@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{Ouadah2017b,

title = {Correction of Patient Motion in Cone-Beam CT Using 3D-2D Registration},

author = {Sarah Ouadah and Matthew W. Jacobson and J. Webster Stayman and Tina Ehtiati and Clifford Weiss and J. Webster Stayman },

url = {http://iopscience.iop.org/article/10.1088/1361-6560/aa9254},

doi = {10.1088/1361-6560/aa9254},

year  = {2017},

date = {2017-12-01},

journal = {Physics in Medicine and Biology},

volume = {62},

number = {23},

pages = {8813},

keywords = {Image Guided Surgery, Image Registration, Motion Compensation},

pubstate = {published},

tppubtype = {article}

}

@article{Ouadah2016,

title = {Self-calibration of cone-beam CT geometry using 3D-2D image registration.},

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

url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4948745},

doi = {10.1088/0031-9155/61/7/2613},

issn = {1361-6560},

year  = {2016},

date = {2016-04-01},

journal = {Physics in medicine and biology},

volume = {61},

number = {7},

pages = {2613--32},

abstract = {Robotic C-arms are capable of complex orbits that can increase field of view, reduce artifacts, improve image quality, and/or reduce dose; however, it can be challenging to obtain accurate, reproducible geometric calibration required for image reconstruction for such complex orbits. This work presents a method for geometric calibration for an arbitrary source-detector orbit by registering 2D projection data to a previously acquired 3D image. It also yields a method by which calibration of simple circular orbits can be improved. The registration uses a normalized gradient information similarity metric and the covariance matrix adaptation-evolution strategy optimizer for robustness against local minima and changes in image content. The resulting transformation provides a 'self-calibration' of system geometry. The algorithm was tested in phantom studies using both a cone-beam CT (CBCT) test-bench and a robotic C-arm (Artis Zeego, Siemens Healthcare) for circular and non-circular orbits. Self-calibration performance was evaluated in terms of the full-width at half-maximum (FWHM) of the point spread function in CBCT reconstructions, the reprojection error (RPE) of steel ball bearings placed on each phantom, and the overall quality and presence of artifacts in CBCT images. In all cases, self-calibration improved the FWHM-e.g. on the CBCT bench},

keywords = {CBCT, Customized Acquisition, Geometric Calibration, Image Registration},

pubstate = {published},

tppubtype = {article}

}

@article{Uneri2015,

title = {Known-component 3D-2D registration for quality assurance of spine surgery pedicle screw placement.},

author = {Ali Uneri and Tharindu De Silva and J. Webster Stayman and Gerhard Kleinszig and Sebastian Vogt and A. Jay Khanna and Ziya L. Gokaslan and John-Paul Wolinsky and Jeffrey H. Siewerdsen },

url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4640195},

doi = {10.1088/0031-9155/60/20/8007},

issn = {1361-6560},

year  = {2015},

date = {2015-10-01},

journal = {Physics in medicine and biology},

volume = {60},

number = {20},

pages = {8007--24},

abstract = {A 3D-2D image registration method is presented that exploits knowledge of interventional devices (e.g. K-wires or spine screws-referred to as 'known components') to extend the functionality of intraoperative radiography/fluoroscopy by providing quantitative measurement and quality assurance (QA) of the surgical product. The known-component registration (KC-Reg) algorithm uses robust 3D-2D registration combined with 3D component models of surgical devices known to be present in intraoperative 2D radiographs. Component models were investigated that vary in fidelity from simple parametric models (e.g. approximation of a screw as a simple cylinder, referred to as 'parametrically-known' component [pKC] registration) to precise models based on device-specific CAD drawings (referred to as 'exactly-known' component [eKC] registration). 3D-2D registration from three intraoperative radiographs was solved using the covariance matrix adaptation evolution strategy (CMA-ES) to maximize image-gradient similarity, relating device placement relative to 3D preoperative CT of the patient. Spine phantom and cadaver studies were conducted to evaluate registration accuracy and demonstrate QA of the surgical product by verification of the type of devices delivered and conformance within the 'acceptance window' of the spinal pedicle. Pedicle screws were successfully registered to radiographs acquired from a mobile C-arm, providing TRE 1-4 mm and textless5° using simple parametric (pKC) models, further improved to textless1 mm and textless1° using eKC registration. Using advanced pKC models, screws that did not match the device models specified in the surgical plan were detected with an accuracy of textgreater99%. Visualization of registered devices relative to surgical planning and the pedicle acceptance window provided potentially valuable QA of the surgical product and reliable detection of pedicle screw breach. 3D-2D registration combined with 3D models of known surgical devices offers a novel method for intraoperative QA. The method provides a near-real-time independent check against pedicle breach, facilitating revision within the same procedure if necessary and providing more rigorous verification of the surgical product.},

