@article{Reynolds2023,

title = {Extended longitudinal and lateral 3D imaging with a continuous dual‐isocenter CBCT scan},

author = {Tess Reynolds and Sepideh Hatamikia and Yiqun Ma and Owen Dillon and Grace Gang and J. Webster Stayman and Ricky T O'Brien},

url = {https://aapm.onlinelibrary.wiley.com/doi/full/10.1002/mp.16234

https://pubmed.ncbi.nlm.nih.gov/36681083/},

doi = {10.1002/mp.16234},

year  = {2023},

date = {2023-04-01},

journal = {Medical Physics},

volume = {50},

issue = {4},

pages = {2372-2379},

keywords = {CBCT, Customized Acquisition, Spine},

pubstate = {published},

tppubtype = {article}

}

@article{Reynolds2022,

title = {Extended Intraoperative Longitudinal 3-Dimensional Cone Beam Computed Tomography Imaging With a Continuous Multi-Turn Reverse Helical Scan},

author = {Tess Reynolds and Yiqun Ma and Andrew J Kanawati and Alex Constantinidis and Zoe Williams and Grace Gang and Owen Dillon and Tom Russ and Wenying Wang and Tina Ehtiati and Clifford Weiss and Nick Theodore and Jeffrey H. Siewerdsen and J. Webster Stayman and Ricky T O'Brien},

url = {https://pubmed.ncbi.nlm.nih.gov/35510875/, https://journals.lww.com/investigativeradiology/Fulltext/2022/11000/Extended_Intraoperative_Longitudinal_3_Dimensional.7.aspx},

doi = {10.1097/RLI.0000000000000885 },

year  = {2022},

date = {2022-05-03},

journal = {Investigative Radiology},

volume = {57},

issue = {11},

keywords = {CBCT, Customized Acquisition, Spine},

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{Uneri2018c,

title = {Image quality and dose characteristics for an O‐arm intraoperative imaging system with model‐based image reconstruction},

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

url = {https://aapm.onlinelibrary.wiley.com/doi/full/10.1002/mp.13167},

doi = {10.1002/mp.13167},

year  = {2018},

date = {2018-09-04},

journal = {Medical Physics},

volume = {45},

number = {11},

pages = {4857-4868},

keywords = {CBCT, MBIR, Spine, System Assessment},

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{Otake2013,

title = {Robust 3D-2D image registration: application to spine interventions and vertebral labeling in the presence of anatomical deformation.},

author = {Yoshito Otake and Adam S. Wang and J. Webster Stayman and Ali Uneri and Gerhard Kleinszig and Sebastian Vogt and A. Jay Khanna and Ziya L. Gokaslan and Jeffrey H. Siewerdsen },

url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4915373},

doi = {10.1088/0031-9155/58/23/8535},

issn = {1361-6560},

year  = {2013},

date = {2013-12-01},

journal = {Physics in medicine and biology},

volume = {58},

number = {23},

pages = {8535--53},

abstract = {We present a framework for robustly estimating registration between a 3D volume image and a 2D projection image and evaluate its precision and robustness in spine interventions for vertebral localization in the presence of anatomical deformation. The framework employs a normalized gradient information similarity metric and multi-start covariance matrix adaptation evolution strategy optimization with local-restarts, which provided improved robustness against deformation and content mismatch. The parallelized implementation allowed orders-of-magnitude acceleration in computation time and improved the robustness of registration via multi-start global optimization. Experiments involved a cadaver specimen and two CT datasets (supine and prone) and 36 C-arm fluoroscopy images acquired with the specimen in four positions (supine, prone, supine with lordosis, prone with kyphosis), three regions (thoracic, abdominal, and lumbar), and three levels of geometric magnification (1.7, 2.0, 2.4). Registration accuracy was evaluated in terms of projection distance error (PDE) between the estimated and true target points in the projection image, including 14 400 random trials (200 trials on the 72 registration scenarios) with initialization error up to ±200 mm and ±10°. The resulting median PDE was better than 0.1 mm in all cases, depending somewhat on the resolution of input CT and fluoroscopy images. The cadaver experiments illustrated the tradeoff between robustness and computation time, yielding a success rate of 99.993% in vertebral labeling (with 'success' defined as PDE textless5 mm) using 1,718 664 ± 96 582 function evaluations computed in 54.0 ± 3.5 s on a mid-range GPU (nVidia, GeForce GTX690). Parameters yielding a faster search (e.g., fewer multi-starts) reduced robustness under conditions of large deformation and poor initialization (99.535% success for the same data registered in 13.1 s), but given good initialization (e.g., ±5 mm, assuming a robust initial run) the same registration could be solved with 99.993% success in 6.3 s. The ability to register CT to fluoroscopy in a manner robust to patient deformation could be valuable in applications such as radiation therapy, interventional radiology, and an assistant to target localization (e.g., vertebral labeling) in image-guided spine surgery.},

keywords = {CBCT, Image Registration, 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}

