August  2020, 14(4): 607-629. doi: 10.3934/ipi.2020028

Joint reconstruction in low dose multi-energy CT

1. 

Department of Applied Physics, University of Eastern Finland, POB 1627, FI-70211 Kuopio, Finland

2. 

Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland

* Corresponding author: Alexander.Meaney@helsinki.fi

Received  June 2019 Revised  December 2019 Published  May 2020

Multi-energy CT takes advantage of the non-linearly varying attenuation properties of elemental media with respect to energy, enabling more precise material identification than single-energy CT. The increased precision comes with the cost of a higher radiation dose. A straightforward way to lower the dose is to reduce the number of projections per energy, but this makes tomographic reconstruction more ill-posed. In this paper, we propose how this problem can be overcome with a combination of a regularization method that promotes structural similarity between images at different energies and a suitably selected low-dose data acquisition protocol using non-overlapping projections. The performance of various joint regularization models is assessed with both simulated and experimental data, using the novel low-dose data acquisition protocol. Three of the models are well-established, namely the joint total variation, the linear parallel level sets and the spectral smoothness promoting regularization models. Furthermore, one new joint regularization model is introduced for multi-energy CT: a regularization based on the structure function from the structural similarity index. The findings show that joint regularization outperforms individual channel-by-channel reconstruction. Furthermore, the proposed combination of joint reconstruction and non-overlapping projection geometry enables significant reduction of radiation dose.

Citation: Jussi Toivanen, Alexander Meaney, Samuli Siltanen, Ville Kolehmainen. Joint reconstruction in low dose multi-energy CT. Inverse Problems and Imaging, 2020, 14 (4) : 607-629. doi: 10.3934/ipi.2020028
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show all references

References:
[1]

R. E. Alvarez and A. Macovski, Energy-selective reconstructions in x-ray computerised tomography, Physics in Medicine & Biology, 21 (1976), 733-744.  doi: 10.1088/0031-9155/21/5/002.

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[3]

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[4]

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[12]

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[13]

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[14]

M. J. Ehrhardt, K. Thielemans, L. Pizarro, D. Atkinson, S. Ourselin, B. F. Hutton and S. R. Arridge, Joint reconstruction of PET-MRI by exploiting structural similarity, Inverse Problems, 31 (2014), 015001, 23 pp. doi: 10.1088/0266-5611/31/1/015001.

[15]

M. J. Ehrhardt and S. R. Arridge, Vector-valued image processing by parallel level sets, IEEE Trans. Image Process., 23 (2014), 9-18.  doi: 10.1109/TIP.2013.2277775.

[16]

I. A. Elbakri and J. A. Fessler, Segmentation-free statistical image reconstruction for polyenergetic x-ray computed tomography, in Proceedings IEEE International Symposium on Biomedical Imaging, IEEE, 2002,828–831. doi: 10.1109/ISBI.2002.1029387.

[17]

R. Forghani and S. K. Mukherji, Advanced dual-energy CT applications for the evaluation of the soft tissues of the neck, Clinical Radiology, 73 (2018), 70-80.  doi: 10.1016/j.crad.2017.04.002.

[18]

J. FornaroS. LeschkaD. HibbelnA. ButlerN. Anderson and G. Pache, Dual- and multi-energy CT: Approach to functional imaging, Insights into Imaging, 2 (2011), 149-159.  doi: 10.1007/s13244-010-0057-0.

[19]

H. Gao, H. Yu, S. Osher and G. Wang, Multi-energy CT based on a prior rank, intensity and sparsity model (PRISM), Inverse Problems, 27 (2011), 115012, 22 pp. doi: 10.1088/0266-5611/27/11/115012.

[20]

D. T. Ginat and R. Gupta, Advances in computed tomography imaging technology, Annual Review of Biomedical Engineering, 16 (2014), 431-453.  doi: 10.1146/annurev-bioeng-121813-113601.

[21]

H. W. Goo and J. M. Goo, Dual-energy CT: New horizon in medical imaging, Korean Journal of Radiology, 18 (2017), 555-569.  doi: 10.3348/kjr.2017.18.4.555.