keywords = {Image Registration, Known Components, Spine},

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{Otake2013,

title = {Robust 3D-2D image registration: application to spine interventions and vertebral labeling in the presence of anatomical deformation.},

author = {Yoshito Otake and Adam S. Wang and J. Webster Stayman and Ali Uneri and Gerhard Kleinszig and Sebastian Vogt and A. Jay Khanna and Ziya L. Gokaslan and Jeffrey H. Siewerdsen },

url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4915373},

doi = {10.1088/0031-9155/58/23/8535},

issn = {1361-6560},

year  = {2013},

date = {2013-12-01},

journal = {Physics in medicine and biology},

volume = {58},

number = {23},

pages = {8535--53},

abstract = {We present a framework for robustly estimating registration between a 3D volume image and a 2D projection image and evaluate its precision and robustness in spine interventions for vertebral localization in the presence of anatomical deformation. The framework employs a normalized gradient information similarity metric and multi-start covariance matrix adaptation evolution strategy optimization with local-restarts, which provided improved robustness against deformation and content mismatch. The parallelized implementation allowed orders-of-magnitude acceleration in computation time and improved the robustness of registration via multi-start global optimization. Experiments involved a cadaver specimen and two CT datasets (supine and prone) and 36 C-arm fluoroscopy images acquired with the specimen in four positions (supine, prone, supine with lordosis, prone with kyphosis), three regions (thoracic, abdominal, and lumbar), and three levels of geometric magnification (1.7, 2.0, 2.4). Registration accuracy was evaluated in terms of projection distance error (PDE) between the estimated and true target points in the projection image, including 14 400 random trials (200 trials on the 72 registration scenarios) with initialization error up to ±200 mm and ±10°. The resulting median PDE was better than 0.1 mm in all cases, depending somewhat on the resolution of input CT and fluoroscopy images. The cadaver experiments illustrated the tradeoff between robustness and computation time, yielding a success rate of 99.993% in vertebral labeling (with 'success' defined as PDE textless5 mm) using 1,718 664 ± 96 582 function evaluations computed in 54.0 ± 3.5 s on a mid-range GPU (nVidia, GeForce GTX690). Parameters yielding a faster search (e.g., fewer multi-starts) reduced robustness under conditions of large deformation and poor initialization (99.535% success for the same data registered in 13.1 s), but given good initialization (e.g., ±5 mm, assuming a robust initial run) the same registration could be solved with 99.993% success in 6.3 s. The ability to register CT to fluoroscopy in a manner robust to patient deformation could be valuable in applications such as radiation therapy, interventional radiology, and an assistant to target localization (e.g., vertebral labeling) in image-guided spine surgery.},

keywords = {CBCT, Image Registration, Spine},

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}

}

@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}

}

@article{Stayman2012a,

title = {Model-based tomographic reconstruction of objects containing known components.},

author = {J. Webster Stayman and Yoshito Otake and Jerry L. Prince and A. Jay Khanna and Jeffrey H. Siewerdsen},

