X-ray CT/micro-CT methods with photon-counting detectors (PCDs) and high Z materials are a hot research topic. One method using PCDs allows for spectral imaging in 5 energy windows while conventional X-ray detectors only collect energy-integrating data.
OBJECTIVE:
To demonstrate the enhanced separation of contrast materials by using PCDs, multivariate analysis, and linear discriminant methods.
METHODS:
Phantoms containing iodine and aqueous nanomaterials were scanned on a MARS spectral micro-CT. Image volumes were segmented into separate material-specific populations. Contrast comparisons were made by calculating T2 test statistics in the univariate, pseudo-conventional and multivariate, spectral CT data sets. Separability after Fisher discriminant analysis (FDA) was also assessed.
RESULTS:
The T2 values calculated for material comparisons increased as a result of the spectral expansion. The majority of the tested contrast agents showed increased T2 values by a factor of ∼2 -3. The total significant T2 statistics in the pure and mixed lanthanide image sets increased in the spectral data set.
CONCLUSION:
This work consolidates the groundwork for photon-counting-based material decomposition with micro-CT, facilitating future development of novel nanomaterials and their preclinical applications.
FlohrT.G., McColloughC.H., BruderH., PetersilkaM., GruberK., SussC., GrasruckM., StierstorferK., KraussB., RaupachR., PrimakA.N., KuttnerA., AchenbachS., BeckerC., KoppA. and OhnesorgeB.M., First performance evaluation of a dual-source ct (dsct) system, Eur Radiol16(2) (2006), 256–268.
2.
PrimakA.N., Ramirez GiraldoJ.C., LiuX., YuL. and McColloughC.H., Improved dual-energy material discrimination for dual-source ct by means of additional spectral filtration, Med Phys36(4) (2009), 1359–1369.
3.
ClarkD.P., BadeaC.T., Spectral diffusion: An algorithm for robust material decomposition of spectral ct data, Phys Med Biol59(21) (2014), 6445–6466.
lLeeO., KapplerS., PolsterC., TaguchiK., Estimation of basis line-integrals in a spectral distortion-modeled photon counting detector using low-rank approximation-based x-ray transmittance modeling: K-edge imaging application, IEEE Trans Med Imaging36(11) (2017), 2389–2403.
11.
RoesslE., ProksaR., K-edge imaging in x-ray computed tomography using multi-bin photon counting detectors, Phys Med Biol52(15) (2007), 4679–4696.
12.
SameiE., BarberW.C., HsiehJ., NygardE., IwanczykJ.S., ZhangM., FreyE.C., TsuiB.M.W., WesselJ.C., MalakhovN., WawrzyniakG., HartsoughN.E., GandhiT., TaguchiK., Characterization of a novel photon counting detector for clinical ct: Count rate, energy resolution, and noise performance, 7258 (2009), 725824.
13.
TaguchiK., PolsterC., LeeO., StierstorferK., KapplerS., Spatio-energetic cross talk in photon counting detectors: Detector model and correlated poisson data generator, Med Phys43(12) (2016), 6386–6404.
14.
SchlomkaJ.P., RoesslE., DorscheidR., DillS., MartensG., IstelT., BaumerC., HerrmannC., SteadmanR., ZeitlerG., LivneA., ProksaR., Experimental feasibility of multi-energy photon-counting k-edge imaging in pre-clinical computed tomography, Phys Med Biol53(15) (2008), 4031–4047.
15.
HeP., WeiB., CongW., WangG., Optimization of k-edge imaging with spectral ct, Med Phys39(11) (2012), 6572–6579.
BatemanC., McMahonJ., MalpasA., De RuiterN., BellS., ButlerA., ButlerP. and RenaudP., Segmentation enhances material analysis in multi-energy ct: A simulation study, in pp, Image and Vision Computing New Zealand (IVCNZ), 2013 28th International Conference of (IEEE, ed.), pp. 190–195.
WangX., MeierD., TaguchiK., WagenaarD.J., PattB.E., FreyE.C., Material separation in x-ray ct with energy resolved photon-counting detectors, Med Phys38(3) (2011), 1534–1546.
24.
AlvarezR.E., MacovskiA., Energy-selective reconstructions in x-ray computerized tomography, Phys Med Biol21(5) (1976), 733–744.
25.
KarunamuniR., MaidmentA.D., Search for novel contrast materials in dual-energy x-ray breast imaging using theoretical modeling of contrast-to-noise ratio, Phys Med Biol59(15) (2014), 4311–4324.
YeJ., Least squares linear discriminant analysis, Proceedings of the 24th International Conference on Machine Learning. (ACM, ed.).
28.
GordonR., BenderR., HermanG., Algebraic reconstruction techniques (art) for three-dimensional electron microscopy and x-ray photography, Journal of theoretical Biology29(3) (1970), 471–481.
29.
AndersenA., Simultaneous algebraic reconstruction technique (sart): A superior implementation of the art algorithm, Ultrasonic Imaging6(1) (1984), 81–94.
