@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{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{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{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{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{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{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{gang2015taskb,

title = {Task-driven image acquisition and reconstruction in cone-beam CT.},

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

url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4539970},

doi = {10.1088/0031-9155/60/8/3129},

issn = {1361-6560},

year  = {2015},

date = {2015-04-01},

journal = {Physics in medicine and biology},

volume = {60},

number = {8},

pages = {3129--50},

publisher = {IOP Publishing},

abstract = {This work introduces a task-driven imaging framework that incorporates a mathematical definition of the imaging task, a model of the imaging system, and a patient-specific anatomical model to prospectively design image acquisition and reconstruction techniques to optimize task performance. The framework is applied to joint optimization of tube current modulation, view-dependent reconstruction kernel, and orbital tilt in cone-beam CT. The system model considers a cone-beam CT system incorporating a flat-panel detector and 3D filtered backprojection and accurately describes the spatially varying noise and resolution over a wide range of imaging parameters in the presence of a realistic anatomical model. Task-based detectability index (d') is incorporated as the objective function in a task-driven optimization of image acquisition and reconstruction techniques. The orbital tilt was optimized through an exhaustive search across tilt angles ranging ± 30°. For each tilt angle, the view-dependent tube current and reconstruction kernel (i.e. the modulation profiles) that maximized detectability were identified via an alternating optimization. The task-driven approach was compared with conventional unmodulated and automatic exposure control (AEC) strategies for a variety of imaging tasks and anthropomorphic phantoms. The task-driven strategy outperformed the unmodulated and AEC cases for all tasks. For example, d' for a sphere detection task in a head phantom was improved by 30% compared to the unmodulated case by using smoother kernels for noisy views and distributing mAs across less noisy views (at fixed total mAs) in a manner that was beneficial to task performance. Similarly for detection of a line-pair pattern, the task-driven approach increased d' by 80% compared to no modulation by means of view-dependent mA and kernel selection that yields modulation transfer function and noise-power spectrum optimal to the task. Optimization of orbital tilt identified the tilt angle that reduced quantum noise in the region of the stimulus by avoiding highly attenuating anatomical structures. The task-driven imaging framework offers a potentially valuable paradigm for prospective definition of acquisition and reconstruction protocols that improve task performance without increase in dose.},

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

pubstate = {published},

tppubtype = {article}

}

@article{Gang2014,

title = {Task-based detectability in CT image reconstruction by filtered backprojection and penalized likelihood estimation.},

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

url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4115652},

doi = {10.1118/1.4883816},

issn = {0094-2405},

year  = {2014},

date = {2014-08-01},

journal = {Medical physics},

volume = {41},

number = {8},

pages = {081902},

publisher = {American Association of Physicists in Medicine},

abstract = {PURPOSE Nonstationarity is an important aspect of imaging performance in CT and cone-beam CT (CBCT), especially for systems employing iterative reconstruction. This work presents a theoretical framework for both filtered-backprojection (FBP) and penalized-likelihood (PL) reconstruction that includes explicit descriptions of nonstationary noise, spatial resolution, and task-based detectability index. Potential utility of the model was demonstrated in the optimal selection of regularization parameters in PL reconstruction. METHODS Analytical models for local modulation transfer function (MTF) and noise-power spectrum (NPS) were investigated for both FBP and PL reconstruction, including explicit dependence on the object and spatial location. For FBP, a cascaded systems analysis framework was adapted to account for nonstationarity by separately calculating fluence and system gains for each ray passing through any given voxel. For PL, the point-spread function and covariance were derived using the implicit function theorem and first-order Taylor expansion according to Fessler ["Mean and variance of implicitly defined biased estimators (such as penalized maximum likelihood): Applications to tomography," IEEE Trans. Image Process. 5(3), 493-506 (1996)]. Detectability index was calculated for a variety of simple tasks. The model for PL was used in selecting the regularization strength parameter to optimize task-based performance, with both a constant and a spatially varying regularization map. RESULTS Theoretical models of FBP and PL were validated in 2D simulated fan-beam data and found to yield accurate predictions of local MTF and NPS as a function of the object and the spatial location. The NPS for both FBP and PL exhibit similar anisotropic nature depending on the pathlength (and therefore, the object and spatial location within the object) traversed by each ray, with the PL NPS experiencing greater smoothing along directions with higher noise. The MTF of FBP is isotropic and independent of location to a first order approximation, whereas the MTF of PL is anisotropic in a manner complementary to the NPS. Task-based detectability demonstrates dependence on the task, object, spatial location, and smoothing parameters. A spatially varying regularization "map" designed from locally optimal regularization can improve overall detectability beyond that achievable with the commonly used constant regularization parameter. CONCLUSIONS Analytical models for task-based FBP and PL reconstruction are predictive of nonstationary noise and resolution characteristics, providing a valuable framework for understanding and optimizing system performance in CT and CBCT.},

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

pubstate = {published},

tppubtype = {article}

}

@article{wang2014low,

title = {Low-dose preview for patient-specific, task-specific technique selection in cone-beam CT.},

