@article{Chen2019,

title = {Precise measurement of coronary stenosis diameter with CCTA using CT number calibration},

author = {Z Chen and Francisco Contijoch and A Schluchter and L Grady and M Schaap and J. Webster Stayman and Jed Pack and Elliot McVeigh},

url = {https://pubmed.ncbi.nlm.nih.gov/31603567/},

doi = {10.1002/mp.13862},

year  = {2019},

date = {2019-12-01},

journal = {Medical Physics},

volume = {46},

number = {12},

pages = {5514-5527},

abstract = {Purpose: Coronary x-ray computed tomography angiography (CCTA) continues to develop as a noninvasive method for the assessment of coronary vessel geometry and the identification of physiologically significant lesions. The uncertainty of quantitative lesion diameter measurement due to limited spatial resolution and vessel motion reduces the accuracy of CCTA diagnoses. In this paper, we introduce a new technique called computed tomography (CT)-number-Calibrated Diameter to improve the accuracy of the vessel and stenosis diameter measurements with CCTA.



Methods: A calibration phantom containing cylindrical holes (diameters spanning from 0.8 mm through 4.0 mm) capturing the range of diameters found in human coronary vessels was three-dimensional printed. We also printed a human stenosis phantom with 17 tubular channels having the geometry of lesions derived from patient data. We acquired CT scans of the two phantoms with seven different imaging protocols. Calibration curves relating vessel intraluminal maximum voxel value (maximum CT number of a voxel, described in Hounsfield Units, HU) to true diameter, and full-width-at-half maximum (FWHM) to true diameter were constructed for each CCTA protocol. In addition, we acquired scans with a small constant motion (15 mm/s) and used a motion correction reconstruction (Snapshot Freeze) algorithm to correct motion artifacts. We applied our technique to measure the lesion diameter in the 17 lesions in the stenosis phantom and compared the performance of CT-number-Calibrated Diameter to the ground truth diameter and a FWHM estimate.



Results: In all cases, vessel intraluminal maximum voxel value vs diameter was found to have a simple functional form based on the two-dimensional point spread function yielding a constant maximum voxel value region above a cutoff diameter, and a decreasing maximum voxel value vs decreasing diameter below a cutoff diameter. After normalization, focal spot size and reconstruction kernel were the principal determinants of cutoff diameter and the rate of maximum voxel value reduction vs decreasing diameter. The small constant motion had a significant effect on the CT number calibration; however, the motion-correction algorithm returned the maximum voxel value vs diameter curve to that of stationary vessels. The CT number Calibration technique showed better performance than FWHM estimation of diameter, yielding a high accuracy in the tested range (0.8 mm through 2.5 mm). We found a strong linear correlation between the smallest diameter in each of 17 lesions measured by CT-number-Calibrated Diameter (DC ) and ground truth diameter (Dgt ), (DC = 0.951 × Dgt + 0.023 mm, r = 0.998 with a slope very close to 1.0 and intercept very close to 0 mm.



Conclusions: Computed tomography-number-Calibrated Diameter is an effective method to enhance the accuracy of the estimate of small vessel diameters and degree of coronary stenosis in CCTA.



Keywords: coronary CT angiography; coronary stenosis quantification; vessel motion.},

keywords = {High-Resolution CT, System Assessment},

pubstate = {published},

tppubtype = {article}

}

@article{Hehn2019,

title = {Blind deconvolution in model-based iterative reconstruction for CT using a normalized sparsity measure},

author = {Lorenz Hehn and Steven Tilley and Franz Pfeiffer and J. Webster Stayman},

url = {https://pubmed.ncbi.nlm.nih.gov/31561247/},

doi = {10.1088/1361-6560/ab489e},

year  = {2019},

date = {2019-10-01},

journal = {Physics in Medicine and Biology},

volume = {64},

number = {21},

pages = {215010},

keywords = {High-Resolution CT, MBIR, Regularization Design},

pubstate = {published},

tppubtype = {article}

}

@article{Wang2019d,

title = {Volume-of-interest Imaging with Dynamic Fluence Modulation Using Multiple Aperture Devices},

author = {Wenying Wang and Grace Gang and Jeffrey H. Siewerdsen and Reuven Levinson and Satomi Kawamoto and J. Webster Stayman},

url = {https://pubmed.ncbi.nlm.nih.gov/31528659/},

doi = {10.1117/1.JMI.6.3.033504},

year  = {2019},

date = {2019-09-01},

journal = {Journal of Medical Imaging},

volume = {6},

number = {3},

pages = {033504},

abstract = {Volume-of-interest (VOI) imaging is a strategy in computed tomography (CT) that restricts x-ray fluence to particular anatomical targets via dynamic beam modulation. This permits dose reduction while retaining image quality within the VOI. VOI-CT implementation has been challenged, in part, by a lack of hardware solutions for tailoring the incident fluence to the patient and anatomical site, as well as difficulties involving interior tomography reconstruction of truncated projection data. We propose a general VOI-CT imaging framework using multiple aperture devices (MADs), an emerging beam filtration scheme based on two binary x-ray filters. Location of the VOI is prescribed using two scout views at anterior-posterior (AP) and lateral perspectives. Based on a calibration of achievable fluence field patterns, MAD motion trajectories were designed using an optimization objective that seeks to maximize the relative fluence in the VOI subject to minimum fluence constraints. A modified penalized-likelihood method is developed for reconstruction of heavily truncated data using the full-field scout views to help solve the interior tomography problem. Physical experiments were conducted to show the feasibility of noncentered and elliptical VOI in two applications-spine and lung imaging. Improved dose utilization and retained image quality are validated with respect to standard full-field protocols. We observe that the contrast-to-noise ratio (CNR) is 40% higher compared with low-dose full-field scans at the same dose. The total dose reduction is 50% for equivalent image quality (CNR) within the VOI.},

keywords = {Customized Acquisition, Dynamic Bowtie, Task-Driven Imaging},

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{Gang2019b,

title = {Dynamic fluence field modulation in computed tomography using multiple aperture devices},

