High-resolution model-based material decomposition in dual-layer flat-panel CBCT.
dual-layer detector
material decomposition
model-based iterative reconstruction
spectral CT
Journal
Medical physics
ISSN: 2473-4209
Titre abrégé: Med Phys
Pays: United States
ID NLM: 0425746
Informations de publication
Date de publication:
Oct 2021
Oct 2021
Historique:
revised:
29
03
2021
received:
05
10
2020
accepted:
31
03
2021
pubmed:
18
7
2021
medline:
6
11
2021
entrez:
17
7
2021
Statut:
ppublish
Résumé
Spectral CT uses energy-dependent measurements that enable material discrimination in addition to reconstruction of structural information. Flat-panel detectors (FPDs) have been widely used in dedicated and interventional systems to deliver high spatial resolution, volumetric cone-beam CT (CBCT) in compact and OR-friendly designs. In this work, we derive a model-based method that facilitates high-resolution material decomposition in a spectral CBCT system equipped with a prototype dual-layer FPD. Through high-fidelity modeling of multilayer detector, we seek to avoid resolution loss that is present in more traditional processing and decomposition approaches. A physical model for spectral measurements in dual-layer flat-panel CBCT is developed including layer-dependent differences in system geometry, spectral sensitivities, and detector blur (e.g., due to varied scintillator thicknesses). This forward model is integrated into a model-based material decomposition (MBMD) method based on minimization of a penalized weighted least-squared (PWLS) objective function. The noise and resolution performance of this approach was compared with traditional projection-domain decomposition (PDD) and image-domain decomposition (IDD) approaches as well as one-step MBMD with lower-fidelity models that use approximated geometry, projection interpolation, or an idealized system geometry without system blur model. Physical studies using high-resolution three-dimensional (3D)-printed water-iodine phantoms were conducted to demonstrate the high-resolution imaging performance of the compared decomposition methods in iodine basis images and synthetic monoenergetic images. Physical experiments demonstrate that the MBMD methods incorporating an accurate geometry model can yield higher spatial resolution iodine basis images and synthetic monoenergetic images than PDD and IDD results at the same noise level. MBMD with blur modeling can further improve the spatial-resolution compared with the decomposition results obtained with IDD, PDD, and MBMD methods with lower-fidelity models. Using the MBMD without or with blur model can increase the absolute modulation at 1.75 lp/mm by 10% and 22% compared with IDD at the same noise level. The proposed model-based material decomposition method for a dual-layer flat-panel CBCT system has demonstrated an ability to extend high-resolution performance through sophisticated detector modeling including the layer-dependent blur. The proposed work has the potential to not only facilitate high-resolution spectral CT in interventional and dedicated CBCT systems, but may also provide the opportunity to evaluate different flat-panel design trade-offs including multilayer FPDs with mismatched geometries, scintillator thicknesses, and spectral sensitivities.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
6375-6387Informations de copyright
© 2021 American Association of Physicists in Medicine.
Références
Riederer SJ, Mistretta CA. Selective iodine imaging using K -edge energies in computerized x-ray tomography. Medical Phys. 1977;4:474-481. http://dx.doi.org/10.1118/1.594357
Flohr TG, McCollough CH, Bruder H, et al. First performance evaluation of a dual-source CT (DSCT) system. Eur Radiol. 2006;16:256-268.
Euler A, Parakh A, Falkowski AL, et al. Initial results of a single-source dual-energy computed tomography technique using a split-filter: Assessment of image quality, radiation dose, and accuracy of dual-energy applications in an in vitro and in vivo study. Invest Radiol. 2016;51:491-498.
Toepker M, Kuehas F, Kienzl D, et al. Dual energy computerized tomography with a split bolus - A 1-stop shop for patients with suspected urinary stones? J Urol. 2014;191:792-797.
Primak AN, Fletcher JG, Vrtiska TJ, et al. Noninvasive differentiation of uric acid versus non-uric acid kidney stones using dual-energy CT. Academic Radiology. 2007;14:1441-1447.
Stolzmann P, Kozomara M, Chuck N, et al. In vivo identification of uric acid stones with dual-energy CT: Diagnostic performance evaluation in patients. Abdom Imaging. 2010;35:629-635.
Graser A, Johnson TRC, Hecht EM, et al. Dual-energy CT in patients suspected of having renal masses: Can virtual nonenhanced images replace true nonenhanced images? Radiology. 2009;252:433-440. http://dx.doi.org/10.1148/radiol.2522080557
Zhang L-J, Peng J, Wu S-Y, et al. Liver virtual non-enhanced CT with dual-source, dual-energy CT: A preliminary study. Eur Radiol. 2010;20:2257-2264.
