@article{Liu2020bb,

title = {Model-based dual-energy tomographic image reconstruction of objects containing known metal components},

author = {Stephen Liu and Qian Cao and Matt Tivnan and Steven Tilley and Jeffrey H. Siewerdsen and J. Webster Stayman and Wojciech Zbijewski},

url = {https://pubmed.ncbi.nlm.nih.gov/33113519/},

doi = {10.1088/1361-6560/abc5a9},

year  = {2020},

date = {2020-12-15},

journal = {Physics in Medicine and Biology},

volume = {65},

number = {24},

pages = {245046},

keywords = {Artifact Correction, Known Components, MBIR, Metal Artifacts, Spectral X-ray/CT},

pubstate = {published},

tppubtype = {article}

}

@article{Uneri2019b,

title = {Known-component metal artifact reduction (KC-MAR) for cone-beam CT},

author = {Ali Uneri and Xiaoxuan Zhang and T. Yi and J. Webster Stayman and Patrick Helm and Greg M. Osgood and Nick Theodore and Jeffrey H. Siewerdsen},

url = {https://pubmed.ncbi.nlm.nih.gov/31287092/},

doi = {10.1088/1361-6560/ab3036},

year  = {2019},

date = {2019-08-01},

journal = {Physics in Medicine and Biology},

volume = {64},

number = {16},

pages = {165021 },

abstract = {Intraoperative cone-beam CT (CBCT) is increasingly used for surgical navigation and validation of device placement. In spinal deformity correction, CBCT provides visualization of pedicle screws and fixation rods in relation to adjacent anatomy. This work reports and evaluates a method that uses prior information regarding such surgical instrumentation for improved metal artifact reduction (MAR). The known-component MAR (KC-MAR) approach achieves precise localization of instrumentation in projection images using rigid or deformable 3D-2D registration of component models, thereby overcoming residual errors associated with segmentation-based methods. Projection data containing metal components are processed via 2D inpainting of the detector signal, followed by 3D filtered back-projection (FBP). Phantom studies were performed to identify nominal algorithm parameters and quantitatively investigate performance over a range of component material composition and size. A cadaver study emulating screw and rod placement in spinal deformity correction was conducted to evaluate performance under realistic clinical imaging conditions. KC-MAR demonstrated reduction in artifacts (standard deviation in voxel values) across a range of component types and dose levels, reducing the artifact to 5-10 HU. Accurate component delineation was demonstrated for rigid (screw) and deformable (rod) models with sub-mm registration errors, and a single-pixel dilation of the projected components was found to compensate for partial-volume effects. Artifacts associated with spine screws and rods were reduced by 40%-80% in cadaver studies, and the resulting images demonstrated markedly improved visualization of instrumentation (e.g. screw threads) within cortical margins. The KC-MAR algorithm combines knowledge of surgical instrumentation with 3D image reconstruction in a manner that overcomes potential pitfalls of segmentation. The approach is compatible with FBP-thereby maintaining simplicity in a manner that is consistent with surgical workflow-or more sophisticated model-based reconstruction methods that could further improve image quality and/or help reduce radiation dose.},

keywords = {Known Components, Metal Artifacts, Prior Images},

pubstate = {published},

tppubtype = {article}

}

@article{Zhang2019b,

title = {Known-component 3D image reconstruction for improved intraoperative imaging in spine surgery: A clinical pilot study},

author = {Xiaoxuan Zhang and Ali Uneri and J. Webster Stayman and C. C. Zygourakis and S. L. Lo and Nick Theodore and Jeffrey H. Siewerdsen},

url = {https://pubmed.ncbi.nlm.nih.gov/31180586/},

doi = {10.1002/mp.13652},

year  = {2019},

date = {2019-08-01},

journal = {Medical Physics},

volume = {46},

number = {8},

pages = {3483-95},

abstract = {Purpose: Intraoperative imaging plays an increased role in support of surgical guidance and quality assurance for interventional approaches. However, image quality sufficient to detect complications and provide quantitative assessment of the surgical product is often confounded by image noise and artifacts. In this work, we translated a three-dimensional model-based image reconstruction (referred to as "Known-Component Reconstruction," KC-Recon) for the first time to clinical studies with the aim of resolving both limitations.