author = {Grace Gang and Andrew Mao and Wenying Wang and Jeffrey H. Siewerdsen and Aswin Mathews and Satomi Kawamoto and Reuven Levinson and J. Webster Stayman},

url = {https://iopscience.iop.org/article/10.1088/1361-6560/ab155e/meta},

doi = {10.1088/1361-6560/ab155e},

year  = {2019},

date = {2019-05-21},

journal = {Physics in Medicine and Biology},

volume = {64},

pages = {105024},

keywords = {Customized Acquisition, Dynamic Bowtie, System Design},

pubstate = {published},

tppubtype = {article}

}

@article{Stayman2019,

title = {Task-driven source–detector trajectories in cone-beam computed tomography: I. Theory and methods},

author = {J. Webster Stayman and Sarah Capostagno and Grace Gang and Jeffrey H. Siewerdsen },

url = {https://www.spiedigitallibrary.org/journals/Journal-of-Medical-Imaging/volume-6/issue-2/025002/Task-driven-sourcedetector-trajectories-in-cone-beam-computed-tomography/10.1117/1.JMI.6.2.025002.full},

doi = {10.1117/1.JMI.6.2.025002},

year  = {2019},

date = {2019-05-02},

journal = {Journal of Medical Imaging},

volume = {6},

number = {2},

pages = {025002},

keywords = {CBCT, Customized Acquisition, Image Guided Surgery, MBIR, Task-Driven Imaging},

pubstate = {published},

tppubtype = {article}

}

@article{Capostagno2019,

title = {Task-driven source–detector trajectories in cone-beam computed tomography: II. Application to neuroradiology},

author = {Sarah Capostagno and J. Webster Stayman and Matthew W. Jacobson and Tina Ehtiati and Clifford Weiss and Jeffrey H. Siewerdsen },

url = {https://www.spiedigitallibrary.org/journals/Journal-of-Medical-Imaging/volume-6/issue-2/025004/Task-driven-sourcedetector-trajectories-in-cone-beam-computed-tomography/10.1117/1.JMI.6.2.025004.full},

doi = {10.1117/1.JMI.6.2.025004},

year  = {2019},

date = {2019-03-09},

journal = {Journal of Medical Imaging},

volume = {6},

number = {2},

pages = {025004},

keywords = {CBCT, Customized Acquisition, Head/Neck, Image Guided Surgery, Task-Driven Imaging},

pubstate = {published},

tppubtype = {article}

}

@article{Tilley2019,

title = {Model-based material decomposition with a penalized nonlinear least-squares CT reconstruction algorithm},

author = {Steven Tilley and Wojciech Zbijewski and J. Webster Stayman },

url = {https://pubmed.ncbi.nlm.nih.gov/30561382/},

doi = {10.1088/1361-6560/aaf973},

year  = {2019},

date = {2019-02-01},

journal = {Physics in Medicine and Biology},

volume = {64},

number = {3},

pages = {035005–1-14},

abstract = {Spectral information in CT may be used for material decomposition to produce accurate reconstructions of material density and to separate materials with similar overall attenuation. Traditional methods separate the reconstruction and decomposition steps, often resulting in undesirable trade-offs (e.g. sampling constraints, a simplified spectral model). In this work, we present a model-based material decomposition algorithm which performs the reconstruction and decomposition simultaneously using a multienergy forward model. In a kV-switching simulation study, the presented method is capable of reconstructing iodine at 0.5 mg ml-1 with a contrast-to-noise ratio greater than two, as compared to 3.0 mg ml-1 for image domain decomposition. The presented method also enables novel acquisition methods, which was demonstrated in this work with a combined kV-switching/split-filter acquisition explored in simulation and physical test bench studies. This novel design used four spectral channels to decompose three materials: water, iodine, and gadolinium. In simulation, the presented method accurately reconstructed concentration value estimates with RMSE values of 4.86 mg ml-1 for water, 0.108 mg ml-1 for iodine and 0.170 mg ml-1 for gadolinium. In test-bench data, the RMSE values were 134 mg ml-1, 5.26 mg ml-1 and 1.85 mg ml-1, respectively. These studies demonstrate the ability of model-based material decomposition to produce accurate concentration estimates in challenging spatial/spectral sampling acquisitions.},

keywords = {High-Fidelity Modeling, MBIR, Spectral X-ray/CT},

pubstate = {published},

tppubtype = {article}

}

@article{Wang2019,

title = {Predicting image properties in penalized‐likelihood reconstructions of flat‐panel CBCT},

author = {Wenying Wang and Grace Gang and Jeffrey H. Siewerdsen and J. Webster Stayman},

url = {https://aapm.onlinelibrary.wiley.com/doi/full/10.1002/mp.13249},

doi = {10.1002/mp.13249},

year  = {2019},

date = {2019-01-01},

journal = {Medical Physics},

volume = {46},

number = {1},

pages = {65-80},

keywords = {Analysis, CBCT, High-Fidelity Modeling, MBIR, Regularization Design},

pubstate = {published},

tppubtype = {article}

}

@article{Wu2018c,

title = {Statistical weights for model-based reconstruction in cone-beam CT with electronic noise and dual-gain detector readout},

author = {Pengwei Wu and J. Webster Stayman and Alejandro Sisniega and Wojciech Zbijewski and David H. Foos and Xiaohui Wang and Nafi Aygun and R. Stevens and Jeffrey H. Siewerdsen},

url = {https://iopscience.iop.org/article/10.1088/1361-6560/aaf0b4/meta},

doi = {10.1088/1361-6560/aaf0b4},

year  = {2018},

date = {2018-12-14},

journal = {Physics in Medicine and Biology},

volume = {63},

number = {24},

pages = {245018},

keywords = {CBCT, High-Fidelity Modeling, MBIR},

pubstate = {published},

tppubtype = {article}

}

@article{Mao2018b,

title = {Dynamic fluence field modulation for miscentered patients in computed tomography},

