@article{Jiang2024c,

title = {Strategies for CT Reconstruction using Diffusion Posterior Sampling with a Nonlinear Model },

author = {Xiao Jiang and Shudong Li and Peiqing Teng and Grace Gang and J. Webster Stayman },

url = {https://arxiv.org/abs/2407.12956},

year  = {2024},

date = {2024-07-17},

urldate = {2024-07-17},

journal = {TBD},

keywords = {Fast Algorithms, Machine Learning/Deep Learning, MBIR},

pubstate = {forthcoming},

tppubtype = {article}

}

@article{Sisniega2021b,

title = {Accelerated 3D image reconstruction with a morphological pyramid and noise-power convergence criterion },

author = {Alejandro Sisniega and J. Webster Stayman and Sarah Capostagno and Clifford Weiss and Tina Ehtiati and Jeffrey H. Siewerdsen},

url = {https://pubmed.ncbi.nlm.nih.gov/33477131/},

doi = {10.1088/1361-6560/abde97 },

year  = {2021},

date = {2021-02-20},

journal = {Physics in Medicine and Biology},

volume = {66},

number = {5},

pages = {055012},

keywords = {Analysis, Fast Algorithms, MBIR},

pubstate = {published},

tppubtype = {article}

}

@article{Dang2017,

title = {Multi-resolution statistical image reconstruction for mitigation of truncation effects: application to cone-beam CT of the head},

author = {Hao Dang and J. Webster Stayman and Alejandro Sisniega and Wojciech Zbijewski and Jennifer Xu and Xiaohui Wang and David H. Foos and Nafi Aygun and Vassilis Koliatsos and Jeffrey H. Siewerdsen},

url = {http://stacks.iop.org/0031-9155/62/i=2/a=539?key=crossref.8d03553e9159a88e56933c7faab62387},

doi = {10.1088/1361-6560/aa52b8},

issn = {0031-9155},

year  = {2017},

date = {2017-01-01},

journal = {Physics in Medicine and Biology},

volume = {62},

number = {2},

pages = {539-559},

keywords = {Artifact Correction, Fast Algorithms, Head/Neck},

pubstate = {published},

tppubtype = {article}

}

@article{Cao2016,

title = {Multiresolution iterative reconstruction in high-resolution extremity cone-beam CT},

author = {Qian Cao and Wojciech Zbijewski and Alejandro Sisniega and John Yorkston and Jeffrey H. Siewerdsen and J. Webster Stayman},

url = {http://stacks.iop.org/0031-9155/61/i=20/a=7263?key=crossref.3714aec7223f83ec2e527f7be1ae5e4f},

doi = {10.1088/0031-9155/61/20/7263},

issn = {0031-9155},

year  = {2016},

date = {2016-10-03},

journal = {Physics in Medicine and Biology},

volume = {61},

number = {20},

pages = {7263-7281},

keywords = {Artifact Correction, Extremities, Fast Algorithms, High-Resolution CT},

pubstate = {published},

tppubtype = {article}

}

@article{wang2014nesterov,

title = {Accelerated statistical reconstruction for C-arm cone-beam CT using Nesterov's method.},

author = {Adam S. Wang and J. Webster Stayman and Yoshito Otake and Sebastian Vogt and Gerhard Kleinszig and Jeffrey H. Siewerdsen },

url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4425726},

doi = {10.1118/1.4914378},

issn = {0094-2405},

year  = {2015},

date = {2015-05-01},

journal = {Medical physics},

volume = {42},

number = {5},

pages = {2699--708},

abstract = {PURPOSE To accelerate model-based iterative reconstruction (IR) methods for C-arm cone-beam CT (CBCT), thereby combining the benefits of improved image quality and/or reduced radiation dose with reconstruction times on the order of minutes rather than hours. METHODS The ordered-subsets, separable quadratic surrogates (OS-SQS) algorithm for solving the penalized-likelihood (PL) objective was modified to include Nesterov's method, which utilizes "momentum" from image updates of previous iterations to better inform the current iteration and provide significantly faster convergence. Reconstruction performance of an anthropomorphic head phantom was assessed on a benchtop CBCT system, followed by CBCT on a mobile C-arm, which provided typical levels of incomplete data, including lateral truncation. Additionally, a cadaveric torso that presented realistic soft-tissue and bony anatomy was imaged on the C-arm, and different projectors were assessed for reconstruction speed. RESULTS Nesterov's method provided equivalent image quality to OS-SQS while reducing the reconstruction time by an order of magnitude (10.0 ×) by reducing the number of iterations required for convergence. The faster projectors were shown to produce similar levels of convergence as more accurate projectors and reduced the reconstruction time by another 5.3 ×. Despite the slower convergence of IR with truncated C-arm CBCT, comparison of PL reconstruction methods implemented on graphics processing units showed that reconstruction time was reduced from 106 min for the conventional OS-SQS method to as little as 2.0 min with Nesterov's method for a volumetric reconstruction of the head. In body imaging, reconstruction of the larger cadaveric torso was reduced from 159 min down to 3.3 min with Nesterov's method. CONCLUSIONS The acceleration achieved through Nesterov's method combined with ordered subsets reduced IR times down to a few minutes. This improved compatibility with clinical workflow better enables broader adoption of IR in CBCT-guided procedures, with corresponding benefits in overcoming conventional limits of image quality at lower dose.},