url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4503263},

doi = {10.1109/TMI.2012.2199763},

issn = {1558-254X},

year  = {2012},

date = {2012-10-01},

journal = {IEEE transactions on medical imaging},

volume = {31},

number = {10},

pages = {1837--48},

abstract = {The likelihood of finding manufactured components (surgical tools, implants, etc.) within a tomographic field-of-view has been steadily increasing. One reason is the aging population and proliferation of prosthetic devices, such that more people undergoing diagnostic imaging have existing implants, particularly hip and knee implants. Another reason is that use of intraoperative imaging (e.g., cone-beam CT) for surgical guidance is increasing, wherein surgical tools and devices such as screws and plates are placed within or near to the target anatomy. When these components contain metal, the reconstructed volumes are likely to contain severe artifacts that adversely affect the image quality in tissues both near and far from the component. Because physical models of such components exist, there is a unique opportunity to integrate this knowledge into the reconstruction algorithm to reduce these artifacts. We present a model-based penalized-likelihood estimation approach that explicitly incorporates known information about component geometry and composition. The approach uses an alternating maximization method that jointly estimates the anatomy and the position and pose of each of the known components. We demonstrate that the proposed method can produce nearly artifact-free images even near the boundary of a metal implant in simulated vertebral pedicle screw reconstructions and even under conditions of substantial photon starvation. The simultaneous estimation of device pose also provides quantitative information on device placement that could be valuable to quality assurance and verification of treatment delivery.},

keywords = {Artifact Correction, Image Registration, Known Components, MBIR, Metal Artifacts, Spine},

pubstate = {published},

tppubtype = {article}

}

@article{otake2012automaticb,

title = {Automatic localization of vertebral levels in x-ray fluoroscopy using 3D-2D registration: a tool to reduce wrong-site surgery.},

author = {Yoshito Otake and Sebastian Schafer and J. Webster Stayman and Wojciech Zbijewski and Gerhard Kleinszig and Rainer Graumann and A. Jay Khanna and Jeffrey H. Siewerdsen },

url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC3429949},

doi = {10.1088/0031-9155/57/17/5485},

issn = {1361-6560},

year  = {2012},

date = {2012-09-01},

journal = {Physics in medicine and biology},

volume = {57},

number = {17},

pages = {5485--508},

publisher = {IOP Publishing},

abstract = {Surgical targeting of the incorrect vertebral level (wrong-level surgery) is among the more common wrong-site surgical errors, attributed primarily to the lack of uniquely identifiable radiographic landmarks in the mid-thoracic spine. The conventional localization method involves manual counting of vertebral bodies under fluoroscopy, is prone to human error and carries additional time and dose. We propose an image registration and visualization system (referred to as LevelCheck), for decision support in spine surgery by automatically labeling vertebral levels in fluoroscopy using a GPU-accelerated, intensity-based 3D-2D (namely CT-to-fluoroscopy) registration. A gradient information (GI) similarity metric and a CMA-ES optimizer were chosen due to their robustness and inherent suitability for parallelization. Simulation studies involved ten patient CT datasets from which 50 000 simulated fluoroscopic images were generated from C-arm poses selected to approximate the C-arm operator and positioning variability. Physical experiments used an anthropomorphic chest phantom imaged under real fluoroscopy. The registration accuracy was evaluated as the mean projection distance (mPD) between the estimated and true center of vertebral levels. Trials were defined as successful if the estimated position was within the projection of the vertebral body (namely mPD textless5 mm). Simulation studies showed a success rate of 99.998% (1 failure in 50 000 trials) and computation time of 4.7 s on a midrange GPU. Analysis of failure modes identified cases of false local optima in the search space arising from longitudinal periodicity in vertebral structures. Physical experiments demonstrated the robustness of the algorithm against quantum noise and x-ray scatter. The ability to automatically localize target anatomy in fluoroscopy in near-real-time could be valuable in reducing the occurrence of wrong-site surgery while helping to reduce radiation exposure. The method is applicable beyond the specific case of vertebral labeling, since any structure defined in pre-operative (or intra-operative) CT or cone-beam CT can be automatically registered to the fluoroscopic scene.},

keywords = {CBCT, Image Guided Surgery, Image Registration, Multimodality, Spine},

pubstate = {published},

tppubtype = {article}

}

@article{Nithiananthan2012,

title = {Extra-dimensional Demons: a method for incorporating missing tissue in deformable image registration.},

author = {Sajendra Nithiananthan and Sebastian Schafer and Daniel J. Mirota and J. Webster Stayman and Wojciech Zbijewski and Douglas D. Reh and Gary L. Gallia and Jeffrey H. Siewerdsen },