author = {Andrew Mao and Grace Gang and William Shyr and Reuven Levinson and Jeffrey H. Siewerdsen and Satomi Kawamoto and J. Webster Stayman },

url = {https://www.spiedigitallibrary.org/journals/Journal-of-Medical-Imaging/volume-5/issue-4/043501/Dynamic-fluence-field-modulation-for-miscentered-patients-in-computed-tomography/10.1117/1.JMI.5.4.043501.full?SSO=1},

doi = {10.1117/1.JMI.5.4.043501},

year  = {2018},

date = {2018-10-24},

journal = {Journal of Medical Imaging},

volume = {5},

number = {4},

pages = {043501},

keywords = {Customized Acquisition, Dynamic Bowtie},

pubstate = {published},

tppubtype = {article}

}

@article{Zhang2018,

title = {Regularization Analysis and Design for Prior-Image-Based X-ray CT Reconstruction},

author = {Hao Zhang and Grace Gang and Hao Dang and J. Webster Stayman },

url = {https://ieeexplore.ieee.org/document/8384280/},

doi = {10.1109/TMI.2018.2847250},

year  = {2018},

date = {2018-10-01},

journal = {IEEE Transactions on Medical Imaging},

pages = {Early Access},

keywords = {Analysis, Prior Images, Regularization Design, Sequential CT},

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{Seyyedi2018,

title = {Low-Dose CT Perfusion of the Liver using Reconstruction of Difference},

author = {Saeed Seyyedi and Eleni Liapi and Tobias Lasser and Robert Ivkov and Rajeev Hatwar and J. Webster Stayman },

url = {https://ieeexplore.ieee.org/document/8306928/},

doi = {10.1109/TRPMS.2018.2812360},

year  = {2018},

date = {2018-05-01},

journal = {IEEE Transactions on Radiation and Plasma Medical Sciences},

volume = {2},

number = {3},

pages = {205-214},

keywords = {Prior Images, Sequential CT},

pubstate = {published},

tppubtype = {article}

}

@article{Wu2018,

title = {Reconstruction-of-Difference (RoD) Imaging for Cone-Beam CT Neuro-Angiography},

author = {Pengwei Wu and J. Webster Stayman and Michael Mow and Wojciech Zbijewski and Alejandro Sisniega and Nafi Aygun and R. Stevens and David H. Foos and Xiaohui Wang and Jeffrey H. Siewerdsen },

url = {http://iopscience.iop.org/article/10.1088/1361-6560/aac225},

doi = {10.1088/1361-6560/aac225},

year  = {2018},

date = {2018-05-01},

journal = {Physics in Medicine and Biology},

volume = {63},

number = {11},

pages = {115004-1-16},

keywords = {Head/Neck, Prior Images, Sequential CT},

pubstate = {published},

tppubtype = {article}

}

@article{Tilley2018,

title = {Penalized-likelihood reconstruction with high-fidelity measurement models for high-resolution cone-beam CT imaging},

author = {Steven Tilley and Matthew W. Jacobson and Qian Cao and Michael Brehler and Alejandro Sisniega and Wojciech Zbijewski and J. Webster Stayman },

url = {https://www.ncbi.nlm.nih.gov/pubmed/29621002

https://ieeexplore.ieee.org/document/8125700/},

doi = {10.1109/TMI.2017.2779406},

year  = {2018},

date = {2018-04-01},

journal = {IEEE Transactions on Medical Imaging},

volume = {37},

number = {4},

pages = {988-999},

keywords = {High-Fidelity Modeling, High-Resolution CT},

pubstate = {published},

tppubtype = {article}

}

@article{Sisniega2018,

title = {Image quality, scatter, dose in compact CBCT systems with flat and curved detectors},

author = {Alejandro Sisniega and Wojciech Zbijewski and Pengwei Wu and J. Webster Stayman and Vassilis Koliatsos and Nafi Aygun and R. Stevens and Xiaohui Wang and David H. Foos and Jeffrey H. Siewerdsen },

url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10573/2293872/Image-quality-scatter-and-dose-in-compact-CBCT-systems-with/10.1117/12.2293872.full},

doi = {10.1117/12.2293872},

year  = {2018},

date = {2018-02-15},

journal = {Proc. SPIE Medical Imaging},

volume = {10573},

pages = {1015734E-1-7},

keywords = {Artifact Correction, Head/Neck, Scatter Estimation, System Assessment, System Design},

pubstate = {published},

tppubtype = {article}

}

@article{Wang2018,

title = {Spatial Resolution and Noise Prediction in Flat-Panel Cone-Beam CT Penalized-likelihood Reconstruction},

author = {Wenying Wang and Grace Gang and Jeffrey H. Siewerdsen and J. Webster Stayman },

url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5881953/

https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10573/2294546/Spatial-resolution-and-noise-prediction-in-flat-panel-cone-beam/10.1117/12.2294546.full},

doi = {10.1117/12.2294546},

year  = {2018},

date = {2018-02-15},

journal = {Proc. SPIE Medical Imaging},

volume = {10573},

pages = {10157346-1-6},

keywords = {Analysis, High-Fidelity Modeling, High-Resolution CT, Regularization Design},

pubstate = {published},

tppubtype = {article}

}

@article{Jacobson2018,

title = {A line fiducial method for geometric calibration of cone-beam CT systems with diverse scan trajectories},

author = {Matthew W. Jacobson and Michael Ketcha and Sarah Capostagno and Andrew Martin and Ali Uneri and Joseph Goerres and Tharindu De Silva and Sureerat Reaungamornrat and Runze Han and Amir Manbachi and J. Webster Stayman and Sebastian Vogt and Gerhard Kleinszig and Jeffrey H. Siewerdsen},

url = {http://iopscience.iop.org/article/10.1088/1361-6560/aa9910/meta},

doi = {10.1088/1361-6560/aa9910},

year  = {2018},

date = {2018-01-15},

journal = {Physics in Medicine and Biology},

volume = {63},

number = {2},

pages = {025030},

keywords = {Customized Acquisition, Image Guided Surgery, Task-Driven Imaging},

pubstate = {published},

tppubtype = {article}

}

@article{Cao2018,

title = {Modeling and evaluation of a high-resolution CMOS detector for cone-beam CT of the extremities},

