@article{Li2024b,

title = {Generative Adversarial Networks with Radiomics Supervision for Lung Lesion Generation},

author = {Junyuan Li and Shaoyan Pan and Xiaoxuan Zhang and J. Webster Stayman and Grace Gang},

url = {https://ieeexplore.ieee.org/document/10659138},

doi = {10.1109/TBME.2024.3451409},

year  = {2024},

date = {2024-10-01},

urldate = {2024-10-01},

journal = {IEEE Trans. Biomedical Engineering},

keywords = {Lungs, Machine Learning/Deep Learning, Phantoms, Radiomics},

pubstate = {forthcoming},

tppubtype = {article}

}

@article{Shapira2023,

title = {Three-dimensional printing of patient-specific computed tomography lung phantoms: a reader study},

author = {Nadav Shapira and Kevin Donovan and Kai Mei and Michael Geagan and Leonid Roshkovan and Grace Gang and Mohammed Abed and Nathaniel Linna and Coulter Cranston and Cathal O'Leary and Ali Dhanaliwala and Despina Kontos and Harold I Litt and J. Webster Stayman and Russell Shinohara and Peter Noël},

url = {https://academic.oup.com/pnasnexus/article/2/3/pgad026/7019413

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9992761/},

doi = {10.1093/pnasnexus/pgad026},

year  = {2023},

date = {2023-03-01},

journal = {PNAS Nexus},

volume = {2},

issue = {3},

pages = {pgad026},

keywords = {Lungs, Phantoms, System Assessment},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Three‐dimensional printing of patient‐specific lung phantoms for CT imaging: Emulating lung tissue with accurate attenuation profiles and textures},

author = {Kai Mei and Michael Geagan and Leonid Roshkovan and Harold I Litt and Grace Gang and Nadav Shapira and J. Webster Stayman and Peter Noël },

url = {https://pubmed.ncbi.nlm.nih.gov/34910309/, https://aapm.onlinelibrary.wiley.com/doi/full/10.1002/mp.15407},

doi = { 10.1002/mp.15407 },

year  = {2022},

date = {2022-02-01},

urldate = {2022-02-01},

journal = {Medical Physics},

volume = {49},

issue = {2},

pages = {825-835},

keywords = {Lungs, Phantoms},

pubstate = {published},

tppubtype = {article}

}

@article{Mei2021,

title = {Three-dimensional printing of patient-specific lung phantoms for CT imaging: emulating lung tissue with accurate attenuation profiles and textures},

author = {Kai Mei and Michael Geagan and Leonid Roshkovan and Harold I Litt and Grace Gang and Nadav Shapira and J. Webster Stayman and Peter Noël},

url = {https://www.medrxiv.org/content/10.1101/2021.07.30.21261292v1.full-text},

doi = {10.1101/2021.07.30.21261292 },

year  = {2021},

date = {2021-08-01},

urldate = {2021-08-01},

journal = {Medical Physics},

pages = {Submitted},

keywords = {Lungs, System Assessment},

pubstate = {published},

tppubtype = {article}

}

@article{Flores2021,

title = {Direct reconstruction of anatomical change in low-dose lung nodule surveillance},

author = {Jessica Flores and Grace Gang and Hao Zhang and Chen Ting Lin and Shui K Fung and J. Webster Stayman},

url = {https://pubmed.ncbi.nlm.nih.gov/33846692/},

doi = {10.1117/1.JMI.8.2.023503 },

year  = {2021},

date = {2021-04-01},

journal = {Journal of Medical Imaging},

volume = {8},

number = {2},

pages = {023503},

keywords = {Image Registration, Lungs, MBIR, Prior Images},

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{uneri2013deformable,

title = {Deformable registration of the inflated and deflated lung in cone-beam CT-guided thoracic surgery: initial investigation of a combined model- and image-driven approach.},

author = {Ali Uneri and Sajendra Nithiananthan and Sebastian Schafer and Yoshito Otake and J. Webster Stayman and Gerhard Kleinszig and Marc S. Sussman and Jerry L. Prince and Jeffrey H. Siewerdsen },

url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC3537709},

doi = {10.1118/1.4767757},

issn = {0094-2405},

year  = {2013},

date = {2013-01-01},

journal = {Medical physics},

volume = {40},

number = {1},

pages = {017501},

publisher = {American Association of Physicists in Medicine},

abstract = {PURPOSE Surgical resection is the preferred modality for curative treatment of early stage lung cancer, but localization of small tumors (textless10 mm diameter) during surgery presents a major challenge that is likely to increase as more early-stage disease is detected incidentally and in low-dose CT screening. To overcome the difficulty of manual localization (fingers inserted through intercostal ports) and the cost, logistics, and morbidity of preoperative tagging (coil or dye placement under CT-fluoroscopy), the authors propose the use of intraoperative cone-beam CT (CBCT) and deformable image registration to guide targeting of small tumors in video-assisted thoracic surgery (VATS). A novel algorithm is reported for registration of the lung from its inflated state (prior to pleural breach) to the deflated state (during resection) to localize surgical targets and adjacent critical anatomy. METHODS The registration approach geometrically resolves images of the inflated and deflated lung using a coarse model-driven stage followed by a finer image-driven stage. The model-driven stage uses image features derived from the lung surfaces and airways: triangular surface meshes are morphed to capture bulk motion; concurrently, the airways generate graph structures from which corresponding nodes are identified. Interpolation of the sparse motion fields computed from the bounding surface and interior airways provides a 3D motion field that coarsely registers the lung and initializes the subsequent image-driven stage. The image-driven stage employs an intensity-corrected, symmetric form of the Demons method. The algorithm was validated over 12 datasets, obtained from porcine specimen experiments emulating CBCT-guided VATS. Geometric accuracy was quantified in terms of target registration error (TRE) in anatomical targets throughout the lung, and normalized cross-correlation. Variations of the algorithm were investigated to study the behavior of the model- and image-driven stages by modifying individual algorithmic steps and examining the effect in comparison to the nominal process. RESULTS The combined model- and image-driven registration process demonstrated accuracy consistent with the requirements of minimally invasive VATS in both target localization (∼3-5 mm within the target wedge) and critical structure avoidance (∼1-2 mm). The model-driven stage initialized the registration to within a median TRE of 1.9 mm (95% confidence interval (CI) maximum = 5.0 mm), while the subsequent image-driven stage yielded higher accuracy localization with 0.6 mm median TRE (95% CI maximum = 4.1 mm). The variations assessing the individual algorithmic steps elucidated the role of each step and in some cases identified opportunities for further simplification and improvement in computational speed. CONCLUSIONS The initial studies show the proposed registration method to successfully register CBCT images of the inflated and deflated lung. Accuracy appears sufficient to localize the target and adjacent critical anatomy within ∼1-2 mm and guide localization under conditions in which the target cannot be discerned directly in CBCT (e.g., subtle, nonsolid tumors). The ability to directly localize tumors in the operating room could provide a valuable addition to the VATS arsenal, obviate the cost, logistics, and morbidity of preoperative tagging, and improve patient safety. Future work includes in vivo testing, optimization of workflow, and integration with a CBCT image guidance system.},

keywords = {CBCT, Image Registration, Lungs},

pubstate = {published},

tppubtype = {article}

}