Polyenergetic Known-Component Reconstruction without Prior Shape Models

Abstract

Purpose: Previous work has demonstrated that structural models of surgical tools and implants can be integrated into model-based CT reconstruction to greatly reduce metal artifacts and improve image quality. This work extends a polyenergetic formulation of known-component reconstruction (Poly-KCR) by removing the requirement that a physical model (e.g. CAD drawing) be known a priori, permitting much more widespread application.

Methods: We adopt a single-threshold segmentation technique with the help of morphological structuring elements to build a shape model of metal components in a patient scan based on initial filtered-backprojection (FBP) reconstruction. This shape model is used as an input to Poly-KCR, a formulation of known-component reconstruction that does not require a prior knowledge of beam quality or component material composition. An investigation of performance as a function of segmentation thresholds is performed in simulation studies, and qualitative comparisons to Poly-KCR with an a priori shape model are made using physical CBCT data of an implanted cadaver and in patient data from a prototype extremities scanner.

Results: We find that model-free Poly-KCR (MF-Poly-KCR) provides much better image quality compared to conventional reconstruction techniques (e.g. FBP). Moreover, the performance closely approximates that of Poly- KCR with an a prior shape model. In simulation studies, we find that imaging performance generally follows segmentation accuracy with slight under- or over-estimation based on the shape of the implant. In both simulation and physical data studies we find that the proposed approach can remove most of the blooming and streak artifacts around the component permitting visualization of the surrounding soft-tissues.

Conclusion: This work shows that it is possible to perform known-component reconstruction without prior knowledge of the known component. In conjunction with the Poly-KCR technique that does not require knowledge of beam quality or material composition, very little needs to be known about the metal implant and system beforehand. These generalizations will allow more widespread application of KCR techniques in real patient studies where the information of surgical tools and implants is limited or not available.

Fig. 1

Illustration of sensitivity to component model accuracy in two simulated phantoms emulating (a) a cuboid implant; and (b) an orthopedic nail. The ground truth, FBP reconstruction, and MF-Poly-KCR reconstructions over a range of segmentation threshols (mm⁻¹) are shown. The red square shows the optimal results in terms of RMSE in the background.

Fig. 2

Comparison of imaging and segmentation performance as a function of threshold for the two simulation phantoms: a) cuboid implant, and b) hollow orthopedic nail implant. Note that the imaging optimum is not coincident with the segmentation optimum. A higher threshold is optimal for the cuboid implant suggesting that it is better to underestimate the size of the component for imaging while a lower threshold is optimal for the rod-shaped implant.

Fig. 3

A comparison of reconstruction methods for flat-panel CBCT data of an implanted cadaver. While significant metal artifacts are present in the FBP reconstruction, blooming and streak artifacts are greatly reduced in the proposed model-free Poly-KCR approach. Moreover, the image quality approaches that of the Poly-KCR approach where a CAD model was used to inform the model-based reconstruction.

Fig. 4

Reconstructions of patient data from the prototype extremities scanner. Significant metal artifacts are found in the FBP reconstruction while metal artifacts are largely mitigated in the MF-Poly-KCR approach. In particular MF-Poly-KCR is able to reduce streak artifacts and improve visibility near the tibial fracture.