@article{Flores2021,

title = {Direct reconstruction of anatomical change in low-dose lung nodule surveillance},

author = {Jessica Flores and Grace Gang and Hao Zhang and Chen Ting Lin and Shui K Fung and J. Webster Stayman},

url = {https://pubmed.ncbi.nlm.nih.gov/33846692/},

doi = {10.1117/1.JMI.8.2.023503 },

year  = {2021},

date = {2021-04-01},

journal = {Journal of Medical Imaging},

volume = {8},

number = {2},

pages = {023503},

keywords = {Image Registration, Lungs, MBIR, Prior Images},

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{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{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{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{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{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{dang2014dpirple,

title = {dPIRPLE: a joint estimation framework for deformable registration and penalized-likelihood CT image reconstruction using prior images.},

author = {Hao Dang and Adam S. Wang and Marc S. Sussman and Jeffrey H. Siewerdsen and J. Webster Stayman },

url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4142353},

doi = {10.1088/0031-9155/59/17/4799},

issn = {1361-6560},

year  = {2014},

date = {2014-09-01},

journal = {Physics in medicine and biology},

volume = {59},

number = {17},

pages = {4799--826},

publisher = {IOP Publishing},

abstract = {Sequential imaging studies are conducted in many clinical scenarios. Prior images from previous studies contain a great deal of patient-specific anatomical information and can be used in conjunction with subsequent imaging acquisitions to maintain image quality while enabling radiation dose reduction (e.g., through sparse angular sampling, reduction in fluence, etc). However, patient motion between images in such sequences results in misregistration between the prior image and current anatomy. Existing prior-image-based approaches often include only a simple rigid registration step that can be insufficient for capturing complex anatomical motion, introducing detrimental effects in subsequent image reconstruction. In this work, we propose a joint framework that estimates the 3D deformation between an unregistered prior image and the current anatomy (based on a subsequent data acquisition) and reconstructs the current anatomical image using a model-based reconstruction approach that includes regularization based on the deformed prior image. This framework is referred to as deformable prior image registration, penalized-likelihood estimation (dPIRPLE). Central to this framework is the inclusion of a 3D B-spline-based free-form-deformation model into the joint registration-reconstruction objective function. The proposed framework is solved using a maximization strategy whereby alternating updates to the registration parameters and image estimates are applied allowing for improvements in both the registration and reconstruction throughout the optimization process. Cadaver experiments were conducted on a cone-beam CT testbench emulating a lung nodule surveillance scenario. Superior reconstruction accuracy and image quality were demonstrated using the dPIRPLE algorithm as compared to more traditional reconstruction methods including filtered backprojection, penalized-likelihood estimation (PLE), prior image penalized-likelihood estimation (PIPLE) without registration, and prior image penalized-likelihood estimation with rigid registration of a prior image (PIRPLE) over a wide range of sampling sparsity and exposure levels.},

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

pubstate = {published},

tppubtype = {article}

}

@article{Stayman2013b,

title = {PIRPLE: a penalized-likelihood framework for incorporation of prior images in CT reconstruction.},

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

url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC3868341},

doi = {10.1088/0031-9155/58/21/7563},

issn = {1361-6560},

year  = {2013},

date = {2013-11-01},

journal = {Physics in medicine and biology},

volume = {58},

number = {21},

pages = {7563--82},

abstract = {Over the course of diagnosis and treatment, it is common for a number of imaging studies to be acquired. Such imaging sequences can provide substantial patient-specific prior knowledge about the anatomy that can be incorporated into a prior-image-based tomographic reconstruction for improved image quality and better dose utilization. We present a general methodology using a model-based reconstruction approach including formulations of the measurement noise that also integrates prior images. This penalized-likelihood technique adopts a sparsity enforcing penalty that incorporates prior information yet allows for change between the current reconstruction and the prior image. Moreover, since prior images are generally not registered with the current image volume, we present a modified model-based approach that seeks a joint registration of the prior image in addition to the reconstruction of projection data. We demonstrate that the combined prior-image- and model-based technique outperforms methods that ignore the prior data or lack a noise model. Moreover, we demonstrate the importance of registration for prior-image-based reconstruction methods and show that the prior-image-registered penalized-likelihood estimation (PIRPLE) approach can maintain a high level of image quality in the presence of noisy and undersampled projection data.},

keywords = {Image Registration, MBIR, Prior Images, Sequential CT},

pubstate = {published},

tppubtype = {article}

}

@article{Lee2012,

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

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

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

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

issn = {1361-6560},

year  = {2012},

date = {2012-08-01},

journal = {Physics in medicine and biology},

volume = {57},

number = {15},

pages = {4969--89},

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

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

pubstate = {published},

tppubtype = {article}

}