Name
Abstract | ||
|---|---|---|
DUAL-energy computed tomography (DECT) differentiates materials by exploiting the varying material linear attenuation coefficients (LACs) for different x-ray energy spectra. Multi-material decomposition (MMD) is a particularly attractive DECT clinical application to distinguish the complicated material components within the human body. One prior material assisted (PMA) image domain MMD method was implemented, but has suffered from inaccurate decomposition, magnified noise, and expensive computation. To suppress the noise, we implemented a statistical MMD (SMMD) algorithm, which applied the statistical weight to account for the noise variance in the DECT images. Its decomposition accuracy heavily relies on the initial value. In this paper, we propose a novel method to overcome these challenges. Based on the piecewise constant property of CT images with energy-dependent LAC, we assume that the pixels with high similarity have the same material composition. We cluster pixel patches into groups using the block-matching technique. The material composition in each group is preselected according to the shortest Euclidean distance in the energy map between the center of mass of the similar patch groups and the LAC of the object with known material composition pre-assigned by the clinician. MMD is performed on the central pixel of each patch using the preselected material composition. In a preliminary study, the proposed method is evaluated using the digital and water phantoms. The proposed method increases the volume fraction by 25.2% and decreases the standard deviation by 66.2% compared with the PMA method and increases the volume fraction by 19.6% compared with the SMMD method. The proposed method achieves an overall improvement of the normalized cross-correlation matrix diagonality by 34.8% and 69.4% compared with the PMA and SMMD methods. The phantom results indicate that the proposed method has the potential to be applied to clinical practice due to its increased decomposition accuracy, and suppressed noise and cross contamination. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TCI.2019.2909192 | IEEE Transactions on Computational Imaging |
Keywords | Field | DocType |
Dual-energy CT,multi-material decomposition,block matching,material triplets | Computer vision,Normalization (statistics),Statistical weight,Imaging phantom,Euclidean distance,Algorithm,Pixel,Artificial intelligence,Attenuation,Standard deviation,Mathematics,Piecewise | Journal |
Volume | Issue | ISSN |
5 | 4 | 2573-0436 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
13 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yi Xue | 1 | 0 | 1.01 |
| Yangkang Jiang | 2 | 0 | 1.35 |
| Chunlin Yang | 3 | 0 | 0.68 |
| Qihui Lyu | 4 | 0 | 1.01 |
| Jing Wang | 5 | 0 | 0.34 |
| Chen Luo | 6 | 0 | 0.34 |
| Luhan Zhang | 7 | 0 | 0.34 |
| Catherine Desrosiers | 8 | 0 | 0.34 |
| Kun Feng | 9 | 0 | 0.34 |
| Xiaonan Sun | 10 | 0 | 0.34 |
| Xiuhua Hu | 11 | 0 | 0.68 |
| Ke Sheng | 12 | 2 | 2.73 |
| Tianye Niu | 13 | 3 | 2.87 |