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Photon-counting cine-cardiac CT in the mouse


Autoři: Darin P. Clark aff001;  Matthew Holbrook aff001;  Chang-Lung Lee aff002;  Cristian T. Badea aff001
Působiště autorů: Center for In Vivo Microscopy, Department of Radiology, Duke University, Durham, NC, United States of America aff001;  Department of Radiation Oncology, Duke University, Durham, NC, United States of America aff002
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0218417

Souhrn

The maturation of photon-counting detector (PCD) technology promises to enhance routine CT imaging applications with high-fidelity spectral information. In this paper, we demonstrate the power of this synergy and our complementary reconstruction techniques, performing 4D, cardiac PCD-CT data acquisition and reconstruction in a mouse model of atherosclerosis, including calcified plaque. Specifically, in vivo cardiac micro-CT scans were performed in four ApoE knockout mice, following their development of calcified plaques. The scans were performed with a prototype PCD (DECTRIS, Ltd.) with 4 energy thresholds. Projections were sampled every 10 ms with a 10 ms exposure, allowing the reconstruction of 10 cardiac phases at each of 4 energies (40 total 3D volumes per mouse scan). Reconstruction was performed iteratively using the split Bregman method with constraints on spectral rank and spatio-temporal gradient sparsity. The reconstructed images represent the first in vivo, 4D PCD-CT data in a mouse model of atherosclerosis. Robust regularization during iterative reconstruction yields high-fidelity results: an 8-fold reduction in noise standard deviation for the highest energy threshold (relative to unregularized algebraic reconstruction), while absolute spectral bias measurements remain below 13 Hounsfield units across all energy thresholds and scans. Qualitatively, image domain material decomposition results show clear separation of iodinated contrast and soft tissue from calcified plaque in the in vivo data. Quantitatively, spatial, spectral, and temporal fidelity are verified through a water phantom scan and a realistic MOBY phantom simulation experiment: spatial resolution is robustly preserved by iterative reconstruction (10% MTF: 2.8–3.0 lp/mm), left-ventricle, cardiac functional metrics can be measured from iodine map segmentations with ~1% error, and small calcifications (615 μm) can be detected during slow moving phases of the cardiac cycle. Given these preliminary results, we believe that PCD technology will enhance dynamic CT imaging applications with high-fidelity spectral and material information.

Klíčová slova:

Research and analysis methods – Imaging techniques – In vivo imaging – Animal studies – Experimental organism systems – Model organisms – Mouse models – Animal models – Biology and life sciences – Neuroscience – Neuroimaging – Physiology – Physiological processes – Calcification – Anatomy – Cardiac ventricles – Medicine and health sciences – Diagnostic medicine – Diagnostic radiology – Tomography – Computed axial tomography – Radiology and imaging – Cardiology – Heart rate – Cardiovascular anatomy – Heart – Physical sciences – Chemistry – Chemical elements – Iodine – Computer and information sciences – Data acquisition


Zdroje

1. Johnson TR, Krauss B, Sedlmair M, Grasruck M, Bruder H, Morhard D, et al. Material differentiation by dual energy CT: initial experience. European radiology. 2007;17(6):1510–7. doi: 10.1007/s00330-006-0517-6 17151859

2. Graser A, Johnson TR, Hecht EM, Becker CR, Leidecker C, Staehler M, et al. Dual-energy CT in patients suspected of having renal masses: can virtual nonenhanced images replace true nonenhanced images? Radiology. 2009;252(2):433–40. doi: 10.1148/radiol.2522080557 19487466

3. Chae EJ, Song J-W, Seo JB, Krauss B, Jang YM, Song K-S. Clinical utility of dual-energy CT in the evaluation of solitary pulmonary nodules: initial experience. Radiology. 2008;249(2):671–81. doi: 10.1148/radiol.2492071956 18796658

4. Yu L, Leng S, McCollough CH. Dual-energy CT–based monochromatic imaging. American journal of Roentgenology. 2012;199(5_supplement):S9–S15.

5. Johnson T, Fink C, Schönberg SO, Reiser MF. Dual energy CT in clinical practice: Springer Science & Business Media; 2011.

