Polyenergetic known-component reconstruction without prior shape models

Dual-energy (DE) decomposition has been adopted in orthopedic imaging to measure bone composition and visualize intraarticular contrast enhancement. One of the potential applications involves monitoring of callus mineralization for longitudinal assessment of fracture healing. However, fracture repair usually involves internal fixation hardware that can generate significant artifacts in reconstructed images. To address this challenge, we develop a novel algorithm that combines simultaneous reconstruction-decomposition using a previously reported method for model-based material decomposition (MBMD) augmented by the known-component (KC) reconstruction framework to mitigate metal artifacts. We apply the proposed algorithm to simulated DE data representative of a dedicated extremity cone-beam CT (CBCT) employing an x-ray unit with three vertically arranged sources. The scanner generates DE data with non-coinciding high- and low-energy projection rays when the central source is operated at high tube potential and the peripheral sources at low potential. The proposed algorithm was validated using a digital extremity phantom containing varying concentrations of Ca-water mixtures and Ti implants. Decomposition accuracy was compared to MBMD without the KC model. The proposed method suppressed metal artifacts and yielded estimated Ca concentrations that approached the reconstructions of an implant-free phantom for most mixture regions. In the vicinity of simple components, the errors of Ca density estimates obtained by incorporating KC in MBMD were ∼1.5–5× lower than the errors of conventional MBMD; for cases with complex implants, the errors were ∼3–5× lower. In conclusion, the proposed method can achieve accurate bone mineral density measurements in the presence of metal implants using non-coinciding DE projections acquired on a multisource CBCT system.

Section: Discussionmentioning

confidence: 99%

“…In KC reconstructions, prior knowledge of the metal component (e.g. shape and composition) is incorporated into the object forward model (Stayman et al 2012, Zhang et al 2017 after the location of the component is established using 3D-2D registration (Uneri et al 2015(Uneri et al , 2017(Uneri et al , 2019.…”

Section: Introductionmentioning

confidence: 99%

Model-based dual-energy tomographic image reconstruction of objects containing known metal components

Liu¹,

Cao²,

Tivnan³

et al. 2020

“…using a deformable model). Ongoing work has sought to extend the KCR methodology where shape models are not known a priori through an initial segmentation of reconstructed data to build a shape model (Zhang et al 2017). Additional generalizations of component knowledge to accommodate surgical tools, implants, and other components for which prior knowledge of structure is not well-established could permit more widespread application of these approaches.…”

Section: Discussionmentioning

confidence: 99%

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

Uneri

Khanna

et al. 2017

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.

“…As an alternative to MV imaging, considerable prior work (Stayman et al 2012a, 2012b, , Xu et al 2017, Zhang et al 2017 has demonstrated improvements in kV-CBCT image reconstruction quality when the presence of metal in the patient can be explicitly modeled into the reconstruction process. Such advanced methods, however, require that the shape, material composition, and geometric pose be determined either a priori, or else co-estimated in the course of the reconstruction.…”

Section: Introductionmentioning

confidence: 99%

A kV–MV approach to CBCT metal artifact reduction using multi-layer MV-CBCT

Jacobson,

Harris,

Myronakis

et al. 2024

Objective: To demonstrate that complete cone beam CT (CBCT) scans from both MV-energy and kV-energy LINAC sources can reduce metal artifacts in radiotherapy guidance, while maintaining standard-of-care x-ray doses levels.Approach: MV-CBCT and kV-CBCT scans are acquired at half normal dose. The impact of lowered dose on MV-CBCT data quality is mitigated by the use of a 4-layer MV-imager prototype and reduced LINAC energy settings (2.5 MV) to improve photon capture. Additionally, the MV-CBCT is used to determine the 3D position and pose of metal implants, which in turn is used to guide model-based poly-energetic correction and interleaving of the kV-CBCT and MV-CBCT data. Certain edge-preserving regularization steps incorporated into the model-based correction algorithm further reduce MV data noise.Main Results: The method was tested in digital phantoms and a real pelvis phantom with large 2.5” spherical inserts, emulating hip replacements of different materials. The proposed method demonstrated an appealing compromise between the high contrast of kV-CBCT and low artifact content of MV-CBCT. Contrast-to-noise improved 3-fold compared to MV-CBCT with a clinical 1-layer architecture at matched dose (37 mGy) and edge blur levels. Visual delineation of the bladder and prostate improved noteably over kV- or MV-CBCT alone.Significance: The proposed method demonstrates that a full MV-CBCT scan can be combined with kV-CBCT to reduce metal artifacts without resorting to complicated beam collimation strategies to limit the MV-CBCT dose contribution. Additionally, significant improvements in CNR can be achieved as compared to metal artifact reduction through current clinical MV-CBCT practices.