keywords = {CBCT, Fast Algorithms, MBIR},

pubstate = {published},

tppubtype = {article}

}

@article{wang2015accelerated,

title = {Accelerated statistical reconstruction for C-arm cone-beam CT using Nesterov's method.},

author = {Adam S. Wang and J. Webster Stayman and Yoshito Otake and Sebastian Vogt and Gerhard Kleinszig and Jeffrey H. Siewerdsen },

url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4425726},

doi = {10.1118/1.4914378},

issn = {0094-2405},

year  = {2015},

date = {2015-05-01},

journal = {Medical physics},

volume = {42},

number = {5},

pages = {2699--708},

publisher = {American Association of Physicists in Medicine},

abstract = {PURPOSE To accelerate model-based iterative reconstruction (IR) methods for C-arm cone-beam CT (CBCT), thereby combining the benefits of improved image quality and/or reduced radiation dose with reconstruction times on the order of minutes rather than hours. METHODS The ordered-subsets, separable quadratic surrogates (OS-SQS) algorithm for solving the penalized-likelihood (PL) objective was modified to include Nesterov's method, which utilizes "momentum" from image updates of previous iterations to better inform the current iteration and provide significantly faster convergence. Reconstruction performance of an anthropomorphic head phantom was assessed on a benchtop CBCT system, followed by CBCT on a mobile C-arm, which provided typical levels of incomplete data, including lateral truncation. Additionally, a cadaveric torso that presented realistic soft-tissue and bony anatomy was imaged on the C-arm, and different projectors were assessed for reconstruction speed. RESULTS Nesterov's method provided equivalent image quality to OS-SQS while reducing the reconstruction time by an order of magnitude (10.0 ×) by reducing the number of iterations required for convergence. The faster projectors were shown to produce similar levels of convergence as more accurate projectors and reduced the reconstruction time by another 5.3 ×. Despite the slower convergence of IR with truncated C-arm CBCT, comparison of PL reconstruction methods implemented on graphics processing units showed that reconstruction time was reduced from 106 min for the conventional OS-SQS method to as little as 2.0 min with Nesterov's method for a volumetric reconstruction of the head. In body imaging, reconstruction of the larger cadaveric torso was reduced from 159 min down to 3.3 min with Nesterov's method. CONCLUSIONS The acceleration achieved through Nesterov's method combined with ordered subsets reduced IR times down to a few minutes. This improved compatibility with clinical workflow better enables broader adoption of IR in CBCT-guided procedures, with corresponding benefits in overcoming conventional limits of image quality at lower dose.},

keywords = {CBCT, Fast Algorithms, MBIR},

pubstate = {published},

tppubtype = {article}

}

@article{Stayman2004,

title = {Efficient calculation of resolution and covariance for penalized-likelihood reconstruction in fully 3-D SPECT.},

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

url = {http://www.ncbi.nlm.nih.gov/pubmed/15575411},

doi = {10.1109/TMI.2004.837790},

issn = {0278-0062},

year  = {2004},

date = {2004-12-01},

journal = {IEEE transactions on medical imaging},

volume = {23},

number = {12},

pages = {1543--56},

abstract = {Resolution and covariance predictors have been derived previously for penalized-likelihood estimators. These predictors can provide accurate approximations to the local resolution properties and covariance functions for tomographic systems given a good estimate of the mean measurements. Although these predictors may be evaluated iteratively, circulant approximations are often made for practical computation times. However, when numerous evaluations are made repeatedly (as in penalty design or calculation of variance images), these predictors still require large amounts of computing time. In Stayman and Fessler (2000), we discussed methods for precomputing a large portion of the predictor for shift-invariant system geometries. In this paper, we generalize the efficient procedure discussed in Stayman and Fessler (2000) to shift-variant single photon emission computed tomography (SPECT) systems. This generalization relies on a new attenuation approximation and several observations on the symmetries in SPECT systems. These new general procedures apply to both two-dimensional and fully three-dimensional (3-D) SPECT models, that may be either precomputed and stored, or written in procedural form. We demonstrate the high accuracy of the predictions based on these methods using a simulated anthropomorphic phantom and fully 3-D SPECT system. The evaluation of these predictors requires significantly less computation time than traditional prediction techniques, once the system geometry specific precomputations have been made.},

keywords = {Analysis, Fast Algorithms, MBIR},

pubstate = {published},

tppubtype = {article}

}