[22]

D. GürsoyT. BiçerA. LanzirottiM. G. Newville and F. De Carlo, Hyperspectral image reconstruction for x-ray fluorescence tomography, Optics Express, 23 (2015), 9014-9023. 

[23]

K. HämäläinenL. HarhanenA. HauptmannA. KallonenE. Niemi and S. Siltanen, Total variation regularization for large-scale x-ray tomography, International Journal of Tomography & Simulation, 25 (2014), 1-25. 

[24]

K. Hämäläinen, A. Kallonen, V. Kolehmainen, M. Lassas, K. Niinimäki and S. Siltanen, Sparse tomography, SIAM J. Sci. Comput., 35 (2013), B644–B665. doi: 10.1137/120876277.

[25]

W. R. Hendee and M. K. O'Connor, Radiation risks of medical imaging: Separating fact from fantasy, Radiology, 264 (2012), 312-321.  doi: 10.1148/radiol.12112678.

[26]

ICRP, ICRPpublication 103: The 2007 recommendations of the international commission on radiological protection, Ann. ICRP, 37 (2007), 2-4. 

[27]

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[28]

W. A. Kalender, Dose in x-ray computed tomography, Phys. Med. Biol., 59 (2014), R129–R150. doi: 10.1088/0031-9155/59/3/R129.

[29]

K. KaliszS. HalliburtonS. AbbaraJ. A. LeipsicM. H. AlbrechtU. J. Schoepf and P. Rajiah, Update on cardiovascular applications of multienergy CT, RadioGraphics, 37 (2017), 1955-1974.  doi: 10.1148/rg.2017170100.

[30]

D. Kazantsev, J. S. Jørgensen, M. S. Andersen, W. R. B. Lionheart, P. D. Lee and P. J. Withers, Joint image reconstruction method with correlative multi-channel prior for x-ray spectral computed tomography, Inverse Problems, 34 (2018), 064001, 26 pp. doi: 10.1088/1361-6420/aaba86.

[31]

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[32]

