@inproceedings{Li2021,

title = {Mitigating unknown biases in CT data using machine learning},

author = {Junyuan Li and Grace Gang and J. Webster Stayman},

url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11595/1159541/Mitigating-unknown-biases-in-CT-data-using-machine-learning/10.1117/12.2582274.full},

doi = {10.1117/12.2582274},

year  = {2021},

date = {2021-02-15},

booktitle = {SPIE Medical Imaging},

volume = {11595},

pages = {1159541},

publisher = {International Society for Optics and Photonics},

keywords = {Artifact Correction, Machine Learning/Deep Learning},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Cao2019,

title = {Quantitative cone-beam CT of bone mineral density using model-based reconstruction},

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

url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10948/109480Y/Quantitative-cone-beam-CT-of-bone-mineral-density-using-model/10.1117/12.2513216.full},

doi = {10.1117/12.2513216},

year  = {2019},

date = {2019-03-01},

booktitle = {Proc. SPIE Medical Imaging},

volume = {10948},

pages = {109480Y-1-6},

keywords = {Artifact Correction, Beam Hardening, CBCT, Extremities, MBIR},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Sisniega2018b,

title = {Multi-Motion Compensation for High-Quality Cone-Beam CT of the Head},

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

year  = {2018},

date = {2018-05-20},

booktitle = {International Conference on Image Formation in X-Ray Computed Tomography},

pages = {73-77},

keywords = {Artifact Correction, Head/Neck, Motion Compensation},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Uneri2018b,

title = {3D-2D known-component registration for metal artifact reduction in cone-beam CT},

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

url = {https://aiai.jhu.edu/papers/CT2018_uneri.pdf},

year  = {2018},

date = {2018-05-20},

booktitle = {International Conference on Image Formation in X-Ray Computed Tomography},

pages = {151-155},

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

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Tilley2018b,

title = {High-Fidelity Modeling of Detector Lag and Gantry Motion in CT Reconstruction},

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

url = {https://aiai.jhu.edu/papers/CT2018_tilley.pdf},

year  = {2018},

date = {2018-05-20},

booktitle = {International Conference on Image Formation in X-Ray Computed Tomography},

pages = {318-322},

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

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Sisniega2017a,

title = {Development and clinical translation of a cone-beam CT scanner for high-quality imaging of intracranial hemorrhage},

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

editor = {Thomas G. Flohr and Joseph Y. Lo and Taly Gilat Schmidt},

url = {http://proceedings.spiedigitallibrary.org/proceeding.aspx?doi=10.1117/12.2255670},

doi = {10.1117/12.2255670},

year  = {2017},

date = {2017-03-01},

booktitle = {SPIE Medical Imaging},

volume = {10132},

pages = {101320K},

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

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Sisniega2016a,

title = {Motion Estimation Using a Penalized Image Sharpness Criterion for Resolution Recovery in Extremities Cone-Beam CT},

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

url = {https://aiai.jhu.edu/papers/CT2016_Sisniega.pdf},

year  = {2016},

date = {2016-01-01},

booktitle = {4th International Conference on Image Formation in X-Ray Computed Tomography},

pages = {549--552},

keywords = {Artifact Correction, CBCT, Extremities, High-Resolution CT, Motion Compensation},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{stayman2014integration,

title = {Integration of Component Knowledge in Penalized-Likelihood Reconstruction with Morphological and Spectral Uncertainties},

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

url = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4211110/},

year  = {2014},

date = {2014-01-01},

booktitle = {Conference proceedings/International Conference on Image Formation in X-Ray Computed Tomography. International Conference on Image Formation in X-Ray Computed Tomography},

volume = {2014},

pages = {111},

organization = {NIH Public Access},

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

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{zbijewski2014sparse,

title = {A Sparse Monte Carlo Method for High-Speed, High-Accuracy Scatter Correction for Soft-Tissue Imaging in Cone-Beam CT},

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

year  = {2014},

date = {2014-01-01},

booktitle = {Proceedings of the International Conference on Image Formation in X-Ray Computed Tomography},

volume = {3},

pages = {401--404},

keywords = {Artifact Correction, CBCT, Scatter Estimation},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{stayman2013overcoming,

title = {Overcoming nonlinear partial volume effects in known-component reconstruction of Cochlear implants},

author = {J. Webster Stayman and Hao Dang and Yoshito Otake and Wojciech Zbijewski and Jack Noble and Benoit Dawant and Robert Labadie and John P. Carey and Jeffrey H. Siewerdsen},

editor = {Robert M. Nishikawa and Bruce R. Whiting },

url = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4060628/},

doi = {10.1117/12.2007945},

year  = {2013},

date = {2013-03-01},

booktitle = {SPIE Medical Imaging},

pages = {86681L},

organization = {International Society for Optics and Photonics},

keywords = {Artifact Correction, Head/Neck, High-Resolution CT, Known Components, MBIR, Metal Artifacts},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{otake2013model,

title = {Model-based cone-beam CT reconstruction for image-guided minimally invasive treatment of hip osteolysis},