}

@article{otake2012automaticb,

title = {Automatic localization of vertebral levels in x-ray fluoroscopy using 3D-2D registration: a tool to reduce wrong-site surgery.},

author = {Yoshito Otake and Sebastian Schafer and J. Webster Stayman and Wojciech Zbijewski and Gerhard Kleinszig and Rainer Graumann and A. Jay Khanna and Jeffrey H. Siewerdsen },

url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC3429949},

doi = {10.1088/0031-9155/57/17/5485},

issn = {1361-6560},

year  = {2012},

date = {2012-09-01},

journal = {Physics in medicine and biology},

volume = {57},

number = {17},

pages = {5485--508},

publisher = {IOP Publishing},

abstract = {Surgical targeting of the incorrect vertebral level (wrong-level surgery) is among the more common wrong-site surgical errors, attributed primarily to the lack of uniquely identifiable radiographic landmarks in the mid-thoracic spine. The conventional localization method involves manual counting of vertebral bodies under fluoroscopy, is prone to human error and carries additional time and dose. We propose an image registration and visualization system (referred to as LevelCheck), for decision support in spine surgery by automatically labeling vertebral levels in fluoroscopy using a GPU-accelerated, intensity-based 3D-2D (namely CT-to-fluoroscopy) registration. A gradient information (GI) similarity metric and a CMA-ES optimizer were chosen due to their robustness and inherent suitability for parallelization. Simulation studies involved ten patient CT datasets from which 50 000 simulated fluoroscopic images were generated from C-arm poses selected to approximate the C-arm operator and positioning variability. Physical experiments used an anthropomorphic chest phantom imaged under real fluoroscopy. The registration accuracy was evaluated as the mean projection distance (mPD) between the estimated and true center of vertebral levels. Trials were defined as successful if the estimated position was within the projection of the vertebral body (namely mPD textless5 mm). Simulation studies showed a success rate of 99.998% (1 failure in 50 000 trials) and computation time of 4.7 s on a midrange GPU. Analysis of failure modes identified cases of false local optima in the search space arising from longitudinal periodicity in vertebral structures. Physical experiments demonstrated the robustness of the algorithm against quantum noise and x-ray scatter. The ability to automatically localize target anatomy in fluoroscopy in near-real-time could be valuable in reducing the occurrence of wrong-site surgery while helping to reduce radiation exposure. The method is applicable beyond the specific case of vertebral labeling, since any structure defined in pre-operative (or intra-operative) CT or cone-beam CT can be automatically registered to the fluoroscopic scene.},

keywords = {CBCT, Image Guided Surgery, Image Registration, Multimodality, Spine},

pubstate = {published},

tppubtype = {article}

}

@article{Lee2012,

title = {Volume-of-change cone-beam CT for image-guided surgery.},

author = {Junghoon Lee and J. Webster Stayman and Yoshito Otake and Sebastian Schafer and Wojciech Zbijewski and A. Jay Khanna and Jerry L. Prince and Jeffrey H. Siewerdsen },

url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC3432954},

doi = {10.1088/0031-9155/57/15/4969},

issn = {1361-6560},

year  = {2012},

date = {2012-08-01},

journal = {Physics in medicine and biology},

volume = {57},

number = {15},

pages = {4969--89},

abstract = {C-arm cone-beam CT (CBCT) can provide intraoperative 3D imaging capability for surgical guidance, but workflow and radiation dose are the significant barriers to broad utilization. One main reason is that each 3D image acquisition requires a complete scan with a full radiation dose to present a completely new 3D image every time. In this paper, we propose to utilize patient-specific CT or CBCT as prior knowledge to accurately reconstruct the aspects of the region that have changed by the surgical procedure from only a sparse set of x-rays. The proposed methods consist of a 3D-2D registration between the prior volume and a sparse set of intraoperative x-rays, creating digitally reconstructed radiographs (DRRs) from the registered prior volume, computing difference images by subtracting DRRs from the intraoperative x-rays, a penalized likelihood reconstruction of the volume of change (VOC) from the difference images, and finally a fusion of VOC reconstruction with the prior volume to visualize the entire surgical field. When the surgical changes are local and relatively small, the VOC reconstruction involves only a small volume size and a small number of projections, allowing less computation and lower radiation dose than is needed to reconstruct the entire surgical field. We applied this approach to sacroplasty phantom data obtained from a CBCT test bench and vertebroplasty data with a fresh cadaver acquired from a C-arm CBCT system with a flat-panel detector. The VOCs were reconstructed from a varying number of images (10-66 images) and compared to the CBCT ground truth using four different metrics (mean squared error, correlation coefficient, structural similarity index and perceptual difference model). The results show promising reconstruction quality with structural similarity to the ground truth close to 1 even when only 15-20 images were used, allowing dose reduction by the factor of 10-20.},