6. Clark DP, Ghaghada K, Moding EJ, Kirsch DG, Badea CT. In vivo characterization of tumor vasculature using iodine and gold nanoparticles and dual energy micro-CT. Physics in Medicine and Biology. 2013;58(6):1683. doi: 10.1088/0031-9155/58/6/1683 23422321

7. Ashton JR, Clark DP, Moding EJ, Ghaghada K, Kirsch DG, West JL, et al. Dual-energy micro-CT functional imaging of primary lung cancer in mice using gold and iodine nanoparticle contrast agents: a validation study. PLOS ONE. 2014;9(2):e88129. doi: 10.1371/journal.pone.0088129 24520351

8. Mukundan S Jr, Ghaghada KB, Badea CT, Kao C-Y, Hedlund LW, Provenzale JM, et al. A liposomal nanoscale contrast agent for preclinical CT in mice. American Journal of Roentgenology. 2006;186(2):300–7. doi: 10.2214/AJR.05.0523 16423931

9. Clark DP, Badea CT. Hybrid spectral CT reconstruction. PLOS ONE. 2017;12(7):e0180324. doi: 10.1371/journal.pone.0180324 28683124

10. Cruje C, Holdsworth DW, Gillies ER, Drangova M. High-concentration gadolinium nanoparticles for pre-clinical vascular imaging. Medical Imaging 2018: Physics of Medical Imaging: International Society for Optics and Photonics; 2018. p. 105732N.

11. Shikhaliev PM. Energy-resolved computed tomography: first experimental results. Physics in Medicine & Biology. 2008;53(20):5595.

12. Taguchi K, Iwanczyk JS. Vision 20/20: Single photon counting x-ray detectors in medical imaging. Medical Physics. 2013;40(10):100901. doi: 10.1118/1.4820371 24089889

13. Leng S, Rajendran K, Gong H, Zhou W, Halaweish AF, Henning A, et al. 150-μm Spatial Resolution Using Photon-Counting Detector Computed Tomography Technology: Technical Performance and First Patient Images. Investigative radiology. 2018;53(11):655–62. doi: 10.1097/RLI.0000000000000488 29847412

14. Gutjahr R, Halaweish AF, Yu Z, Leng S, Yu L, Li Z, et al. Human Imaging With Photon Counting–Based Computed Tomography at Clinical Dose Levels: Contrast-to-Noise Ratio and Cadaver Studies. Investigative Radiology. 2016;51(7):421–9. doi: 10.1097/RLI.0000000000000251 26818529

15. Rößler A-C, Kalender W, Kolditz D, Steiding C, Ruth V, Preuss C, et al. Performance of photon-counting breast computed tomography, digital mammography, and digital breast tomosynthesis in evaluating breast specimens. Academic radiology. 2017;24(2):184–90. doi: 10.1016/j.acra.2016.09.017 27888024

16. Leng S, Yu Z, Halaweish A, Kappler S, Hahn K, Henning A, et al., editors. A high-resolution imaging technique using a whole-body, research photon counting detector CT system. Medical Imaging 2016: Physics of Medical Imaging; 2016: International Society for Optics and Photonics.

17. Zhou W, Montoya J, Gutjahr R, Ferrero A, Halaweish A, Kappler S, et al. Lung nodule volume quantification and shape differentiation with an ultra-high resolution technique on a photon-counting detector computed tomography system. Journal of Medical Imaging. 2017;4(4):043502. doi: 10.1117/1.JMI.4.4.043502 29181429

18. Willemink MJ, Persson M, Pourmorteza A, Pelc NJ, Fleischmann D. Photon-counting CT: technical principles and clinical prospects. Radiology. 2018;289(2):293–312. doi: 10.1148/radiol.2018172656 30179101

19. Symons R, Krauss B, Sahbaee P, Cork TE, Lakshmanan MN, Bluemke DA, et al. Photon‐counting CT for simultaneous imaging of multiple contrast agents in the abdomen: an in vivo study. Medical physics. 2017;44(10):5120–7. doi: 10.1002/mp.12301 28444761

20. Cormode DP, Si-Mohamed S, Bar-Ness D, Sigovan M, Naha PC, Balegamire J, et al. Multicolor spectral photon-counting computed tomography: in vivo dual contrast imaging with a high count rate scanner. Scientific reports. 2017;7(1):4784. doi: 10.1038/s41598-017-04659-9 28684756

21. Gao H, Yu H, Osher S, Wang G. Multi-energy CT based on a prior rank, intensity and sparsity model (PRISM). Inverse Problems. 2011;27(11):115012. doi: 10.1088/0266-5611/27/11/115012 22223929

22. Candès EJ, Li X, Ma Y, Wright J. Robust principal component analysis? Journal of the ACM (JACM). 2011;58(3):11.

23. Clark DP, Lee C-L, Kirsch DG, Badea CT. Spectrotemporal CT data acquisition and reconstruction at low dose. Medical Physics. 2015;42(11):6317–36. doi: 10.1118/1.4931407 26520724

24. Holbrook M, Clark D, Badea C. Low-dose 4D cardiac imaging in small animals using dual source micro-CT. Physics in Medicine & Biology. 2018;63(2):025009.

