@article{Wu2020,

title = {Cone-beam CT for imaging of the head/brain: Development and assessment of scanner prototype and reconstruction algorithms },

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

url = {https://pubmed.ncbi.nlm.nih.gov/32145076/},

doi = {10.1002/mp.14124},

year  = {2020},

date = {2020-06-01},

journal = {Medical Physics},

volume = {47},

number = {6},

pages = {2392-2407},

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

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{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{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{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{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{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{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{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{Sisniega2015,

title = {High-fidelity artifact correction for cone-beam CT imaging of the brain.},

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

url = {http://www.ncbi.nlm.nih.gov/pubmed/25611041},

doi = {10.1088/0031-9155/60/4/1415},

issn = {1361-6560},

year  = {2015},

date = {2015-02-01},

journal = {Physics in medicine and biology},

volume = {60},

number = {4},

pages = {1415--39},

abstract = {CT is the frontline imaging modality for diagnosis of acute traumatic brain injury (TBI), involving the detection of fresh blood in the brain (contrast of 30-50 HU, detail size down to 1 mm) in a non-contrast-enhanced exam. A dedicated point-of-care imaging system based on cone-beam CT (CBCT) could benefit early detection of TBI and improve direction to appropriate therapy. However, flat-panel detector (FPD) CBCT is challenged by artifacts that degrade contrast resolution and limit application in soft-tissue imaging. We present and evaluate a fairly comprehensive framework for artifact correction to enable soft-tissue brain imaging with FPD CBCT. The framework includes a fast Monte Carlo (MC)-based scatter estimation method complemented by corrections for detector lag, veiling glare, and beam hardening.The fast MC scatter estimation combines GPU acceleration, variance reduction, and simulation with a low number of photon histories and reduced number of projection angles (sparse MC) augmented by kernel de-noising to yield a runtime of ~4 min per scan. Scatter correction is combined with two-pass beam hardening correction. Detector lag correction is based on temporal deconvolution of the measured lag response function. The effects of detector veiling glare are reduced by deconvolution of the glare response function representing the long range tails of the detector point-spread function. The performance of the correction framework is quantified in experiments using a realistic head phantom on a testbench for FPD CBCT.Uncorrected reconstructions were non-diagnostic for soft-tissue imaging tasks in the brain. After processing with the artifact correction framework, image uniformity was substantially improved, and artifacts were reduced to a level that enabled visualization of ~3 mm simulated bleeds throughout the brain. Non-uniformity (cupping) was reduced by a factor of 5, and contrast of simulated bleeds was improved from ~7 to 49.7 HU, in good agreement with the nominal blood contrast of 50 HU. Although noise was amplified by the corrections, the contrast-to-noise ratio (CNR) of simulated bleeds was improved by nearly a factor of 3.5 (CNR = 0.54 without corrections and 1.91 after correction). The resulting image quality motivates further development and translation of the FPD-CBCT system for imaging of acute TBI.},

keywords = {Artifact Correction, Beam Hardening, CBCT, Head/Neck, Scatter Estimation},

pubstate = {published},

tppubtype = {article}

}

@article{wang2014soft,

title = {Soft-tissue imaging with C-arm cone-beam CT using statistical reconstruction.},