keywords = {CBCT, Image Guided Surgery, MBIR, Prior Images, Sparse Sampling, Spine},

pubstate = {published},

tppubtype = {article}

}

@article{schafer2011mobile,

title = {Mobile C-arm cone-beam CT for guidance of spine surgery: image quality, radiation dose, and integration with interventional guidance.},

author = {Sebastian Schafer and Sajendra Nithiananthan and Daniel J. Mirota and Ali Uneri and J. Webster Stayman and Wojciech Zbijewski and Christian Schmidgunst and Gerhard Kleinszig and A. Jay Khanna and Jeffrey H. Siewerdsen },

url = {http://www.ncbi.nlm.nih.gov/pubmed/21928628 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC3161502},

doi = {10.1118/1.3597566},

issn = {0094-2405},

year  = {2011},

date = {2011-08-01},

journal = {Medical physics},

volume = {38},

number = {8},

pages = {4563--74},

publisher = {American Association of Physicists in Medicine},

abstract = {PURPOSE A flat-panel detector based mobile isocentric C-arm for cone-beam CT (CBCT) has been developed to allow intraoperative 3D imaging with sub-millimeter spatial resolution and soft-tissue visibility. Image quality and radiation dose were evaluated in spinal surgery, commonly relying on lower-performance image intensifier based mobile C-arms. Scan protocols were developed for task-specific imaging at minimum dose, in-room exposure was evaluated, and integration of the imaging system with a surgical guidance system was demonstrated in preclinical studies of minimally invasive spine surgery. METHODS Radiation dose was assessed as a function of kilovolt (peak) (80-120 kVp) and milliampere second using thoracic and lumbar spine dosimetry phantoms. In-room radiation exposure was measured throughout the operating room for various CBCT scan protocols. Image quality was assessed using tissue-equivalent inserts in chest and abdomen phantoms to evaluate bone and soft-tissue contrast-to-noise ratio as a function of dose, and task-specific protocols (i.e., visualization of bone or soft-tissues) were defined. Results were applied in preclinical studies using a cadaveric torso simulating minimally invasive, transpedicular surgery. RESULTS Task-specific CBCT protocols identified include: thoracic bone visualization (100 kVp; 60 mAs; 1.8 mGy); lumbar bone visualization (100 kVp; 130 mAs; 3.2 mGy); thoracic soft-tissue visualization (100 kVp; 230 mAs; 4.3 mGy); and lumbar soft-tissue visualization (120 kVp; 460 mAs; 10.6 mGy)--each at (0.3 x 0.3 x 0.9 mm3) voxel size. Alternative lower-dose, lower-resolution soft-tissue visualization protocols were identified (100 kVp; 230 mAs; 5.1 mGy) for the lumbar region at (0.3 x 0.3 x 1.5 mm3) voxel size. Half-scan orbit of the C-arm (x-ray tube traversing under the table) was dosimetrically advantageous (prepatient attenuation) with a nonuniform dose distribution (-2 x higher at the entrance side than at isocenter, and -3-4 lower at the exit side). The in-room dose (microsievert) per unit scan dose (milligray) ranged from -21 microSv/mGy on average at tableside to -0.1 microSv/mGy at 2.0 m distance to isocenter. All protocols involve surgical staff stepping behind a shield wall for each CBCT scan, therefore imparting -zero dose to staff. Protocol implementation in preclinical cadaveric studies demonstrate integration of the C-arm with a navigation system for spine surgery guidance-specifically, minimally invasive vertebroplasty in which the system provided accurate guidance and visualization of needle placement and bone cement distribution. Cumulative dose including multiple intraoperative scans was -11.5 mGy for CBCT-guided thoracic vertebroplasty and -23.2 mGy for lumbar vertebroplasty, with dose to staff at tableside reduced to -1 min of fluoroscopy time (-4(0-60 microSv), compared to 5-11 min for the conventional approach. CONCLUSIONS Intraoperative CBCT using a high-performance mobile C-arm prototype demonstrates image quality suitable to guidance of spine surgery, with task-specific protocols providing an important basis for minimizing radiation dose, while maintaining image quality sufficient for surgical guidance. Images demonstrate a significant advance in spatial resolution and soft-tissue visibility, and CBCT guidance offers the potential to reduce fluoroscopy reliance, reducing cumulative dose to patient and staff. Integration with a surgical guidance system demonstrates precise tracking and visualization in up-to-date images (alleviating reliance on preoperative images only), including detection of errors or suboptimal surgical outcomes in the operating room.},

keywords = {CBCT, Image Guided Surgery, Spine, System Assessment},

pubstate = {published},

tppubtype = {article}

}