author = {Yoshito Otake and J. Webster Stayman and Wojciech Zbijewski and Ryan J. Murphy and Michael D. Kutzer and Russell H. Taylor and Jeffrey H. Siewerdsen and Mehran Armand},

editor = {David R. Holmes III and Ziv R. Yaniv },

url = {http://proceedings.spiedigitallibrary.org/proceeding.aspx?doi=10.1117/12.2008094},

doi = {10.1117/12.2008094},

year  = {2013},

date = {2013-03-01},

booktitle = {SPIE Medical Imaging},

pages = {86710Y},

organization = {International Society for Optics and Photonics},

keywords = {Artifact Correction, Image Guided Surgery, Known Components, MBIR, Metal Artifacts},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Stayman2012,

title = {Model-based reconstruction of objects with inexactly known components},

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

editor = {Norbert J. Pelc and Robert M. Nishikawa and Bruce R. Whiting },

url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4507268},

doi = {10.1117/12.911202},

issn = {0277-786X},

year  = {2012},

date = {2012-02-01},

booktitle = {SPIE Medical Imaging},

volume = {8313},

number = {10},

pages = {83131S},

abstract = {Because tomographic reconstructions are ill-conditioned, algorithms that incorporate additional knowledge about the imaging volume generally have improved image quality. This is particularly true when measurements are noisy or have missing data. This paper presents a general reconstruction framework for including attenuation contributions from objects known to be in the field-of-view. Components such as surgical devices and tools may be modeled explicitly as part of the attenuating volume but are inexactly known with respect to their locations poses, and possible deformations. The proposed reconstruction framework, referred to as Known-Component Reconstruction (KCR), is based on this novel parameterization of the object, a likelihood-based objective function, and alternating optimizations between registration and image parameters to jointly estimate the both the underlying attenuation and unknown registrations. A deformable KCR (dKCR) approach is introduced that adopts a control point-based warping operator to accommodate shape mismatches between the component model and the physical component, thereby allowing for a more general class of inexactly known components. The KCR and dKCR approaches are applied to low-dose cone-beam CT data with spine fixation hardware present in the imaging volume. Such data is particularly challenging due to photon starvation effects in projection data behind the metallic components. The proposed algorithms are compared with traditional filtered-backprojection and penalized-likelihood reconstructions and found to provide substantially improved image quality. Whereas traditional approaches exhibit significant artifacts that complicate detection of breaches or fractures near metal, the KCR framework tends to provide good visualization of anatomy right up to the boundary of surgical devices.},

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

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Zbijewski2012,

title = {CT Reconstruction Using Spectral and Morphological Prior Knowledge: Application to Imaging the Prosthetic Knee},

author = {Wojciech Zbijewski and J. Webster Stayman and Abdullah Al Muhit and John Yorkston and John A. Carrino and Jeffrey H. Siewerdsen },

url = {http://istar.jhu.edu/pdf/Zbijewski_KCR_CTMeeting2012.pdf},

year  = {2012},

date = {2012-01-01},

booktitle = {Second Int. Conf. Image Form. X-ray Computed Tomography},

pages = {434--438},

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

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Jacobson2008,

title = {Compensating for head motion in slowly-rotating cone beam CT systems with optimization transfer based motion estimation},

author = {Matthew W. Jacobson and J. Webster Stayman },

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4774416},

doi = {10.1109/NSSMIC.2008.4774416},

issn = {1082-3654},

year  = {2008},

date = {2008-10-01},

booktitle = {2008 IEEE Nuclear Science Symposium Conference Record},

pages = {5240--5245},

publisher = {IEEE},

abstract = {We present an algorithm that estimates the changing pose of a rigidly moving subject, based on tomographic projections of fiducial markers. The pose estimates can then be used as input data to motion-compensated image reconstruction methods. The proposed algorithm is in the family of so-called optimization transfer algorithms, which have been applied more frequently to tomographic image reconstruction (e.g., the ML-EM algorithm for PET). The algorithm is particularly relevant to the class of slowly-rotating, point-of-care cone beam CT systems, which have recently become prominent in otolaryngological and dental clinics for head and neck scanning. These systems are more compact and inexpensive than traditional CT systems, but because of their slower gantry rotation, are more susceptible to patient head motion over the course of the scan. A virtue of our algorithm is that it does not require a priori knowledge of the marker geometry. The relative positions of the markers are co-estimated together with their 6 degree of freedom position/orientation in each projection view. This means that the marker configuration can be deformably adjusted to fit different patients, giving considerably more flexibility in the design of the marker-to-head attachment gear than with conventional fiducial-based approaches. Our algorithm is also both fast and accurate. In a highly sub-optimal MATLAB implementation, we typically achieve motion estimates yielding sub-millimeter positioning error in 15–30 seconds. We also present motion-compensated reconstructions from real CT acquisitions as evidence of motion-estimation performance.},

keywords = {Artifact Correction, CBCT, Motion Compensation},

pubstate = {published},

tppubtype = {inproceedings}

}