@article{Tivnan2023b,

title = {Tunable neural networks for CT image formation},

author = {Matt Tivnan and Grace Gang and Wenying Wang and Peter Noël and Jeremias Sulam and J. Webster Stayman},

url = {https://www.spiedigitallibrary.org/journals/journal-of-medical-imaging/volume-10/issue-3/033501/Tunable-neural-networks-for-CT-image-formation/10.1117/1.JMI.10.3.033501.full

https://pubmed.ncbi.nlm.nih.gov/37151806/},

doi = {10.1117/1.JMI.10.3.033501},

year  = {2023},

date = {2023-05-01},

journal = {Journal of Medical Imaging},

volume = {10},

issue = {3},

pages = {033501-033501},

keywords = {High-Fidelity Modeling, Machine Learning/Deep Learning, Task-Driven Imaging},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Fourier Diffusion Models: A Method to Control MTF and NPS in Score-Based Stochastic Image Generation},

author = {Matt Tivnan and Jacopo Teneggi and Tzu-Cheng Lee and Ruoqiao Zhang and Kirsten Boedeker and Liang Cai and Grace Gang and Jeremias Sulam and J. Webster Stayman},

url = {https://arxiv.org/abs/2303.13285},

doi = {10.48550/arXiv.2303.13285},

year  = {2023},

date = {2023-03-23},

journal = {arXiv},

keywords = {High-Fidelity Modeling, Machine Learning/Deep Learning, Task-Driven Imaging},

pubstate = {forthcoming},

tppubtype = {article}

}

@article{Wang2019d,

title = {Volume-of-interest Imaging with Dynamic Fluence Modulation Using Multiple Aperture Devices},

author = {Wenying Wang and Grace Gang and Jeffrey H. Siewerdsen and Reuven Levinson and Satomi Kawamoto and J. Webster Stayman},

url = {https://pubmed.ncbi.nlm.nih.gov/31528659/},

doi = {10.1117/1.JMI.6.3.033504},

year  = {2019},

date = {2019-09-01},

journal = {Journal of Medical Imaging},

volume = {6},

number = {3},

pages = {033504},

abstract = {Volume-of-interest (VOI) imaging is a strategy in computed tomography (CT) that restricts x-ray fluence to particular anatomical targets via dynamic beam modulation. This permits dose reduction while retaining image quality within the VOI. VOI-CT implementation has been challenged, in part, by a lack of hardware solutions for tailoring the incident fluence to the patient and anatomical site, as well as difficulties involving interior tomography reconstruction of truncated projection data. We propose a general VOI-CT imaging framework using multiple aperture devices (MADs), an emerging beam filtration scheme based on two binary x-ray filters. Location of the VOI is prescribed using two scout views at anterior-posterior (AP) and lateral perspectives. Based on a calibration of achievable fluence field patterns, MAD motion trajectories were designed using an optimization objective that seeks to maximize the relative fluence in the VOI subject to minimum fluence constraints. A modified penalized-likelihood method is developed for reconstruction of heavily truncated data using the full-field scout views to help solve the interior tomography problem. Physical experiments were conducted to show the feasibility of noncentered and elliptical VOI in two applications-spine and lung imaging. Improved dose utilization and retained image quality are validated with respect to standard full-field protocols. We observe that the contrast-to-noise ratio (CNR) is 40% higher compared with low-dose full-field scans at the same dose. The total dose reduction is 50% for equivalent image quality (CNR) within the VOI.},

keywords = {Customized Acquisition, Dynamic Bowtie, Task-Driven Imaging},

pubstate = {published},

tppubtype = {article}

}

@article{Stayman2019,

title = {Task-driven source–detector trajectories in cone-beam computed tomography: I. Theory and methods},

author = {J. Webster Stayman and Sarah Capostagno and Grace Gang and Jeffrey H. Siewerdsen },

url = {https://www.spiedigitallibrary.org/journals/Journal-of-Medical-Imaging/volume-6/issue-2/025002/Task-driven-sourcedetector-trajectories-in-cone-beam-computed-tomography/10.1117/1.JMI.6.2.025002.full},

doi = {10.1117/1.JMI.6.2.025002},

year  = {2019},

date = {2019-05-02},

journal = {Journal of Medical Imaging},

volume = {6},

number = {2},

pages = {025002},

keywords = {CBCT, Customized Acquisition, Image Guided Surgery, MBIR, Task-Driven Imaging},

