Polyenergetic known-component CT reconstruction with unknown material compositions and unknown x-ray spectra

Abstract

Metal artifacts can cause substantial image quality issues in computed tomography. This is particularly true in interventional imaging where surgical tools or metal implants are in the field-of-view. Moreover, the region-of-interest is often near such devices which is exactly where image quality degradations are largest. Previous work on known-component reconstruction (KCR) has shown the incorporation of a physical model (e.g. shape, material composition, etc) of the metal component into the reconstruction algorithm can significantly reduce artifacts even near the edge of a metal component. However, for such approaches to be effective, they must have an accurate model of the component that include energy-dependent properties of both the metal device and the CT scanner, placing a burden on system characterization and component material knowledge. In this work, we propose a modified KCR approach that adopts a mixed forward model with a polyenergetic model for the component and a monoenergetic model for the background anatomy. This new approach called Poly-KCR jointly estimates a spectral transfer function associated with known components in addition to the background attenuation values. Thus, this approach eliminates both the need to know component material composition a prior as well as the requirement for an energy-dependent characterization of the CT scanner. We demonstrate the efficacy of this novel approach and illustrate its improved performance over traditional and model-based iterative reconstruction methods in both simulation studies and in physical data including an implanted cadaver sample.

Figure 1

Illustration of A) the CAD model of a pedicle screw used in simulation experiments and B) a digital thorax and abdominal phantom used for the anatomical background.

Figure 2

Illustration of A) the CBCT test-bench and B) the image quality phantom with water-and-spheres background, titanium pedicle screw, and two medium-contrast, 9 lp/cm, line pair blocks outlined in yellow.

Figure 3

Reconstructions of digital phantom data that includes one pedicle screw with a priori unknown material properties. A) The ground truth image volume with the screw shown as a red color overlay. Simulated fractures are indicated by the yellow arrows. B) FBP reconstruction of the digital phantom shows prominent metal artifacts arising from energy-dependent effects and photon starvation. These artifacts obscure anatomy near the screw including the simulated fractures. C) The Poly-KCR approach effectively uses the shape of the known component to greatly improve image quality. Artifacts and noise are largely mitigated permitting good visualization near the boundary of the screw implant and the background anatomy.

Figure 4

Illustration of Poly-KCR convergence properties and STF estimate quality. A) Objective function value differences from the solution Φ* as a function of iteration. B) RMSE as a function of iteration. C) The STF associated with the pedicle screw in the simulation. Note the close agreement between the true simulated and estimated STFs.

Figure 5

Illustration of Poly-KCR robustness to different monoenergetic initializations from 0.02 to 0.04 mm⁻¹. A) Sample reconstructions and STF estimations for 0.2, 0.3 (truth), and 0.4 mm⁻¹ cases are shown. B) RMSE for both the reconstruction and log10(STF) are plotted as a function of the initialization.

Figure 6

Illustration of the STF precalibration process. A) Component-only scanning using a very low attenuation foam platform. B) Comparison of a subsampling of measured data survival probabilities (red) and the estimated STF (cyan) for a range of path lengths through the registered known component (pedicle screw).

Figure 7

Reconstructions of the image quality phantom with a titanium pedicle screw using A) FBP; B) FBP-MAR; C) PWLS; D) Mono-KCR; E) PreCal-KCR; and F) Poly-KCR. A common grayscale is adopted for all images of 0.018 mm⁻¹ to 0.028 mm⁻¹ and line pair blocks are outline in yellow. FBP exhibits significant metal artifacts due to beam hardening and noise. Metal artifact reduction based on replacing measurements through metal with synthetic projection values greatly reduces “blooming” effects but also eliminates features near the screw boundary. PWLS reduces artifacts somewhat through better noise control but substantial artifacts due to energy-dependence remain. Mono-KCR provides some improvements over PWLS but spectrally induced artifacts remain. PreCal-KCR offers more improvements, greatly reducing artifacts; however, there are additional spectral differences (e.g. beam hardening due to the background object) that limit complete mitigation of artifacts. Poly-KCR offers the greatest image quality improvement – providing the best visualization of the boundary between the pedicle screw and the background. Relatively small artifacts persist associated with the longest path lengths through the metal component.

Figure 8

Reconstructions from the cadaver torso investigations. For each method a zoomed region-of-interest is shown in the axial slice. The grayscale is linear for all images from 0.018 mm⁻¹ to 0.028 mm⁻¹. A) FBP reconstruction exhibited substantial metal artifacts around the pedicle screw. Streaks and increased noise prevent good visualization of the pedicle screw placement within the vertebral body. B) FBP-MAR shows a significant reduction in “blooming” artifacts; however, data interpolation has obscured many features in the vicinity of the pedicle screw. C) PWLS reconstruction shows a slight improvement over FBP but significant artifacts remain. D) The Poly-KCR approach yielded substantial reductions in artifacts largely eliminating blooming and streaking effects. Relatively small residual artifacts can be seen at the head of the screw. However, image quality in the vicinity of the implant is good showing bone details, an air bubble near the tip of the implant, and the lateral breach in the body of the vertebra is easily seen suggesting a potentially significant improvement in diagnostic quality.