author = {Qian Cao and Alejandro Sisniega and Michael Brehler and J. Webster Stayman and John Yorkston and Jeffrey H. Siewerdsen and Wojciech Zbijewski},

url = {https://aapm.onlinelibrary.wiley.com/doi/full/10.1002/mp.12654},

doi = {10.1002/mp.12654},

year  = {2018},

date = {2018-01-01},

journal = {Medical Physics},

volume = {45},

number = {1},

pages = {114-130},

keywords = {Extremities, High-Resolution CT, System Assessment, System Design},

pubstate = {published},

tppubtype = {article}

}

@article{Ouadah2017b,

title = {Correction of Patient Motion in Cone-Beam CT Using 3D-2D Registration},

author = {Sarah Ouadah and Matthew W. Jacobson and J. Webster Stayman and Tina Ehtiati and Clifford Weiss and J. Webster Stayman },

url = {http://iopscience.iop.org/article/10.1088/1361-6560/aa9254},

doi = {10.1088/1361-6560/aa9254},

year  = {2017},

date = {2017-12-01},

journal = {Physics in Medicine and Biology},

volume = {62},

number = {23},

pages = {8813},

keywords = {Image Guided Surgery, Image Registration, Motion Compensation},

pubstate = {published},

tppubtype = {article}

}

@article{Gang2017b,

title = {Task-driven optimization of fluence field and regularization for model-based iterative reconstruction in computed tomography},

author = {Grace Gang and Jeffrey H. Siewerdsen and J. Webster Stayman },

url = {https://www.ncbi.nlm.nih.gov/pubmed/29035215

https://ieeexplore.ieee.org/document/8068249/},

doi = {10.1109/TMI.2017.2763538},

year  = {2017},

date = {2017-12-01},

journal = {IEEE Transactions on Medical Imaging},

volume = {36},

number = {12},

pages = {2424-35},

keywords = {Customized Acquisition, Dynamic Bowtie, Regularization Design, Task-Driven Imaging},

pubstate = {published},

tppubtype = {article}

}

@article{Dang2017b,

title = {Task-based statistical image reconstruction for high-quality cone-beam CT},

author = {Hao Dang and J. Webster Stayman and Jennifer Xu and Wojciech Zbijewski and Alejandro Sisniega and Michael Mow and Xiaohui Wang and David H. Foos and Nafi Aygun and Vassilis Koliatsos and Jeffrey H. Siewerdsen},

url = {http://iopscience.iop.org/article/10.1088/1361-6560/aa90fd},

doi = {10.1088/1361-6560/aa90fd},

year  = {2017},

date = {2017-11-01},

journal = {Physics in Medicine and Biology},

volume = {62},

number = {22},

pages = {8693-8719},

keywords = {Head/Neck, Task-Driven Imaging},

pubstate = {published},

tppubtype = {article}

}

@article{Contijoch2017,

title = {The impact of small motion on the visualization of coronary vessels and lesions in cardiac CT: A simulation study},

author = {Francisco Contijoch and J. Webster Stayman and Elliot McVeigh},

url = {http://doi.wiley.com/10.1002/mp.12295},

doi = {10.1002/mp.12295},

year  = {2017},

date = {2017-05-26},

journal = {Medical Physics},

keywords = {Analysis, Motion Compensation},

pubstate = {published},

tppubtype = {article}

}

@article{Gang2017b,

title = {Task-driven optimization of CT tube current modulation and regularization in model-based iterative reconstruction},

author = {Grace Gang and Jeffrey H. Siewerdsen and J. Webster Stayman},

url = {https://www.ncbi.nlm.nih.gov/pubmed/28362638

http://stacks.iop.org/0031-9155/62/i=12/a=4777?key=crossref.b9662a9cb01ba6fc16efadc35de2e4b1},

doi = {10.1088/1361-6560/aa6a97},

issn = {0031-9155},

year  = {2017},

date = {2017-05-18},

journal = {Physics in Medicine and Biology},

volume = {62},

number = {12},

pages = {4777-4797},

keywords = {Customized Acquisition, Regularization Design, Task-Driven Imaging},

pubstate = {published},

tppubtype = {article}

}

@article{Sisniega2017,

title = {Motion compensation in extremity cone-beam CT using a penalized image sharpness criterion},

author = {Alejandro Sisniega and J. Webster Stayman and John Yorkston and Jeffrey H. Siewerdsen and Wojciech Zbijewski},

url = {http://stacks.iop.org/0031-9155/62/i=9/a=3712?key=crossref.84caaff65ba9b2a3f6da8ee49365aca7},

doi = {10.1088/1361-6560/aa6869},

issn = {0031-9155},

year  = {2017},

date = {2017-04-06},

journal = {Physics in Medicine and Biology},

volume = {62},

number = {9},

pages = {3712-3734},

keywords = {Extremities, Motion Compensation},

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{Dang2017,

title = {Multi-resolution statistical image reconstruction for mitigation of truncation effects: application to cone-beam CT of the head},

author = {Hao Dang and J. Webster Stayman and Alejandro Sisniega and Wojciech Zbijewski and Jennifer Xu and Xiaohui Wang and David H. Foos and Nafi Aygun and Vassilis Koliatsos and Jeffrey H. Siewerdsen},

url = {http://stacks.iop.org/0031-9155/62/i=2/a=539?key=crossref.8d03553e9159a88e56933c7faab62387},

doi = {10.1088/1361-6560/aa52b8},

issn = {0031-9155},

year  = {2017},

date = {2017-01-01},

journal = {Physics in Medicine and Biology},

volume = {62},

number = {2},

pages = {539-559},

keywords = {Artifact Correction, Fast Algorithms, Head/Neck},

pubstate = {published},

tppubtype = {article}

}

@article{Cao2016,

title = {Multiresolution iterative reconstruction in high-resolution extremity cone-beam CT},