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{Jacobson2018,

title = {A line fiducial method for geometric calibration of cone-beam CT systems with diverse scan trajectories},

author = {Matthew W. Jacobson and Michael Ketcha and Sarah Capostagno and Andrew Martin and Ali Uneri and Joseph Goerres and Tharindu De Silva and Sureerat Reaungamornrat and Runze Han and Amir Manbachi and J. Webster Stayman and Sebastian Vogt and Gerhard Kleinszig and Jeffrey H. Siewerdsen},

url = {http://iopscience.iop.org/article/10.1088/1361-6560/aa9910/meta},

doi = {10.1088/1361-6560/aa9910},

year  = {2018},

date = {2018-01-15},

journal = {Physics in Medicine and Biology},

volume = {63},

number = {2},

pages = {025030},

keywords = {Customized Acquisition, Image Guided Surgery, Task-Driven Imaging},

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{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{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{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{Stayman2015,

title = {Task-Based Optimization of Source-Detector Orbits in Interventional Cone-beam CT},

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

url = {https://aiai.jhu.edu/papers/Fully3D2015_stayman.pdf},

year  = {2015},

date = {2015-01-01},

journal = {International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine},

volume = {13},

keywords = {CBCT, Customized Acquisition, MBIR, 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{Gang2012,

title = {Cascaded systems analysis of noise and detectability in dual-energy cone-beam CT.},

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

url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC3422357},

doi = {10.1118/1.4736420},

issn = {0094-2405},

year  = {2012},

date = {2012-08-01},

journal = {Medical physics},

volume = {39},

number = {8},

pages = {5145--56},

publisher = {American Association of Physicists in Medicine},

abstract = {PURPOSE Dual-energy computed tomography and dual-energy cone-beam computed tomography (DE-CBCT) are promising modalities for applications ranging from vascular to breast, renal, hepatic, and musculoskeletal imaging. Accordingly, the optimization of imaging techniques for such applications would benefit significantly from a general theoretical description of image quality that properly incorporates factors of acquisition, reconstruction, and tissue decomposition in DE tomography. This work reports a cascaded systems analysis model that includes the Poisson statistics of x rays (quantum noise), detector model (flat-panel detectors), anatomical background, image reconstruction (filtered backprojection), DE decomposition (weighted subtraction), and simple observer models to yield a task-based framework for DE technique optimization. METHODS The theoretical framework extends previous modeling of DE projection radiography and CBCT. Signal and noise transfer characteristics are propagated through physical and mathematical stages of image formation and reconstruction. Dual-energy decomposition was modeled according to weighted subtraction of low- and high-energy images to yield the 3D DE noise-power spectrum (NPS) and noise-equivalent quanta (NEQ), which, in combination with observer models and the imaging task, yields the dual-energy detectability index (d(')). Model calculations were validated with NPS and NEQ measurements from an experimental imaging bench simulating the geometry of a dedicated musculoskeletal extremities scanner. Imaging techniques, including kVp pair and dose allocation, were optimized using d(') as an objective function for three example imaging tasks: (1) kidney stone discrimination; (2) iodine vs bone in a uniform, soft-tissue background; and (3) soft tissue tumor detection on power-law anatomical background. RESULTS Theoretical calculations of DE NPS and NEQ demonstrated good agreement with experimental measurements over a broad range of imaging conditions. Optimization results suggest a lower fraction of total dose imparted by the low-energy acquisition, a finding consistent with previous literature. The selection of optimal kVp pair reveals the combined effect of both quantum noise and contrast in the kidney stone discrimination and soft-tissue tumor detection tasks, whereas the K-edge effect of iodine was the dominant factor in determining kVp pairs in the iodine vs bone task. The soft-tissue tumor task illustrated the benefit of dual-energy imaging in eliminating anatomical background noise and improving detectability beyond that achievable by single-energy scans. CONCLUSIONS This work established a task-based theoretical framework that is predictive of DE image quality. The model can be utilized in optimizing a broad range of parameters in image acquisition, reconstruction, and decomposition, providing a useful tool for maximizing DE-CBCT image quality and reducing dose.},