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

url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4046706},

doi = {10.1088/0031-9155/59/4/1005},

issn = {1361-6560},

year  = {2014},

date = {2014-02-01},

journal = {Physics in medicine and biology},

volume = {59},

number = {4},

pages = {1005--26},

publisher = {IOP Publishing},

abstract = {The potential for statistical image reconstruction methods such as penalized-likelihood (PL) to improve C-arm cone-beam CT (CBCT) soft-tissue visualization for intraoperative imaging over conventional filtered backprojection (FBP) is assessed in this work by making a fair comparison in relation to soft-tissue performance. A prototype mobile C-arm was used to scan anthropomorphic head and abdomen phantoms as well as a cadaveric torso at doses substantially lower than typical values in diagnostic CT, and the effects of dose reduction via tube current reduction and sparse sampling were also compared. Matched spatial resolution between PL and FBP was determined by the edge spread function of low-contrast (∼ 40-80 HU) spheres in the phantoms, which were representative of soft-tissue imaging tasks. PL using the non-quadratic Huber penalty was found to substantially reduce noise relative to FBP, especially at lower spatial resolution where PL provides a contrast-to-noise ratio increase up to 1.4-2.2 × over FBP at 50% dose reduction across all objects. Comparison of sampling strategies indicates that soft-tissue imaging benefits from fully sampled acquisitions at dose above ∼ 1.7 mGy and benefits from 50% sparsity at dose below ∼ 1.0 mGy. Therefore, an appropriate sampling strategy along with the improved low-contrast visualization offered by statistical reconstruction demonstrates the potential for extending intraoperative C-arm CBCT to applications in soft-tissue interventions in neurosurgery as well as thoracic and abdominal surgeries by overcoming conventional tradeoffs in noise, spatial resolution, and dose.},

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

pubstate = {published},

tppubtype = {article}

}

@article{lee2012intraoperative,

title = {Intraoperative C-arm cone-beam computed tomography: quantitative analysis of surgical performance in skull base surgery.},

author = {Stella Lee and Gary L. Gallia and Douglas D. Reh and Sebastian Schafer and Ali Uneri and Daniel J. Mirota and Sajendra Nithiananthan and Yoshito Otake and J. Webster Stayman and Wojciech Zbijewski and Jeffrey H. Siewerdsen },

url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC3725328},

doi = {10.1002/lary.23374},

issn = {1531-4995},

year  = {2012},

date = {2012-09-01},

journal = {The Laryngoscope},

volume = {122},

number = {9},

pages = {1925--32},

publisher = {Wiley Subscription Services, Inc., A Wiley Company},

abstract = {OBJECTIVES/HYPOTHESIS To determine whether incorporation of intraoperative imaging via a new cone-beam computed tomography (CBCT) image-guidance system improves accuracy and facilitates resection in sinus and skull-base surgery through quantification of surgical performance. STUDY DESIGN Landmark identification and skull base ablation tasks were performed with a CBCT intraoperative image-guidance system in the experimental group and with image-guided surgery (IGS) alone based on preoperative computed tomography (CT) in the control group. METHODS Six cadaveric heads underwent preoperative CT imaging and surgical planning identifying surgical targets. Three types of surgical tasks were planned: landmark point identification, line contour identification, and volume drill-out. Key anatomic structures (carotid artery and optic nerve) were chosen for landmark identification and line contour tasks. Complete ethmoidectomy, vidian corridor drill-out, and clival resection were performed for volume ablation tasks. The CBCT guidance system was used in the experimental group and performance was assessed by metrics of target registration error, sensitivity, and specificity of excision. RESULTS Significant improvements were seen for point identification and line tracing tasks. Additional resection was performed in 67% of tasks in the CBCT group, and qualitative feedback indicated unequivocal improvement in confidence for all tasks. In review of tasks in the control group, additional resection would have been performed in 35% of tasks if an intraoperative image was available. CONCLUSIONS An experimental prototype C-arm CBCT guidance system was shown to improve surgical precision in the identification of skull base targets and increase accuracy in the ablation of surgical target volumes in comparison to using IGS alone.},

keywords = {CBCT, Head/Neck, Image Guided Surgery, System Assessment},

pubstate = {published},

tppubtype = {article}

}

@article{lee2011cone,

title = {Cone Beam CT-Assisted Endoscopic Sinus and Skull Base Surgery: Quantitative Analysis of Surgical Performance Using a Next-Generation C-Arm Prototype},

author = {Stella Lee and Gary L. Gallia and Douglas D. Reh and Sebastian Schafer and Ali Uneri and Daniel J. Mirota and Sajendra Nithiananthan and Yoshito Otake and J. Webster Stayman and Wojciech Zbijewski and Jeffrey H. Siewerdsen},

url = {http://www.thieme-connect.de/DOI/DOI?10.1055/s-2011-1274205},

doi = {10.1055/s-2011-1274205},

issn = {1531-5010},

year  = {2011},

date = {2011-01-01},

journal = {Skull Base},

volume = {21},

number = {S 01},

pages = {A030},

keywords = {CBCT, Head/Neck, Image Guided Surgery, System Assessment},

pubstate = {published},

tppubtype = {article}

}