Methods: KC-Recon builds upon a penalized weighted least-squares (PWLS) method by incorporating models of surgical instrumentation ("known components") within a joint image registration-reconstruction process to improve image quality. Under IRB approval, a clinical pilot study was conducted with 17 spine surgery patients imaged under informed consent using the O-arm cone-beam CT system (Medtronic, Littleton MA) before and after spinal instrumentation. Volumetric images were generated for each patient using KC-Recon in comparison to conventional filtered backprojection (FBP). Imaging performance prior to instrumentation ("preinstrumentation") was evaluated in terms of soft-tissue contrast-to-noise ratio (CNR) and spatial resolution. The quality of images obtained after the instrumentation ("postinstrumentation") was assessed by quantifying the magnitude of metal artifacts (blooming and streaks) arising from pedicle screws. The potential low-dose advantages of the algorithm were tested by simulating low-dose data (down to one-tenth of the dose of standard protocols) from images acquired at normal dose.



Results: Preinstrumentation images (at normal clinical dose and matched resolution) exhibited an average 24.0% increase in soft-tissue CNR with KC-Recon compared to FBP (N = 16, P = 0.02), improving visualization of paraspinal muscles, major vessels, and other soft-tissues about the spine and abdomen. For a total of 72 screws in postinstrumentation images, KC-Recon yielded a significant reduction in metal artifacts: 66.3% reduction in overestimation of screw shaft width due to blooming (P < 0.0001) and reduction in streaks at the screw tip (65.8% increase in attenuation accuracy, P < 0.0001), enabling clearer depiction of the screw within the pedicle and vertebral body for an assessment of breach. Depending on the imaging task, dose reduction up to an order of magnitude appeared feasible while maintaining soft-tissue visibility and metal artifact reduction.



Conclusions: KC-Recon offers a promising means to improve visualization in the presence of surgical instrumentation and reduce patient dose in image-guided procedures. The improved soft-tissue visibility could facilitate the use of cone-beam CT to soft-tissue surgeries, and the ability to precisely quantify and visualize instrument placement could provide a valuable check against complications in the operating room (cf., postoperative CT).



Keywords: cone-beam CT; image-guided procedures; intraoperative imaging; model-based image reconstruction; patient safety.},

keywords = {CBCT, Image Guided Surgery, Known Components, MBIR, Metal Artifacts, Spine},

pubstate = {published},

tppubtype = {article}

}

@article{Xu2017,

title = {Polyenergetic known-component CT reconstruction with unknown material compositions and unknown x-ray spectra},

author = {Shiyu Xu and A. Jay Khanna and Jeffrey H. Siewerdsen and J. Webster Stayman },

url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5728157/

http://iopscience.iop.org/article/10.1088/1361-6560/aa6285/meta

},

doi = {10.1088/1361-6560/aa6285},

year  = {2017},

date = {2017-03-28},

journal = {Physics in medicine and biology},

volume = {62},

number = {8},

pages = {3352-3374},

keywords = {Artifact Correction, Beam Hardening, Known Components, MBIR, Metal Artifacts},

pubstate = {published},

tppubtype = {article}

}

@article{Xu2017,

title = {Polyenergetic known-component CT reconstruction with unknown material compositions and unknown x-ray spectra},

author = {Shiyu Xu and A. Jay Khanna and Jeffrey H. Siewerdsen and J. Webster Stayman},

url = {https://www.ncbi.nlm.nih.gov/pubmed/28230539

http://iopscience.iop.org/article/10.1088/1361-6560/aa6285/meta},

doi = {10.1088/1361-6560/aa6285},

year  = {2017},

date = {2017-02-23},

journal = {Physics in Medicine and Biology},

volume = {62},

number = {8},

pages = {3352-3374},

keywords = {Artifact Correction, Beam Hardening, Known Components, Metal Artifacts},

pubstate = {published},

tppubtype = {article}

}

@article{Uneri2015,

title = {Known-component 3D-2D registration for quality assurance of spine surgery pedicle screw placement.},