keywords = {Analysis, Spectral X-ray/CT, Task-Driven Imaging},

pubstate = {published},

tppubtype = {article}

}

@article{Gang2011,

title = {Analysis of Fourier-domain task-based detectability index in tomosynthesis and cone-beam CT in relation to human observer performance.},

author = {Grace Gang and Junghoon Lee and J. Webster Stayman and Daniel J. Mirota and Wojciech Zbijewski and Jerry L. Prince and Jeffrey H. Siewerdsen },

url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC3069989},

doi = {10.1118/1.3560428},

issn = {0094-2405},

year  = {2011},

date = {2011-04-01},

journal = {Medical physics},

volume = {38},

number = {4},

pages = {1754--68},

institution = {Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario M5G 2M9, Canada.},

abstract = {PURPOSE Design and optimization of medical imaging systems benefit from accurate theoretical modeling that identifies the physical factors governing image quality, particularly in the early stages of system development. This work extends Fourier metrics of imaging performance and detectability index (d') to tomosynthesis and cone-beam CT (CBCT) and investigates the extent to which d' is a valid descriptor of task-based imaging performance as assessed by human observers, METHODS The detectability index for tasks presented in 2D slices (d'(slice)) was derived from 3D cascaded systems analysis of tomosynthesis and CBCT. Anatomical background noise measured in a physical phantom presenting power-law spectral density was incorporated in the "generalized" noise-equivalent quanta. Theoretical calculations of d'(slice) were performed as a function of total angular extent (theta(tot)) of source-detector orbit ranging 10 degrees - 360 degrees under two acquisition schemes: (i) Constant angular separation between projections (constant-delta theta), giving variable number of projections (N(proj)) and dose vs theta(tot) and (ii) constant number of projections (constant-N(proj)), giving constant dose (but variable angular sampling) with theta(tot). Five simple observer models were investigated: Prewhitening (PW), prewhitening with eye filter and internal noise (PWEi), nonprewhitening (NPW), nonprewhitening with eye filter (NPWE), and nonprewhitening with eye filter and internal noise (NPWEi). Human observer performance was measured in 9AFC tests for five simple imaging tasks presented within uniform and power-law clutter backgrounds. Measurements (from 9AFC tests) and theoretical calculations (from cascaded systems analysis of d'(slice)) were compared in terms of area under the ROC curve (A(z)) RESULTS Reasonable correspondence between theoretical calculations and human observer performance was achieved for all imaging tasks over the broad range of experimental conditions and acquisition schemes. The PW and PWEi observer models tended to overestimate detectability, while the various NPW models predicted observer performance fairly well, with NPWEi giving the best overall agreement. Detectability was shown to increase with theta(tot) due to the reduction of out-of-plane clutter, reaching a plateau after a particular theta(tot) that depended on the imaging task. Depending on the acquisition scheme, however (i.e., constant-N(proj) or delta theta), detectability was seen in some cases to decline at higher theta(tot) due to tradeoffs among quantum noise, background clutter, and view sampling. CONCLUSIONS Generalized detectability index derived from a 3D cascaded systems model shows reasonable correspondence with human observer performance over a fairly broad range of imaging tasks and conditions, although discrepancies were observed in cases relating to orbits intermediate to 180 degrees and 360 degrees. The basic correspondence of theoretical and measured performance supports the application of such a theoretical framework for system design and optimization of tomosynthesis and CBCT.},

keywords = {Analysis, CBCT, Task-Driven Imaging},

pubstate = {published},

tppubtype = {article}

}