Yu L, Leng S, McCollough CH. Dual-energy CT-based monochromatic imaging. Am J Roentgenol. 2012;199:S9-S15. http://dx.doi.org/10.2214/ajr.12.9121
Symons R, Krauss B, Sahbaee P, et al. Photon-counting CT for simultaneous imaging of multiple contrast agents in the abdomen: Anin vivostudy. Med Phys. 2017;44:5120-5127. http://dx.doi.org/10.1002/mp.12301
Pelgrim GJ, van Hamersvelt RW, Willemink MJ, et al. Accuracy of iodine quantification using dual energy CT in latest generation dual source and dual layer CT. Eur Radiol. 2017;27:3904-3912.
Hua C-H, Shapira N, Merchant TE, Klahr P, Yagil Y. Accuracy of electron density, effective atomic number, and iodine concentration determination with a dual layer dual-energy computed tomography system. Med Phys. 2018;45:2486-2497.
Gang GJ, Zbijewski W, Mahesh M, et al. Image quality and dose for a multisource cone-beam CT extremity scanner. Med Phys. 2018;45:144-155.
Shen L, Xing Y. Multienergy CT acquisition and reconstruction with a stepped tube potential scan. Medical Physics. 2014;42 (1):282-296. http://dx.doi.org/10.1118/1.4903756
Rutt B, Fenster A. Split-filter computed tomography. J Comput Assist Tomogr. 1980;4:501-509. http://dx.doi.org/10.1097/00004728-198008000-00019
Petrongolo M, Zhu L. Single-scan dual-energy CT using primary modulation. IEEE Trans Med Imaging. 2018;37:1799-1808.
Stayman JW, Tilly II S. Model-based multi-material decomposition using spatial spectral CT filters in Proceedings of the international conference on image formation in X-ray computed tomography, 2018:102-105.
Tivnan M, Tilley S II, Stayman JW, et al. Physical modeling and performance of spatial-spectral filters for CT material decomposition. In: Schmidt TG, Chen G-H, Bosmans H, eds. Proc SPIE Medical Imaging 2019: Physics of Medical Imaging. Bethesda: International Society for Optics and Photonics SPIE; 2019:10948.
Roessl E, Brendel B, Engel KJ, Schlomka JP, Thran A, Proksa R. Sensitivity of photon-counting based K-edge imaging in X-ray computed tomography. IEEE Trans Med Imaging. 2011;30:1678-1690. http://dx.doi.org/10.1109/tmi.2011.2142188
Si-Mohamed S, Bar-Ness D, Sigovan M, et al. Review of an initial experience with an experimental spectral photon-counting computed tomography system. Nucl Instrum Methods Phys Res A. 2017;873:27-35.
Wang S, Xing Y, Zhang L, Xu X. Enhanced material separation with a quasi-monochromatic CT imaging method using a photon counting detector. Nucl Instrum Methods Phys Rest A. 2018;881:9-15. http://dx.doi.org/10.1016/j.nima.2017.10.066
Zbijewski W, Gang GJ, Xu J, et al. Dual-energy cone-beam CT with a flat-panel detector: Effect of reconstruction algorithm on material classification. Med Phys. 2014;41:021908.http://dx.doi.org/10.1118/1.4863598
Lu M, Wang A, Shapiro E, et al. Dual energy imaging with a dual-layer at panel detector. In: Schmidt TG, Chen GH, Bosmans H, eds. Proc SPIE Medical Imaging 2019: Physics of Medical Imaging. Bethesda: International Society for Optics and Photonics SPIE; 2019:269-278.
Shi L, Lu M, Bennett NR, et al. Characterization and potential applications of a dual-layer flat-panel detector. Medical Phys. 2020;47:3332-3343. http://dx.doi.org/10.1002/mp.14211
Siewerdsen JH, Zbijewsk W, XU J. Cone-beam CT image quality. In: Cone Beam Computed Tomography. 2014;4:37-58.
Wang W, Gang GJ, Siewerdsen JH, Stayman JW. Predicting image properties in penalized-likelihood reconstructions of flat-panel CBCT. Med Phys. 2019;46:65-80. http://dx.doi.org/10.1002/mp.13249
Alvarez RE, Macovski A. Energy-selective reconstructions in X-ray computerised tomography. Phys Med Biol. 1976;21:733-744. http://dx.doi.org/10.1088/0031-9155/21/5/002
Alvarez RE. Estimator for photon counting energy selective x-ray imaging with multibin pulse height analysis. Med Phys. 2011;38:2324-2334.
Liu X, Yu L, Primak AN, McCollough CH. Quantitative imaging of element composition and mass fraction using dual-energy CT: Three-material decomposition. Med Phys. 2009;36:1602-1609.
Zimmerman KC, Schmidt TG. Experimental comparison of empirical material decomposition methods for spectral CT. Phys Med Biol. 2015;60:3175-3191. http://dx.doi.org/10.1088/0031-9155/60/8/3175
Schmidt TG, Barber RF, Sidky EY. A spectral CT method to directly estimate basis material maps from experimental photon-counting data. IEEE Trans Med Imaging. 2017;36:1808-1819.
