@article{Yuan2024b,

title = {Deep Learning CT Image Restoration using System Blur and Noise Models},

author = {Yijie Yuan and Grace Gang and J. Webster Stayman},

url = {https://arxiv.org/abs/2407.14983},

year  = {2024},

date = {2024-10-01},

urldate = {2024-10-01},

journal = {TBD},

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

pubstate = {forthcoming},

tppubtype = {article}

}

@article{Li2023,

title = {Diffusion Posterior Sampling for Nonlinear CT Reconstruction},

author = {Shudong Li and Xiao Jiang and Matt Tivnan and Grace Gang and Yuan Shen and J. Webster Stayman},

url = {https://www.spiedigitallibrary.org/journals/journal-of-medical-imaging/volume-11/issue-4/043504/CT-reconstruction-using-diffusion-posterior-sampling-conditioned-on-a-nonlinear/10.1117/1.JMI.11.4.043504.short},

doi = {10.1117/1.JMI.11.4.043504},

year  = {2024},

date = {2024-08-30},

urldate = {2024-08-30},

journal = {Journal of Medical Imaging},

volume = {11},

issue = {4},

pages = {043504 },

note = {** Featured on Cover **},

keywords = {-Awards-, High-Fidelity Modeling, Machine Learning/Deep Learning, MBIR},

pubstate = {published},

tppubtype = {article}

}

@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{Wang2021b,

title = {High-resolution Model-based Material Decomposition in Dual-layer Flat-panel CBCT},

author = {Wenying Wang and Yiqun Ma and Matt Tivnan and Junyuan Li and Grace Gang and Wojciech Zbijewski and Minghui Lu and Jin Zhang and Josh Star-Lack and Richard Colbeth and J. Webster Stayman },

url = {https://pubmed.ncbi.nlm.nih.gov/34272890/, https://aapm.onlinelibrary.wiley.com/doi/full/10.1002/mp.14894},

doi = { 10.1002/mp.14894 },

year  = {2021},

date = {2021-10-01},

journal = {Medical Physics},

volume = {48},

issue = {10},

pages = {6375-6387},

keywords = {High-Fidelity Modeling, High-Resolution CT, MBIR, Spectral X-ray/CT},

pubstate = {published},

tppubtype = {article}

}

@article{Tilley2019,

title = {Model-based material decomposition with a penalized nonlinear least-squares CT reconstruction algorithm},

author = {Steven Tilley and Wojciech Zbijewski and J. Webster Stayman },

url = {https://pubmed.ncbi.nlm.nih.gov/30561382/},

doi = {10.1088/1361-6560/aaf973},

year  = {2019},

date = {2019-02-01},

journal = {Physics in Medicine and Biology},

volume = {64},

number = {3},

pages = {035005–1-14},

abstract = {Spectral information in CT may be used for material decomposition to produce accurate reconstructions of material density and to separate materials with similar overall attenuation. Traditional methods separate the reconstruction and decomposition steps, often resulting in undesirable trade-offs (e.g. sampling constraints, a simplified spectral model). In this work, we present a model-based material decomposition algorithm which performs the reconstruction and decomposition simultaneously using a multienergy forward model. In a kV-switching simulation study, the presented method is capable of reconstructing iodine at 0.5 mg ml-1 with a contrast-to-noise ratio greater than two, as compared to 3.0 mg ml-1 for image domain decomposition. The presented method also enables novel acquisition methods, which was demonstrated in this work with a combined kV-switching/split-filter acquisition explored in simulation and physical test bench studies. This novel design used four spectral channels to decompose three materials: water, iodine, and gadolinium. In simulation, the presented method accurately reconstructed concentration value estimates with RMSE values of 4.86 mg ml-1 for water, 0.108 mg ml-1 for iodine and 0.170 mg ml-1 for gadolinium. In test-bench data, the RMSE values were 134 mg ml-1, 5.26 mg ml-1 and 1.85 mg ml-1, respectively. These studies demonstrate the ability of model-based material decomposition to produce accurate concentration estimates in challenging spatial/spectral sampling acquisitions.},

keywords = {High-Fidelity Modeling, MBIR, Spectral X-ray/CT},

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{Wu2018c,

title = {Statistical weights for model-based reconstruction in cone-beam CT with electronic noise and dual-gain detector readout},

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

url = {https://iopscience.iop.org/article/10.1088/1361-6560/aaf0b4/meta},

doi = {10.1088/1361-6560/aaf0b4},

year  = {2018},

date = {2018-12-14},

journal = {Physics in Medicine and Biology},

volume = {63},

number = {24},

pages = {245018},

keywords = {CBCT, High-Fidelity Modeling, MBIR},

pubstate = {published},

tppubtype = {article}

}

@article{Tilley2018,

title = {Penalized-likelihood reconstruction with high-fidelity measurement models for high-resolution cone-beam CT imaging},

author = {Steven Tilley and Matthew W. Jacobson and Qian Cao and Michael Brehler and Alejandro Sisniega and Wojciech Zbijewski and J. Webster Stayman },

url = {https://www.ncbi.nlm.nih.gov/pubmed/29621002

https://ieeexplore.ieee.org/document/8125700/},

doi = {10.1109/TMI.2017.2779406},

year  = {2018},

date = {2018-04-01},

journal = {IEEE Transactions on Medical Imaging},

volume = {37},

number = {4},

pages = {988-999},

keywords = {High-Fidelity Modeling, High-Resolution 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{tilley2015model,