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Figure 1.  Illustration of a spectrally non-overlapping projection sampling scheme for low dose multi-energy CT imaging. The X-ray source energies $ E_k, k = 1,2,3 $, are denoted by tube voltages $ \mathrm{kV}_1,\mathrm{kV}_2,\mathrm{kV}_3 $
Figure 2.  Left: The X-ray attenuation coefficients of water, soft tissue, and cortical bone as functions of X-ray energy. The raw data was obtained from NIST [43]. Right: Example of a realistic tungsten X-ray source spectrum with 120 kVp voltage and filtering (2.5 mm Al + 0.2 mm Cu), and monochromatic radiation with the same effective energy. The spectra were computed using the SpekCalc software [48]
Figure 3.  Reconstructed images for the simulation test case using 30 projection directions at each energy
Figure 4.  The RMS errors (left) and the mean SSIM indices (right) for the different reconstruction approaches using 30 projection directions at each energy
Figure 5.  Comparison of (TV) and (S+TV) reconstructions. Results labeled METHOD(90) are based on overall of $ 3\times 90 = 270 $ projection images using the same 90 directions for each energy. Results labeled METHOD(30W) are based on overall of 90 projections using the non-overlapping 30+30+30 directions (the same geometry that is used in Figure 3). Each image shows a region of interest of the full reconstruction
Figure 6.  Error metrics for the (TV) and (S+TV) reconstructions shown in Figure 5. Results labeled METHOD(90) are based on overall of $ 3\times 90 = 270 $ projection images using the same 90 directions for each energy. Results labeled METHOD(30W) are based on overall of 90 projections using the non-overlapping 30+30+30 directions
Figure 7.  Reconstructed images for the experimental data test case
Table 1.  Imaging geometry used for collecting the experimental data
Parameter Value
Focus-center distance 252 mm
Focus-detector distance 420 mm
Magnification 5/2
Detector pixel size 0.200 mm
Effective pixel size 0.120 mm
Projection size 552 $ \times $ 576 pixels
Angular range 360°
#projections 720
Parameter Value
Focus-center distance 252 mm
Focus-detector distance 420 mm
Magnification 5/2
Detector pixel size 0.200 mm
Effective pixel size 0.120 mm
Projection size 552 $ \times $ 576 pixels
Angular range 360°
#projections 720
Table 2.  Energy-specific settings used for collecting the experimental data
Energy $ U $ (kV) Filtration $ I $ (μA) Exposure time (ms) Frame averaging
$ E_1 $ 50 None 300 125 4
$ E_2 $ 80 1 mm Al 180 125 4
$ E_3 $ 120 0.5 mm Cu 120 250 4
Energy $ U $ (kV) Filtration $ I $ (μA) Exposure time (ms) Frame averaging
$ E_1 $ 50 None 300 125 4
$ E_2 $ 80 1 mm Al 180 125 4
$ E_3 $ 120 0.5 mm Cu 120 250 4
Table 3.  Selected $ \alpha $ and $ \gamma_k $ parameters for the different regularization terms for the simulated data test case ($ \alpha_{ \rm{SIM}}, \gamma_{k, \rm{SIM}} $) and for the experimental data test case ($ \alpha_{ \rm{EXP}}, \gamma_{k, \rm{EXP}} $). The three values of $ \gamma_k $ are for the three different tube energies with indices $ k = 1,2,3 $
Prior Acronym $ \alpha_{ \rm{SIM}} $ $ \gamma_{k, \rm{SIM}} $ $ \alpha_{ \rm{EXP}} $ $ \gamma_{k, \rm{EXP}} $
No prior No prior - - - -
Total variation TV - 2.25, 0.8, 0.7 - 1.0, 0.5, 0.25
Joint total variation JTV 2.25 0 1 0
Linear parallel level sets LPLS 4000 0 6000 0
First difference D1 400 0 100 0
Structural S 4e6 0 1e6 0
First difference + TV D1+TV 50 1.125, 0.4, 0.35 70 0.5, 0.25, 0.125
Structural + TV S+TV 0.75e5 1.125, 0.4, 0.35 0.5e6 0.5, 0.25, 0.125
Prior Acronym $ \alpha_{ \rm{SIM}} $ $ \gamma_{k, \rm{SIM}} $ $ \alpha_{ \rm{EXP}} $ $ \gamma_{k, \rm{EXP}} $
No prior No prior - - - -
Total variation TV - 2.25, 0.8, 0.7 - 1.0, 0.5, 0.25
Joint total variation JTV 2.25 0 1 0
Linear parallel level sets LPLS 4000 0 6000 0
First difference D1 400 0 100 0
Structural S 4e6 0 1e6 0
First difference + TV D1+TV 50 1.125, 0.4, 0.35 70 0.5, 0.25, 0.125
Structural + TV S+TV 0.75e5 1.125, 0.4, 0.35 0.5e6 0.5, 0.25, 0.125
Table 4.  Measurement angles used for energies $ E_1 $, $ E_2 $ and $ E_3 $ in the simulated data case (left) and experimental data case (right)
$ E_1 $ $ E_2 $ $ E_3 $ $ E_1 $ $ E_2 $ $ E_3 $
0 2 4 0 4 8
6 8 10 12 16 20
12 14 16 24 28 32
$ \vdots $ $ \vdots $ $ \vdots $ $ \vdots $ $ \vdots $ $ \vdots $
162 164 166 324 328 332
168 170 172 336 340 344
174 176 178 348 352 356
$ E_1 $ $ E_2 $ $ E_3 $ $ E_1 $ $ E_2 $ $ E_3 $
0 2 4 0 4 8
6 8 10 12 16 20
12 14 16 24 28 32
$ \vdots $ $ \vdots $ $ \vdots $ $ \vdots $ $ \vdots $ $ \vdots $
162 164 166 324 328 332
168 170 172 336 340 344
174 176 178 348 352 356
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