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

url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4106458},

doi = {10.1118/1.4884039},

issn = {0094-2405},

year  = {2014},

date = {2014-07-01},

journal = {Medical physics},

volume = {41},

number = {7},

pages = {071915},

publisher = {American Association of Physicists in Medicine},

abstract = {PURPOSE A method is presented for generating simulated low-dose cone-beam CT (CBCT) preview images from which patient- and task-specific minimum-dose protocols can be confidently selected prospectively in clinical scenarios involving repeat scans. METHODS In clinical scenarios involving a series of CBCT images, the low-dose preview (LDP) method operates upon the first scan to create a projection dataset that accurately simulates the effects of dose reduction in subsequent scans by injecting noise of proper magnitude and correlation, including both quantum and electronic readout noise as important components of image noise in flat-panel detector CBCT. Experiments were conducted to validate the LDP method in both a head phantom and a cadaveric torso by performing CBCT acquisitions spanning a wide dose range (head: 0.8-13.2 mGy, body: 0.8-12.4 mGy) with a prototype mobile C-arm system. After injecting correlated noise to simulate dose reduction, the projections were reconstructed using both conventional filtered backprojection (FBP) and an iterative, model-based image reconstruction method (MBIR). The LDP images were then compared to real CBCT images in terms of noise magnitude, noise-power spectrum (NPS), spatial resolution, contrast, and artifacts. RESULTS For both FBP and MBIR, the LDP images exhibited accurate levels of spatial resolution and contrast that were unaffected by the correlated noise injection, as expected. Furthermore, the LDP image noise magnitude and NPS were in strong agreement with real CBCT images acquired at the corresponding, reduced dose level across the entire dose range considered. The noise magnitude agreed within 7% for both the head phantom and cadaveric torso, and the NPS showed a similar level of agreement up to the Nyquist frequency. Therefore, the LDP images were highly representative of real image quality across a broad range of dose and reconstruction methods. On the other hand, naïve injection ofuncorrelated noise resulted in strong underestimation of the true noise, which would lead to overly optimistic predictions of dose reduction. CONCLUSIONS Correlated noise injection is essential to accurate simulation of CBCT image quality at reduced dose. With the proposed LDP method, the user can prospectively select patient-specific, minimum-dose protocols (viz., acquisition technique and reconstruction method) suitable to a particular imaging task and to the user's own observer preferences for CBCT scans following the first acquisition. The method could provide dose reduction in common clinical scenarios involving multiple CBCT scans, such as image-guided surgery and radiotherapy.},

keywords = {Analysis, CBCT, Regularization Design, System Assessment},

pubstate = {published},

tppubtype = {article}

}

@article{Stayman2004a,

title = {Compensation for nonuniform resolution using penalized-likelihood reconstruction in space-variant imaging systems.},

author = {J. Webster Stayman and Jeffrey A. Fessler },

url = {http://www.ncbi.nlm.nih.gov/pubmed/15027520},

doi = {10.1109/TMI.2003.823063},

issn = {0278-0062},

year  = {2004},

date = {2004-03-01},

journal = {IEEE transactions on medical imaging},

volume = {23},

number = {3},

pages = {269--84},

abstract = {Imaging systems that form estimates using a statistical approach generally yield images with nonuniform resolution properties. That is, the reconstructed images possess resolution properties marked by space-variant and/or anisotropic responses. We have previously developed a space-variant penalty for penalized-likelihood (PL) reconstruction that yields nearly uniform resolution properties. We demonstrated how to calculate this penalty efficiently and apply it to an idealized positron emission tomography (PET) system whose geometric response is space-invariant. In this paper, we demonstrate the efficient calculation and application of this penalty to space-variant systems. (The method is most appropriate when the system matrix has been precalculated.) We apply the penalty to a large field of view PET system where crystal penetration effects make the geometric response space-variant, and to a two-dimensional single photon emission computed tomography system whose detector responses are modeled by a depth-dependent Gaussian with linearly varying full-width at half-maximum. We perform a simulation study comparing reconstructions using our proposed PL approach with other reconstruction methods and demonstrate the relative resolution uniformity, and discuss tradeoffs among estimators that yield nearly uniform resolution. We observe similar noise performance for the PL and post-smoothed maximum-likelihood (ML) approaches with carefully matched resolution, so choosing one estimator over another should be made on other factors like computational complexity and convergence rates of the iterative reconstruction. Additionally, because the postsmoothed ML and the proposed PL approach can outperform one another in terms of resolution uniformity depending on the desired reconstruction resolution, we present and discuss a hybrid approach adopting both a penalty and post-smoothing.},

keywords = {Analysis, MBIR, Regularization Design},

pubstate = {published},

tppubtype = {article}

}

@article{stayman2000regularization,

title = {Regularization for uniform spatial resolution properties in penalized-likelihood image reconstruction.},

author = {J. Webster Stayman and Jeffrey A. Fessler },

url = {http://www.ncbi.nlm.nih.gov/pubmed/11026463},

doi = {10.1109/42.870666},

issn = {0278-0062},

year  = {2000},

date = {2000-06-01},

journal = {IEEE transactions on medical imaging},

volume = {19},

number = {6},

pages = {601--15},

publisher = {IEEE},

abstract = {Traditional space-invariant regularization methods in tomographic image reconstruction using penalized-likelihood estimators produce images with nonuniform spatial resolution properties. The local point spread functions that quantify the smoothing properties of such estimators are space-variant, asymmetric, and object-dependent even for space-invariant imaging systems. We propose a new quadratic regularization scheme for tomographic imaging systems that yields increased spatial uniformity and is motivated by the least-squares fitting of a parameterized local impulse response to a desired global response. We have developed computationally efficient methods for PET systems with shift-invariant geometric responses. We demonstrate the increased spatial uniformity of this new method versus conventional quadratic regularization schemes in simulated PET thorax scans.},

keywords = {Analysis, MBIR, Regularization Design},

pubstate = {published},

tppubtype = {article}

}