author = {Qian Cao and Wojciech Zbijewski and Alejandro Sisniega and John Yorkston and Jeffrey H. Siewerdsen and J. Webster Stayman},

url = {http://stacks.iop.org/0031-9155/61/i=20/a=7263?key=crossref.3714aec7223f83ec2e527f7be1ae5e4f},

doi = {10.1088/0031-9155/61/20/7263},

issn = {0031-9155},

year  = {2016},

date = {2016-10-03},

journal = {Physics in Medicine and Biology},

volume = {61},

number = {20},

pages = {7263-7281},

keywords = {Artifact Correction, Extremities, Fast Algorithms, High-Resolution CT},

pubstate = {published},

tppubtype = {article}

}

@article{Xu2016b,

title = {Technical assessment of a prototype cone-beam CT system for imaging of acute intracranial hemorrhage},

author = {Jennifer Xu and Alejandro Sisniega and Wojciech Zbijewski and Hao Dang and J. Webster Stayman and Michael Mow and Xiaohui Wang and David H. Foos and Vassilis Koliatsos and Nafi Aygun and Jeffrey H. Siewerdsen},

url = {http://doi.wiley.com/10.1118/1.4963220},

doi = {10.1118/1.4963220},

issn = {00942405},

year  = {2016},

date = {2016-09-29},

journal = {Medical Physics},

volume = {43},

number = {10},

pages = {5745-5757},

keywords = {Head/Neck, System Assessment, System Design},

pubstate = {published},

tppubtype = {article}

}

@article{xu2016evaluation,

title = {Evaluation of detector readout gain mode and bowtie filters for cone-beam CT imaging of the head.},

author = {Jennifer Xu and Alejandro Sisniega and Wojciech Zbijewski and Hao Dang and J. Webster Stayman and Xiaohui Wang and David H. Foos and Nafi Aygun and Vassilis Koliatsos and Jeffrey H. Siewerdsen},

url = {http://www.ncbi.nlm.nih.gov/pubmed/27435162},

doi = {10.1088/0031-9155/61/16/5973},

issn = {1361-6560},

year  = {2016},

date = {2016-08-01},

journal = {Physics in medicine and biology},

volume = {61},

number = {16},

pages = {5973--92},

publisher = {IOP Publishing},

abstract = {The effects of detector readout gain mode and bowtie filters on cone-beam CT (CBCT) image quality and dose were characterized for a new CBCT system developed for point-of-care imaging of the head, with potential application to diagnosis of traumatic brain injury, intracranial hemorrhage (ICH), and stroke. A detector performance model was extended to include the effects of detector readout gain on electronic digitization noise. The noise performance for high-gain (HG), low-gain (LG), and dual-gain (DG) detector readout was evaluated, and the benefit associated with HG mode in regions free from detector saturation was quantified. Such benefit could be realized (without detector saturation) either via DG mode or by incorporation of a bowtie filter. Therefore, three bowtie filters were investigated that varied in thickness and curvature. A polyenergetic gain correction method was developed to equalize the detector response between the flood-field and projection data in the presence of a bowtie. The effect of bowtie filters on dose, scatter-to-primary ratio, contrast, and noise was quantified in phantom studies, and results were compared to a high-speed Monte Carlo (MC) simulation to characterize x-ray scatter and dose distributions in the head. Imaging in DG mode improved the contrast-to-noise ratio (CNR) by ~15% compared to LG mode at a dose (D 0, measured at the center of a 16 cm CTDI phantom) of 19 mGy. MC dose calculations agreed with CTDI measurements and showed that bowtie filters reduce peripheral dose by as much as 50% at the same central dose. Bowtie filters were found to increase the CNR per unit square-root dose near the center of the image by ~5-20% depending on bowtie thickness, but reduced CNR in the periphery by ~10-40%. Images acquired at equal CTDIw with and without a bowtie demonstrated a 24% increase in CNR at the center of an anthropomorphic head phantom. Combining a thick bowtie filter with a short arc (180° + fan angle) scan centered on the posterior of the head reduced dose to the eye lens by up to 90%. Acquisition in DG mode (without a bowtie filter) was beneficial to the detection of small, low contrast lesions (e.g. subtle ICH) in CBCT. While bowtie filters were found to reduce dose, mitigate sensor saturation at the periphery in HG mode, and improve CNR at the center of the image, the image quality at the periphery was slightly reduced compared to DG mode, and the use of a bowtie required careful implementation of the polyenergetic flood-field correction to avoid artifacts.},

keywords = {CBCT, Head/Neck, System Assessment, System Design},

pubstate = {published},

tppubtype = {article}

}

@article{Ouadah2016,

title = {Self-calibration of cone-beam CT geometry using 3D-2D image registration.},

author = {Sarah Ouadah and J. Webster Stayman and Grace Gang and Tina Ehtiati and Jeffrey H. Siewerdsen },

url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4948745},

doi = {10.1088/0031-9155/61/7/2613},

issn = {1361-6560},

year  = {2016},

date = {2016-04-01},

journal = {Physics in medicine and biology},

volume = {61},

number = {7},

pages = {2613--32},

abstract = {Robotic C-arms are capable of complex orbits that can increase field of view, reduce artifacts, improve image quality, and/or reduce dose; however, it can be challenging to obtain accurate, reproducible geometric calibration required for image reconstruction for such complex orbits. This work presents a method for geometric calibration for an arbitrary source-detector orbit by registering 2D projection data to a previously acquired 3D image. It also yields a method by which calibration of simple circular orbits can be improved. The registration uses a normalized gradient information similarity metric and the covariance matrix adaptation-evolution strategy optimizer for robustness against local minima and changes in image content. The resulting transformation provides a 'self-calibration' of system geometry. The algorithm was tested in phantom studies using both a cone-beam CT (CBCT) test-bench and a robotic C-arm (Artis Zeego, Siemens Healthcare) for circular and non-circular orbits. Self-calibration performance was evaluated in terms of the full-width at half-maximum (FWHM) of the point spread function in CBCT reconstructions, the reprojection error (RPE) of steel ball bearings placed on each phantom, and the overall quality and presence of artifacts in CBCT images. In all cases, self-calibration improved the FWHM-e.g. on the CBCT bench},