author = {Ali Uneri and Tharindu De Silva and J. Webster Stayman and Gerhard Kleinszig and Sebastian Vogt and A. Jay Khanna and Ziya L. Gokaslan and John-Paul Wolinsky and Jeffrey H. Siewerdsen },

url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4640195},

doi = {10.1088/0031-9155/60/20/8007},

issn = {1361-6560},

year  = {2015},

date = {2015-10-01},

journal = {Physics in medicine and biology},

volume = {60},

number = {20},

pages = {8007--24},

abstract = {A 3D-2D image registration method is presented that exploits knowledge of interventional devices (e.g. K-wires or spine screws-referred to as 'known components') to extend the functionality of intraoperative radiography/fluoroscopy by providing quantitative measurement and quality assurance (QA) of the surgical product. The known-component registration (KC-Reg) algorithm uses robust 3D-2D registration combined with 3D component models of surgical devices known to be present in intraoperative 2D radiographs. Component models were investigated that vary in fidelity from simple parametric models (e.g. approximation of a screw as a simple cylinder, referred to as 'parametrically-known' component [pKC] registration) to precise models based on device-specific CAD drawings (referred to as 'exactly-known' component [eKC] registration). 3D-2D registration from three intraoperative radiographs was solved using the covariance matrix adaptation evolution strategy (CMA-ES) to maximize image-gradient similarity, relating device placement relative to 3D preoperative CT of the patient. Spine phantom and cadaver studies were conducted to evaluate registration accuracy and demonstrate QA of the surgical product by verification of the type of devices delivered and conformance within the 'acceptance window' of the spinal pedicle. Pedicle screws were successfully registered to radiographs acquired from a mobile C-arm, providing TRE 1-4 mm and textless5° using simple parametric (pKC) models, further improved to textless1 mm and textless1° using eKC registration. Using advanced pKC models, screws that did not match the device models specified in the surgical plan were detected with an accuracy of textgreater99%. Visualization of registered devices relative to surgical planning and the pedicle acceptance window provided potentially valuable QA of the surgical product and reliable detection of pedicle screw breach. 3D-2D registration combined with 3D models of known surgical devices offers a novel method for intraoperative QA. The method provides a near-real-time independent check against pedicle breach, facilitating revision within the same procedure if necessary and providing more rigorous verification of the surgical product.},

keywords = {Image Registration, Known Components, Spine},

pubstate = {published},

tppubtype = {article}

}

@article{Stayman2012a,

title = {Model-based tomographic reconstruction of objects containing known components.},

author = {J. Webster Stayman and Yoshito Otake and Jerry L. Prince and A. Jay Khanna and Jeffrey H. Siewerdsen},

url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4503263},

doi = {10.1109/TMI.2012.2199763},

issn = {1558-254X},

year  = {2012},

date = {2012-10-01},

journal = {IEEE transactions on medical imaging},

volume = {31},

number = {10},

pages = {1837--48},

abstract = {The likelihood of finding manufactured components (surgical tools, implants, etc.) within a tomographic field-of-view has been steadily increasing. One reason is the aging population and proliferation of prosthetic devices, such that more people undergoing diagnostic imaging have existing implants, particularly hip and knee implants. Another reason is that use of intraoperative imaging (e.g., cone-beam CT) for surgical guidance is increasing, wherein surgical tools and devices such as screws and plates are placed within or near to the target anatomy. When these components contain metal, the reconstructed volumes are likely to contain severe artifacts that adversely affect the image quality in tissues both near and far from the component. Because physical models of such components exist, there is a unique opportunity to integrate this knowledge into the reconstruction algorithm to reduce these artifacts. We present a model-based penalized-likelihood estimation approach that explicitly incorporates known information about component geometry and composition. The approach uses an alternating maximization method that jointly estimates the anatomy and the position and pose of each of the known components. We demonstrate that the proposed method can produce nearly artifact-free images even near the boundary of a metal implant in simulated vertebral pedicle screw reconstructions and even under conditions of substantial photon starvation. The simultaneous estimation of device pose also provides quantitative information on device placement that could be valuable to quality assurance and verification of treatment delivery.},

keywords = {Artifact Correction, Image Registration, Known Components, MBIR, Metal Artifacts, Spine},

pubstate = {published},

tppubtype = {article}

}