25. Meganck JA, Liu B. Dosimetry in micro-computed tomography: a review of the measurement methods, impacts, and characterization of the Quantum GX imaging system. Molecular Imaging and Biology. 2017;19(4):499–511. doi: 10.1007/s11307-016-1026-x 27957647

26. Badea CT, Clark DP, Holbrook M, Srivastava M, Mowery Y, Ghaghada KB. Functional imaging of tumor vasculature using iodine and gadolinium-based nanoparticle contrast agents: a comparison of spectral micro-CT using energy integrating and photon counting detectors. Physics in medicine and biology. 2019.

27. Piedrahita JA, Zhang SH, Hagaman JR, Oliver PM, Maeda N. Generation of mice carrying a mutant apolipoprotein E gene inactivated by gene targeting in embryonic stem cells. Proceedings of the National Academy of Sciences. 1992;89(10):4471–5.

28. Zhang SH, Reddick RL, Piedrahita JA, Maeda N. Spontaneous hypercholesterolemia and arterial lesions in mice lacking apolipoprotein E. Science. 1992;258(5081):468–71. doi: 10.1126/science.1411543 1411543

29. Lee C-L, Min H, Befera N, Clark D, Qi Y, Das S, et al. Assessing cardiac injury in mice with dual energy-microCT, 4D-microCT, and microSPECT imaging after partial heart irradiation. International Journal of Radiation Oncology* Biology* Physics. 2014;88(3):686–93.

30. Clark D, Badea C. Data-efficient methods for multi-channel x-ray CT reconstruction. Medical Imaging 2018: Physics of Medical Imaging: International Society for Optics and Photonics; 2018. p. 105732A.

31. Clark DP, Badea CT. Joint regularization for spectro-temporal CT reconstruction. Proceedings of SPIE Medical Imaging2016. p. 1–11.

32. Clark DP, Badea CT, editors. GPU-Based Tools for Multi-Channel X-ray CT Reconstruction. The Fifth International Conference on Image Formation in X-Ray Computed Tomography; 2018; Salt Lake City, Utah.

33. Alvarez RE, Macovski A. Energy-selective reconstructions in x-ray computerised tomography. Physics in Medicine and Biology. 1976;21(5):733. doi: 10.1088/0031-9155/21/5/002 967922

34. Segars W, Tsui B. 4D MOBY and NCAT phantoms for medical imaging simulation of mice and men. Journal of Nuclear Medicine. 2007;48(supplement 2):203P-P.

35. Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC, et al. User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage. 2006;31(3):1116–28. doi: 10.1016/j.neuroimage.2006.01.015 16545965

36. Siewerdsen JH, Wojciech Z, Xu J. Chapter 4: Cone-beam CT image quality. In: Shaw CC, editor. Cone beam computed tomography: Taylor & Francis; 2014. p. 37–58.

37. Tao S, Rajendran K, McCollough CH, Leng S. Material decomposition with prior knowledge aware iterative denoising (MD-PKAID). Physics in Medicine & Biology. 2018;63(19):195003.

38. Rigie DS, La Rivière PJ. Joint reconstruction of multi-channel, spectral CT data via constrained total nuclear variation minimization. Physics in Medicine and Biology. 2015;60(5):1741. doi: 10.1088/0031-9155/60/5/1741 25658985

39. Brehm M, Sawall S, Maier J, Sauppe S, Kachelrieß M. Cardiorespiratory motion‐compensated micro‐CT image reconstruction using an artifact model‐based motion estimation. Medical physics. 2015;42(4):1948–58. doi: 10.1118/1.4916083 25832085

40. Chen GH, Li Y. Synchronized multiartifact reduction with tomographic reconstruction (SMART‐RECON): A statistical model based iterative image reconstruction method to eliminate limited‐view artifacts and to mitigate the temporal‐average artifacts in time‐resolved CT. Medical physics. 2015;42(8):4698–707. doi: 10.1118/1.4926430 26233197

41. Clark D, Badea C. Convolutional regularization methods for 4D, x-ray CT reconstruction. Medical Imaging 2019: Physics of Medical Imaging: International Society for Optics and Photonics; 2019. p. 109482A.

42. Taguchi K, Stierstorfer K, Polster C, Lee O, Kappler S. Spatio‐energetic cross‐talk in photon counting detectors: Numerical detector model (Pc TK) and workflow for CT image quality assessment. Medical physics. 2018;45(5):1985–98. doi: 10.1002/mp.12863 29537627

43. Yu Z, Leng S, Li Z, Halaweish AF, Kappler S, Ritman EL, et al. How low can we go in radiation dose for the data-completion scan on a research whole-body photon-counting CT system. Journal of computer assisted tomography. 2016;40(4):663. doi: 10.1097/RCT.0000000000000412 27096399

44. Ballabriga R, Campbell M, Heijne E, Llopart X, Tlustos L, editors. The Medipix3 prototype, a pixel readout chip working in single photon counting mode with improved spectrometric performance. 2006 IEEE Nuclear Science Symposium Conference Record; 2006: IEEE.


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