keywords = {CBCT, Customized Acquisition, Geometric Calibration, Image Registration},

pubstate = {published},

tppubtype = {article}

}

@article{xu2016modeling,

title = {Modeling and design of a cone-beam CT head scanner using task-based imaging performance optimization.},

author = {Jennifer Xu and Alejandro Sisniega and Wojciech Zbijewski and Hao Dang and J. Webster Stayman and Xiaohui Wang and David H. Foos and Nafi Aygun and Vassilis Koliatsos and Jeffrey H. Siewerdsen},

url = {http://www.ncbi.nlm.nih.gov/pubmed/27025783},

doi = {10.1088/0031-9155/61/8/3180},

issn = {1361-6560},

year  = {2016},

date = {2016-04-01},

journal = {Physics in medicine and biology},

volume = {61},

number = {8},

pages = {3180--207},

publisher = {IOP Publishing},

abstract = {Detection of acute intracranial hemorrhage (ICH) is important for diagnosis and treatment of traumatic brain injury, stroke, postoperative bleeding, and other head and neck injuries. This paper details the design and development of a cone-beam CT (CBCT) system developed specifically for the detection of low-contrast ICH in a form suitable for application at the point of care. Recognizing such a low-contrast imaging task to be a major challenge in CBCT, the system design began with a rigorous analysis of task-based detectability including critical aspects of system geometry, hardware configuration, and artifact correction. The imaging performance model described the three-dimensional (3D) noise-equivalent quanta using a cascaded systems model that included the effects of scatter, scatter correction, hardware considerations of complementary metal-oxide semiconductor (CMOS) and flat-panel detectors (FPDs), and digitization bit depth. The performance was analyzed with respect to a low-contrast (40-80 HU), medium-frequency task representing acute ICH detection. The task-based detectability index was computed using a non-prewhitening observer model. The optimization was performed with respect to four major design considerations: (1) system geometry (including source-to-detector distance (SDD) and source-to-axis distance (SAD)); (2) factors related to the x-ray source (including focal spot size, kVp, dose, and tube power); (3) scatter correction and selection of an antiscatter grid; and (4) x-ray detector configuration (including pixel size, additive electronics noise, field of view (FOV), and frame rate, including both CMOS and a-Si:H FPDs). Optimal design choices were also considered with respect to practical constraints and available hardware components. The model was verified in comparison to measurements on a CBCT imaging bench as a function of the numerous design parameters mentioned above. An extended geometry (SAD = 750 mm},

keywords = {CBCT, Head/Neck, System Assessment, System Design},

pubstate = {published},

tppubtype = {article}

}

@article{pourmorteza2016reconstruction,

title = {Reconstruction of difference in sequential CT studies using penalized likelihood estimation.},

author = {Amir Pourmorteza and Hao Dang and Jeffrey H. Siewerdsen and J. Webster Stayman },

url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4948746},

doi = {10.1088/0031-9155/61/5/1986},

issn = {1361-6560},

year  = {2016},

date = {2016-03-01},

journal = {Physics in medicine and biology},

volume = {61},

number = {5},

pages = {1986--2002},

publisher = {IOP Publishing},

abstract = {Characterization of anatomical change and other differences is important in sequential computed tomography (CT) imaging, where a high-fidelity patient-specific prior image is typically present, but is not used, in the reconstruction of subsequent anatomical states. Here, we introduce a penalized likelihood (PL) method called reconstruction of difference (RoD) to directly reconstruct a difference image volume using both the current projection data and the (unregistered) prior image integrated into the forward model for the measurement data. The algorithm utilizes an alternating minimization to find both the registration and reconstruction estimates. This formulation allows direct control over the image properties of the difference image, permitting regularization strategies that inhibit noise and structural differences due to inconsistencies between the prior image and the current data. Additionally, if the change is known to be local, RoD allows local acquisition and reconstruction, as opposed to traditional model-based approaches that require a full support field of view (or other modifications). We compared the performance of RoD to a standard PL algorithm, in simulation studies and using test-bench cone-beam CT data. The performances of local and global RoD approaches were similar, with local RoD providing a significant computational speedup. In comparison across a range of data with differing fidelity, the local RoD approach consistently showed lower error (with respect to a truth image) than PL in both noisy data and sparsely sampled projection scenarios. In a study of the prior image registration performance of RoD, a clinically reasonable capture ranges were demonstrated. Lastly, the registration algorithm had a broad capture range and the error for reconstruction of CT data was 35% and 20% less than filtered back-projection for RoD and PL, respectively. The RoD has potential for delivering high-quality difference images in a range of sequential clinical scenarios including image-guided surgeries and treatments where accurate and quantitative assessments of anatomical change is desired.},

keywords = {MBIR, Prior Images, Sequential CT, Sparse Sampling},

pubstate = {published},

tppubtype = {article}

}

@article{sisniega2015volumetric,

title = {Volumetric CT with sparse detector arrays (and application to Si-strip photon counters).},

author = {Alejandro Sisniega and Wojciech Zbijewski and J. Webster Stayman and Jennifer Xu and Katsuyuki Taguchi and Erik Fredenberg and Mats Lundqvist and Jeffrey H. Siewerdsen },