Mechlem K, Ehn S, Sellerer T, et al. Joint statistical iterative material image reconstruction for spectral computed tomography using a semi-empirical forward model. IEEE Trans Med Imaging. 2018;37:68-80. http://dx.doi.org/10.1109/tmi.2017.2726687
Schirra CO, Roessl E, Koehler T, et al. Statistical reconstruction of material decomposed data in spectral CT. IEEE Trans Med Imaging. 2013;32:1249-1257.
Long Y, Fessler JA. Multi-material decomposition using statistical image reconstruction for spectral CT. IEEE Trans Med Imaging. 2014;33:1614-1626. http://dx.doi.org/10.1109/tmi.2014.2320284
Zhang R, Thibault JB, Bouman CA, Sauer KD, Hsieh J. Model-based iterative reconstruction for dual-energy X-ray CT using a joint quadratic likelihood model. IEEE Trans Med Imaging. 2014;33:117-134. http://dx.doi.org/10.1109/tmi.2013.2282370
Tilley S II, Zbijewski W, Stayman JW. Model-based material decomposition with a penalized nonlinear least-squares CT reconstruction algorithm. Phys Med Biol. 2019;64:035005. http://dx.doi.org/10.1088/1361-6560/aaf973
Wang W, Tivnan M, Gang GJ, et al. Model-based material decomposition with system blur modeling. Proc SPIE Medical Imaging: Physics of Medical Imaging. 11312. 2020. https://doi.org/10.1117/12.2549549
Ma YQ, Wang W, Tivnan M, et al. High-resolution model-based material decomposition for multi-layer flat-panel detectors. Conf Proc Int Conf Image Form Xray Comput Tomogr. 2020:62-64.
Tivnan M, Wang W, Stayman JW. A preconditioned algorithm for model-based iterative CT reconstruction and material decomposition from spectral CT data. arXiv. 2020;1-6.
Punnoose J, Xu J, Sisniega A, Zbijewski W, Siewerdsen JH. Technical Note: SPEKTR 3.0-A computational tool for x-ray spectrum modeling and analysis. Med Phys. 2016;43:4711-4717.
Cao Q, Sisniega A, Brehler M, et al. Modeling and evaluation of a high resolution CMOS detector for cone-beam CT of the extremities. Med Phys. 2018;45:114-130.
Howansky A, Peng B, Lubinsky AR, Zhao W. Deriving depth-dependent light escape efficiency and optical Swank factor from measured pulse height spectra of scintillators. Med Phys. 2017;44:847-860. http://dx.doi.org/10.1002/mp.12083
Samei E, Flynn MJ, Reimann DA. A method for measuring the presampled MTF of digital radiographic systems using an edge test device. Med Phys. 1998;25:102-113. http://dx.doi.org/10.1118/1.598165
Shi H, Gang G, Li J, Liapi E, Abbey C. Performance assessment of texture reproduction in high-resolution CT. In Samuelson FW, Taylor-Phillips S, eds. Proc SPIE Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment. March 2020:25SPIE 2020. 2020:113160R-1-113160R-6. https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11316/113160R/Performance-assessment-of-texture-reproduction-in-high-resolution-CT/10.1117/12.2550579.full?casa_token=n7247MByIfkAAAAA%3aXcAHFNEoNI_erSGPP-V4YcYDqrsKnxf-upb4KSyoZvGterJH9KrRdyvwSB10P0mH8Z66iJPr&SSO=1
Cho Y, Moseley DJ, Siewerdsen JH, Jaffray DA. Accurate technique for complete geometric calibration of cone-beam computed tomography systems. Med Phys. 2005;32:968-983. http://dx.doi.org/10.1118/1.1869652
Tilley S, Jacobson M, Cao Q. Penalized-likelihood reconstruction with high-fidelity measurement models for high-resolution cone-beam imaging. IEEE Trans Med Imaging. 2018;37:988-999. http://dx.doi.org/10.1109/tmi.2017.2779406
Tilley S, Siewerdsen JH, Stayman JW. Model-based iterative reconstruction for flat-panel cone-beam CT with focal spot blur, detector blur, and correlated noise. Phys Med Biol. 2016;61:296-319. http://dx.doi.org/10.1088/0031-9155/61/1/296
Wang W, Tivnan M, Gang GJ, Stayman JW. Prospective prediction and control of image properties in model-based material decomposition for spectral CT. In Chen G-H, Bosmans H, eds. Proc SPIE Medical Imaging 2020: Physics of Medical Imaging. 11312. SPIE; 2020:475-480. https://doi.org/10.1117/12.2549777
Zhao C, Liu SZ, Wang W, et al. Effects of X-Ray scatter in quantitative dual-energy imaging using dual-layer flat panel detectors. In Bosmans H, Zhao W, Yu L, eds. Proc SPIE Medical Imaging: Physics of Medical Imaging;11595. SPIE; 2021. https://doi.org/10.1117/12.2581822