title = {Model-based iterative reconstruction for flat-panel cone-beam CT with focal spot blur, detector blur, and correlated noise.},

author = {Steven Tilley and Jeffrey H. Siewerdsen and J. Webster Stayman },

url = {http://www.ncbi.nlm.nih.gov/pubmed/26649783},

doi = {10.1088/0031-9155/61/1/296},

issn = {1361-6560},

year  = {2016},

date = {2016-01-01},

journal = {Physics in medicine and biology},

volume = {61},

number = {1},

pages = {296--319},

publisher = {IOP Publishing},

abstract = {While model-based reconstruction methods have been successfully applied to flat-panel cone-beam CT (FP-CBCT) systems, typical implementations ignore both spatial correlations in the projection data as well as system blurs due to the detector and focal spot in the x-ray source. In this work, we develop a forward model for flat-panel-based systems that includes blur and noise correlation associated with finite focal spot size and an indirect detector (e.g. scintillator). This forward model is used to develop a staged reconstruction framework where projection data are deconvolved and log-transformed, followed by a generalized least-squares reconstruction that utilizes a non-diagonal statistical weighting to account for the correlation that arises from the acquisition and data processing chain. We investigate the performance of this novel reconstruction approach in both simulated data and in CBCT test-bench data. In comparison to traditional filtered backprojection and model-based methods that ignore noise correlation, the proposed approach yields a superior noise-resolution tradeoff. For example, for a system with 0.34 mm FWHM scintillator blur and 0.70 FWHM focal spot blur, using the correlated noise model instead of an uncorrelated noise model increased resolution by 42% (with variance matched at 6.9 × 10(-8) mm(-2)). While this advantage holds across a wide range of systems with differing blur characteristics, the improvements are greatest for systems where source blur is larger than detector blur.},

note = {Errata: [1] The fidelity terms in equations 10 and 12 are missing a multiplication by 0.5. [2] Equation 14 should be mu(x_j) = a + b erf (2 sqrt( log(2) (x_j-d) / FWHM ). [3] In section 3.2 a reference to Figure 10(e) should be 9(f).},

keywords = {CBCT, High-Fidelity Modeling, High-Resolution CT, MBIR},

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{xu2014cascadedb,

title = {Cascaded systems analysis of photon counting detectors.},

author = {Jennifer Xu and Wojciech Zbijewski and Grace Gang and J. Webster Stayman and Katsuyuki Taguchi and Mats Lundqvist and Erik Fredenberg and John A. Carrino and Jeffrey H. Siewerdsen },

url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4281040},

doi = {10.1118/1.4894733},

issn = {0094-2405},

year  = {2014},

date = {2014-10-01},

urldate = {2014-10-01},

journal = {Medical physics},

volume = {41},

number = {10},

pages = {101907},

publisher = {American Association of Physicists in Medicine},

abstract = {PURPOSE Photon counting detectors (PCDs) are an emerging technology with applications in spectral and low-dose radiographic and tomographic imaging. This paper develops an analytical model of PCD imaging performance, including the system gain, modulation transfer function (MTF), noise-power spectrum (NPS), and detective quantum efficiency (DQE). METHODS A cascaded systems analysis model describing the propagation of quanta through the imaging chain was developed. The model was validated in comparison to the physical performance of a silicon-strip PCD implemented on an experimental imaging bench. The signal response, MTF, and NPS were measured and compared to theory as a function of exposure conditions (70 kVp, 1-7 mA), detector threshold, and readout mode (i.e., the option for coincidence detection). The model sheds new light on the dependence of spatial resolution, charge sharing, and additive noise effects on threshold selection and was used to investigate the factors governing PCD performance, including the fundamental advantages and limitations of PCDs in comparison to energy-integrating detectors (EIDs) in the linear regime for which pulse pileup can be ignored. RESULTS The detector exhibited highly linear mean signal response across the system operating range and agreed well with theoretical prediction, as did the system MTF and NPS. The DQE analyzed as a function of kilovolt (peak), exposure, detector threshold, and readout mode revealed important considerations for system optimization. The model also demonstrated the important implications of false counts from both additive electronic noise and charge sharing and highlighted the system design and operational parameters that most affect detector performance in the presence of such factors: for example, increasing the detector threshold from 0 to 100 (arbitrary units of pulse height threshold roughly equivalent to 0.5 and 6 keV energy threshold, respectively), increased the f50 (spatial-frequency at which the MTF falls to a value of 0.50) by ∼30% with corresponding improvement in DQE. The range in exposure and additive noise for which PCDs yield intrinsically higher DQE was quantified, showing performance advantages under conditions of very low-dose, high additive noise, and high fidelity rejection of coincident photons. CONCLUSIONS The model for PCD signal and noise performance agreed with measurements of detector signal, MTF, and NPS and provided a useful basis for understanding complex dependencies in PCD imaging performance and the potential advantages (and disadvantages) in comparison to EIDs as well as an important guide to task-based optimization in developing new PCD imaging systems.},

note = {Moses and Sylvia Greenfield Best Scientific Paper Award 2014 },

keywords = {-Awards-, Analysis, High-Fidelity Modeling, Photon Counting},

pubstate = {published},

tppubtype = {article}

}