url = {http://www.ncbi.nlm.nih.gov/pubmed/26611740},

doi = {10.1088/0031-9155/61/1/90},

issn = {1361-6560},

year  = {2016},

date = {2016-01-01},

journal = {Physics in medicine and biology},

volume = {61},

number = {1},

pages = {90--113},

publisher = {IOP Publishing},

abstract = {Novel x-ray medical imaging sensors, such as photon counting detectors (PCDs) and large area CCD and CMOS cameras can involve irregular and/or sparse sampling of the detector plane. Application of such detectors to CT involves undersampling that is markedly different from the commonly considered case of sparse angular sampling. This work investigates volumetric sampling in CT systems incorporating sparsely sampled detectors with axial and helical scan orbits and evaluates performance of model-based image reconstruction (MBIR) with spatially varying regularization in mitigating artifacts due to sparse detector sampling. Volumetric metrics of sampling density and uniformity were introduced. Penalized-likelihood MBIR with a spatially varying penalty that homogenized resolution by accounting for variations in local sampling density (i.e. detector gaps) was evaluated. The proposed methodology was tested in simulations and on an imaging bench based on a Si-strip PCD (total area 5 cm × 25 cm) consisting of an arrangement of line sensors separated by gaps of up to 2.5 mm. The bench was equipped with translation/rotation stages allowing a variety of scanning trajectories, ranging from a simple axial acquisition to helical scans with variable pitch. Statistical (spherical clutter) and anthropomorphic (hand) phantoms were considered. Image quality was compared to that obtained with a conventional uniform penalty in terms of structural similarity index (SSIM), image uniformity, spatial resolution, contrast, and noise. Scan trajectories with intermediate helical width (~10 mm longitudinal distance per 360° rotation) demonstrated optimal tradeoff between the average sampling density and the homogeneity of sampling throughout the volume. For a scan trajectory with 10.8 mm helical width, the spatially varying penalty resulted in significant visual reduction of sampling artifacts, confirmed by a 10% reduction in minimum SSIM (from 0.88 to 0.8) and a 40% reduction in the dispersion of SSIM in the volume compared to the constant penalty (both penalties applied at optimal regularization strength). Images of the spherical clutter and wrist phantoms confirmed the advantages of the spatially varying penalty, showing a 25% improvement in image uniformity and 1.8 × higher CNR (at matched spatial resolution) compared to the constant penalty. The studies elucidate the relationship between sampling in the detector plane, acquisition orbit, sampling of the reconstructed volume, and the resulting image quality. They also demonstrate the benefit of spatially varying regularization in MBIR for scenarios with irregular sampling patterns. Such findings are important and integral to the incorporation of a sparsely sampled Si-strip PCD in CT imaging.},

keywords = {MBIR, Photon Counting, Sparse Sampling, System Design},

pubstate = {published},

tppubtype = {article}

}

@article{tilley2015model,

title = {Model-based iterative reconstruction for flat-panel cone-beam CT with focal spot blur, detector blur, and correlated noise.},

author = {Steven Tilley and Jeffrey H. Siewerdsen and J. Webster Stayman },

url = {http://www.ncbi.nlm.nih.gov/pubmed/26649783},

doi = {10.1088/0031-9155/61/1/296},

issn = {1361-6560},

year  = {2016},

date = {2016-01-01},

journal = {Physics in medicine and biology},

volume = {61},

number = {1},

pages = {296--319},

publisher = {IOP Publishing},

abstract = {While model-based reconstruction methods have been successfully applied to flat-panel cone-beam CT (FP-CBCT) systems, typical implementations ignore both spatial correlations in the projection data as well as system blurs due to the detector and focal spot in the x-ray source. In this work, we develop a forward model for flat-panel-based systems that includes blur and noise correlation associated with finite focal spot size and an indirect detector (e.g. scintillator). This forward model is used to develop a staged reconstruction framework where projection data are deconvolved and log-transformed, followed by a generalized least-squares reconstruction that utilizes a non-diagonal statistical weighting to account for the correlation that arises from the acquisition and data processing chain. We investigate the performance of this novel reconstruction approach in both simulated data and in CBCT test-bench data. In comparison to traditional filtered backprojection and model-based methods that ignore noise correlation, the proposed approach yields a superior noise-resolution tradeoff. For example, for a system with 0.34 mm FWHM scintillator blur and 0.70 FWHM focal spot blur, using the correlated noise model instead of an uncorrelated noise model increased resolution by 42% (with variance matched at 6.9 × 10(-8) mm(-2)). While this advantage holds across a wide range of systems with differing blur characteristics, the improvements are greatest for systems where source blur is larger than detector blur.},

note = {Errata: [1] The fidelity terms in equations 10 and 12 are missing a multiplication by 0.5. [2] Equation 14 should be mu(x_j) = a + b erf (2 sqrt( log(2) (x_j-d) / FWHM ). [3] In section 3.2 a reference to Figure 10(e) should be 9(f).},

keywords = {CBCT, High-Fidelity Modeling, High-Resolution CT, MBIR},

pubstate = {published},

tppubtype = {article}

}

@article{Dang2015,

title = {Prospective regularization design in prior-image-based reconstruction.},

