computer-Assisted/*methods
MH  - *Models, Statistical
MH  - Phantoms, Imaging
MH  - Photons
MH  - *Tomography, X-Ray Computed
EDAT- 2018/12/24 06:00
MHDA- 2019/12/19 06:00
CRDT- 2018/12/22 06:00
PHST- 2018/12/24 06:00 [pubmed]
PHST- 2019/12/19 06:00 [medline]
PHST- 2018/12/22 06:00 [entrez]
AID - 10.1088/1361-6560/aafa99 [doi]
PST - epublish
SO  - Phys Med Biol. 2019 Jan 31;64(3):035018. doi: 10.1088/1361-6560/aafa99.