The effect of different image reconstruction techniques on pre-clinical quantitative imaging and dual-energy CT

Abstract

Objective:: To analyse the effect of different image reconstruction techniques on image quality and dual energy CT (DECT) imaging metrics.

Methods:: A software platform for pre-clinical cone beam CT X-ray image reconstruction was built using the open-source reconstruction toolkit. Pre-processed projections were reconstructed with filtered back-projection and iterative algorithms, namely Feldkamp, Davis, and Kress (FDK), Iterative FDK, simultaneous algebraic reconstruction technique (SART), simultaneous iterative reconstruction technique and conjugate gradient. Imaging metrics were quantitatively assessed, using a quality assurance phantom, and DECT analysis was performed to determine the influence of each reconstruction technique on the relative electron density (ρ_e) and effective atomic number (Z_eff) values.

Results:: Iterative reconstruction had favourable results for the DECT analysis: a significantly smaller spread for each material in the ρ_e-Z_eff space and lower Z_eff and ρ_e residuals (on average 24 and 25% lower, respectively). In terms of image quality assurance, the techniques FDK, Iterative FDK and SART provided acceptable results. The three reconstruction methods showed similar geometric accuracy, uniformity and CT number results. The technique SART had a contrast-to-noise ratio up to 76% higher for solid water and twice as high for Teflon, but resolution was up to 28% lower when compared to the other two techniques.

Conclusions:: Advanced image reconstruction can be beneficial, but the benefit is small, and calculation times may be unacceptable with current technology. The use of targeted and downscaled reconstruction grids, larger, yet practicable, pixel sizes and GPU are recommended.

Advances in knowledge:: An iterative CBCT reconstruction platform was build using RTK.

Figure 1.

Image acquisition and reconstruction workflow. Pre-acquisition corrections: (a) defective pixels, (b) dark field, (c) flood field, (d) lateral and (e) longitudinal offset for each gantry angle (flexmap). (f) Cone beam CT acquisition, X-ray tube and the flat panel rotate 360° around the object and generate the pre-processed projection data. (g) Reconstruction cycle, with or without cupping correction. The boxes with RS, rescale and cupping function indicate where the RS and where MATLAB routines were used, respectively. The boxes with phantom or water cylinder illustrate the reconstructed object at that step. The inner image at the centre indicates the reconstruction algorithms. They could be either FBP or IR. IR methods follow the cycle of panel (h). FBP, filtered back-projection; IR, iterativere construction; RS, reconstruction software.

Figure 2.

Phantoms used in this study. (a–e) Different plates of the mCTP-610 quality assurance phantom: (a) CT Number, (b) uniformity, (c) geometric accuracy, (d) resolution coils and (e) slanted edge. The regions of interest used for the image analysis are shaded in red and green. (f) Schematic drawing of the calibration and validation phantoms used for the dual-energy CT analysis. The dashed line represents the 7 cm diameter phantom, which is composed by an outer ring encompassing the 3 cm phantom, represented by the solid line. Both rings are made of the same bulk material (solid water).

Figure 3.

(a) Reconstruction time for each technique vs number of iterations for the 90kVp images with 602 projections, required by an AMD Opteron^™ Processor 6272, 32 physical cores and 128 GB of RAM. CG image after (b) three and (c) 40 iterations (45 h). Iterative FDK image after (d) one and (e) three iterations. SART image after (f) one and (g) three iterations. (h) SIRT image after 100 iterations for a grid of (512 pixels) with pixel size of (0.2 mm), which took 80 h—if the 1024 grid used for the other methods was repeated here the reconstruction times would be even longer. (i) FDK reconstruction. The numbers in the top right corner indicate the number of iterations used in each image. Figures b-i were extracted from a central region of the 7 cm phantom. (b) and (h) present a background different colour because they have not yet converged. (j) Reconstruction times using a smaller grid of 250 pixels × 250 pixels × 200 pixels and voxel size of (0.2 mm) to illustrate a scenario closer to a pre-clinical practice workflow, where only a region of interest in the object is selected. Vertical scale is in minutes here, while in panel a it is in hours. CG, conjugate gradient; FDK, Feldkamp, Davis, and Kress; SART, simultaneous algebraic reconstruction technique.

Figure 4.

Comparison of phantom HU profiles for different reconstruction methods with and without cupping correction for the 40 kVp scan, where cupping is most evident, of the 7 cm phantom using a second order polynomial (C2). HU, Hounsfield unit.

Figure 5.

Quantitative image analysis tests with the mCTP-610 phantom. (a) Geometric accuracy, the blue horizontal line represents the utilized pixel size. (b) CNR for Teflon. (c) Uniformity. (d) CNR for solid water. (e) CT number for tissue equivalent materials for 40 to 120 kVp, the markers colour shade darkens as the energy increases. (f) MTF using both SE and RC techniques for the 50kVp image. The line indicates the MTF at 10%. CNR, contrast-to-noise ratio; MTF, modulation transfer function; RC, resolution coil; SE, slanted edge.

Figure 6.

ρ_e–Z_eff plot for the 7 cm validation DECT phantom and the energy combination 50 and 90 kVp. The colours and the lines indicate the tissue-equivalent inserts and reconstruction methods. Histograms of ρ_e and Z_eff values are illustrated on top and on the right, respectively. The circles, triangles and squares indicate the mean value of each distribution for FDK, Iterative FDK and SART, respectively. DECT, dual-energy CT; FDK, Feldkamp, Davis, and Kress; SART, simultaneous algebraic reconstruction technique.

Figure 7.

Mean (a) Z_eff and (b) ρ_e residuals, and mean (c) Z_eff and (d) ρ_e standard deviations for a number of DECT energy combinations. DECT, dual-energy CT; FDK, Feldkamp, Davis, and Kress; SART, simultaneous algebraic reconstruction technique.