@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{Seyyedi2018,

title = {Low-Dose CT Perfusion of the Liver using Reconstruction of Difference},

author = {Saeed Seyyedi and Eleni Liapi and Tobias Lasser and Robert Ivkov and Rajeev Hatwar and J. Webster Stayman },

url = {https://ieeexplore.ieee.org/document/8306928/},

doi = {10.1109/TRPMS.2018.2812360},

year  = {2018},

date = {2018-05-01},

journal = {IEEE Transactions on Radiation and Plasma Medical Sciences},

volume = {2},

number = {3},

pages = {205-214},

keywords = {Prior Images, Sequential CT},

pubstate = {published},

tppubtype = {article}

}

@article{Wu2018,

title = {Reconstruction-of-Difference (RoD) Imaging for Cone-Beam CT Neuro-Angiography},

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

url = {http://iopscience.iop.org/article/10.1088/1361-6560/aac225},

doi = {10.1088/1361-6560/aac225},

year  = {2018},

date = {2018-05-01},

journal = {Physics in Medicine and Biology},

volume = {63},

number = {11},

pages = {115004-1-16},

keywords = {Head/Neck, Prior Images, Sequential CT},

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{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{Stayman2013b,

title = {PIRPLE: a penalized-likelihood framework for incorporation of prior images in CT reconstruction.},

author = {J. Webster Stayman and Hao Dang and Yifu Ding and Jeffrey H. Siewerdsen },

url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC3868341},

doi = {10.1088/0031-9155/58/21/7563},

issn = {1361-6560},

year  = {2013},

date = {2013-11-01},

journal = {Physics in medicine and biology},

volume = {58},

number = {21},

pages = {7563--82},

abstract = {Over the course of diagnosis and treatment, it is common for a number of imaging studies to be acquired. Such imaging sequences can provide substantial patient-specific prior knowledge about the anatomy that can be incorporated into a prior-image-based tomographic reconstruction for improved image quality and better dose utilization. We present a general methodology using a model-based reconstruction approach including formulations of the measurement noise that also integrates prior images. This penalized-likelihood technique adopts a sparsity enforcing penalty that incorporates prior information yet allows for change between the current reconstruction and the prior image. Moreover, since prior images are generally not registered with the current image volume, we present a modified model-based approach that seeks a joint registration of the prior image in addition to the reconstruction of projection data. We demonstrate that the combined prior-image- and model-based technique outperforms methods that ignore the prior data or lack a noise model. Moreover, we demonstrate the importance of registration for prior-image-based reconstruction methods and show that the prior-image-registered penalized-likelihood estimation (PIRPLE) approach can maintain a high level of image quality in the presence of noisy and undersampled projection data.},

keywords = {Image Registration, MBIR, Prior Images, Sequential CT},

pubstate = {published},

tppubtype = {article}

}