author = {Hao Dang and Jeffrey H. Siewerdsen and J. Webster Stayman },

url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4833649},

doi = {10.1088/0031-9155/60/24/9515},

issn = {1361-6560},

year  = {2015},

date = {2015-12-01},

journal = {Physics in medicine and biology},

volume = {60},

number = {24},

pages = {9515--36},

publisher = {IOP Publishing},

abstract = {Prior-image-based reconstruction (PIBR) methods leveraging patient-specific anatomical information from previous imaging studies and/or sequences have demonstrated dramatic improvements in dose utilization and image quality for low-fidelity data. However, a proper balance of information from the prior images and information from the measurements is required (e.g. through careful tuning of regularization parameters). Inappropriate selection of reconstruction parameters can lead to detrimental effects including false structures and failure to improve image quality. Traditional methods based on heuristics are subject to error and sub-optimal solutions, while exhaustive searches require a large number of computationally intensive image reconstructions. In this work, we propose a novel method that prospectively estimates the optimal amount of prior image information for accurate admission of specific anatomical changes in PIBR without performing full image reconstructions. This method leverages an analytical approximation to the implicitly defined PIBR estimator, and introduces a predictive performance metric leveraging this analytical form and knowledge of a particular presumed anatomical change whose accurate reconstruction is sought. Additionally, since model-based PIBR approaches tend to be space-variant, a spatially varying prior image strength map is proposed to optimally admit changes everywhere in the image (eliminating the need to know change locations a priori). Studies were conducted in both an ellipse phantom and a realistic thorax phantom emulating a lung nodule surveillance scenario. The proposed method demonstrated accurate estimation of the optimal prior image strength while achieving a substantial computational speedup (about a factor of 20) compared to traditional exhaustive search. Moreover, the use of the proposed prior strength map in PIBR demonstrated accurate reconstruction of anatomical changes without foreknowledge of change locations in phantoms where the optimal parameters vary spatially by an order of magnitude or more. In a series of studies designed to explore potential unknowns associated with accurate PIBR, optimal prior image strength was found to vary with attenuation differences associated with anatomical change but exhibited only small variations as a function of the shape and size of the change. The results suggest that, given a target change attenuation, prospective patient-, change-, and data-specific customization of the prior image strength can be performed to ensure reliable reconstruction of specific anatomical changes.},

keywords = {MBIR, Prior Images, Regularization Design, Sparse Sampling},

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{dang2015statistical,

title = {Statistical reconstruction for cone-beam CT with a post-artifact-correction noise model: application to high-quality head imaging.},

author = {Hao Dang and J. Webster Stayman and Alejandro Sisniega and Jennifer Xu and Wojciech Zbijewski and Xiaohui Wang and David H. Foos and Nafi Aygun and Vassilis Koliatsos and Jeffrey H. Siewerdsen },

url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4545529},

doi = {10.1088/0031-9155/60/16/6153},

issn = {1361-6560},

year  = {2015},

date = {2015-08-01},

journal = {Physics in medicine and biology},

volume = {60},

number = {16},

pages = {6153--75},

publisher = {IOP Publishing},

abstract = {Non-contrast CT reliably detects fresh blood in the brain and is the current front-line imaging modality for intracranial hemorrhage such as that occurring in acute traumatic brain injury (contrast ~40-80 HU, size textgreater 1 mm). We are developing flat-panel detector (FPD) cone-beam CT (CBCT) to facilitate such diagnosis in a low-cost, mobile platform suitable for point-of-care deployment. Such a system may offer benefits in the ICU, urgent care/concussion clinic, ambulance, and sports and military theatres. However, current FPD-CBCT systems face significant challenges that confound low-contrast, soft-tissue imaging. Artifact correction can overcome major sources of bias in FPD-CBCT but imparts noise amplification in filtered backprojection (FBP). Model-based reconstruction improves soft-tissue image quality compared to FBP by leveraging a high-fidelity forward model and image regularization. In this work, we develop a novel penalized weighted least-squares (PWLS) image reconstruction method with a noise model that includes accurate modeling of the noise characteristics associated with the two dominant artifact corrections (scatter and beam-hardening) in CBCT and utilizes modified weights to compensate for noise amplification imparted by each correction. Experiments included real data acquired on a FPD-CBCT test-bench and an anthropomorphic head phantom emulating intra-parenchymal hemorrhage. The proposed PWLS method demonstrated superior noise-resolution tradeoffs in comparison to FBP and PWLS with conventional weights (viz. at matched 0.50 mm spatial resolution},

keywords = {Artifact Correction, CBCT, Head/Neck, High-Fidelity Modeling, MBIR},

pubstate = {published},

tppubtype = {article}

}

@article{Demehri2015,

title = {Assessment of image quality in soft tissue and bone visualization tasks for a dedicated extremity cone-beam CT system.},

author = {Shadpour Demehri and Abdullah Al Muhit and Wojciech Zbijewski and J. Webster Stayman and John Yorkston and Nathan Packard and Robert Senn and Dong Yang and David H. Foos and Gaurav K. Thawait and L. M. Fayad and A. Chabra and John A. Carrino and Jeffrey H. Siewerdsen},

url = {http://www.ncbi.nlm.nih.gov/pubmed/25599933},

doi = {10.1007/s00330-014-3546-6},

issn = {1432-1084},

year  = {2015},

date = {2015-06-01},

journal = {European radiology},

volume = {25},

number = {6},

pages = {1742--51},

abstract = {OBJECTIVE To assess visualization tasks using cone-beam CT (CBCT) compared to multi-detector CT (MDCT) for musculoskeletal extremity imaging. METHODS Ten cadaveric hands and ten knees were examined using a dedicated CBCT prototype and a clinical multi-detector CT using nominal protocols (80 kVp-108mAs for CBCT; 120 kVp- 300 mAs for MDCT). Soft tissue and bone visualization tasks were assessed by four radiologists using five-point satisfaction (for CBCT and MDCT individually) and five-point preference (side-by-side CBCT versus MDCT image quality comparison) rating tests. Ratings were analyzed using Kruskal-Wallis and Wilcoxon signed-rank tests, and observer agreement was assessed using the Kappa-statistic. RESULTS Knee CBCT images were rated "excellent" or "good" (median scores 5 and 4) for "bone" and "soft tissue" visualization tasks. Hand CBCT images were rated "excellent" or "adequate" (median scores 5 and 3) for "bone" and "soft tissue" visualization tasks. Preference tests rated CBCT equivalent or superior to MDCT for bone visualization and favoured the MDCT for soft tissue visualization tasks. Intraobserver agreement for CBCT satisfaction tests was fair to almost perfect ($kappa$ ~ 0.26-0.92), and interobserver agreement was fair to moderate ($kappa$ ~ 0.27-0.54). CONCLUSION CBCT provided excellent image quality for bone visualization and adequate image quality for soft tissue visualization tasks. KEY POINTS • CBCT provided adequate image quality for diagnostic tasks in extremity imaging. • CBCT images were "excellent" for "bone" and "good/adequate" for "soft tissue" visualization tasks. • CBCT image quality was equivalent/superior to MDCT for bone visualization tasks.},

keywords = {CBCT, Extremities, System Assessment},

pubstate = {published},

tppubtype = {article}

}