@article{Tivnan2021b,

title = {A prototype spatial–spectral CT system for material decomposition with energy‐integrating detectors},

author = {Matt Tivnan and Wenying Wang and J. Webster Stayman },

url = {https://pubmed.ncbi.nlm.nih.gov/33964021/, https://aapm.onlinelibrary.wiley.com/doi/full/10.1002/mp.14930},

doi = {10.1002/mp.14930 },

year  = {2021},

date = {2021-10-01},

journal = {Medical Physics},

volume = {48},

issue = {10},

pages = {6401-6411},

keywords = {Sparse Sampling, Spectral X-ray/CT, 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{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{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}

}