url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC3443194},

doi = {10.1118/1.4747270},

issn = {0094-2405},

year  = {2012},

date = {2012-09-01},

journal = {Medical physics},

volume = {39},

number = {9},

pages = {5718--31},

abstract = {PURPOSE A deformable registration method capable of accounting for missing tissue (e.g., excision) is reported for application in cone-beam CT (CBCT)-guided surgical procedures. Excisions are identified by a segmentation step performed simultaneous to the registration process. Tissue excision is explicitly modeled by increasing the dimensionality of the deformation field to allow motion beyond the dimensionality of the image. The accuracy of the model is tested in phantom, simulations, and cadaver models. METHODS A variant of the Demons deformable registration algorithm is modified to include excision segmentation and modeling. Segmentation is performed iteratively during the registration process, with initial implementation using a threshold-based approach to identify voxels corresponding to "tissue" in the moving image and "air" in the fixed image. With each iteration of the Demons process, every voxel is assigned a probability of excision. Excisions are modeled explicitly during registration by increasing the dimensionality of the deformation field so that both deformations and excisions can be accounted for by in- and out-of-volume deformations, respectively. The out-of-volume (i.e., fourth) component of the deformation field at each voxel carries a magnitude proportional to the excision probability computed in the excision segmentation step. The registration accuracy of the proposed "extra-dimensional" Demons (XDD) and conventional Demons methods was tested in the presence of missing tissue in phantom models, simulations investigating the effect of excision size on registration accuracy, and cadaver studies emulating realistic deformations and tissue excisions imparted in CBCT-guided endoscopic skull base surgery. RESULTS Phantom experiments showed the normalized mutual information (NMI) in regions local to the excision to improve from 1.10 for the conventional Demons approach to 1.16 for XDD, and qualitative examination of the resulting images revealed major differences: the conventional Demons approach imparted unrealistic distortions in areas around tissue excision, whereas XDD provided accurate "ejection" of voxels within the excision site and maintained the registration accuracy throughout the rest of the image. Registration accuracy in areas far from the excision site (e.g., textgreater ∼5 mm) was identical for the two approaches. Quantitation of the effect was consistent in analysis of NMI, normalized cross-correlation (NCC), target registration error (TRE), and accuracy of voxels ejected from the volume (true-positive and false-positive analysis). The registration accuracy for conventional Demons was found to degrade steeply as a function of excision size, whereas XDD was robust in this regard. Cadaver studies involving realistic excision of the clivus, vidian canal, and ethmoid sinuses demonstrated similar results, with unrealistic distortion of anatomy imparted by conventional Demons and accurate ejection and deformation for XDD. CONCLUSIONS Adaptation of the Demons deformable registration process to include segmentation (i.e., identification of excised tissue) and an extra dimension in the deformation field provided a means to accurately accommodate missing tissue between image acquisitions. The extra-dimensional approach yielded accurate "ejection" of voxels local to the excision site while preserving the registration accuracy (typically subvoxel) of the conventional Demons approach throughout the rest of the image. The ability to accommodate missing tissue volumes is important to application of CBCT for surgical guidance (e.g., skull base drillout) and may have application in other areas of CBCT guidance.},

keywords = {CBCT, Image Guided Surgery, Image Registration},

pubstate = {published},

tppubtype = {article}

}

@article{Nithiananthan2011,

title = {Demons deformable registration of CT and cone-beam CT using an iterative intensity matching approach.},

author = {Sajendra Nithiananthan and Sebastian Schafer and Ali Uneri and Daniel J. Mirota and J. Webster Stayman and Wojciech Zbijewski and Kristy K. Brock and Michael J. Daly and Harley Chan and Jonathan C. Irish and Jeffrey H. Siewerdsen},

url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC3069990},

doi = {10.1118/1.3555037},

issn = {0094-2405},

year  = {2011},

date = {2011-04-01},

journal = {Medical physics},

volume = {38},

number = {4},

pages = {1785--98},

abstract = {PURPOSE A method of intensity-based deformable registration of CT and cone-beam CT (CBCT) images is described, in which intensity correction occurs simultaneously within the iterative registration process. The method preserves the speed and simplicity of the popular Demons algorithm while providing robustness and accuracy in the presence of large mismatch between CT and CBCT voxel values ("intensity"). METHODS A variant of the Demons algorithm was developed in which an estimate of the relationship between CT and CBCT intensity values for specific materials in the image is computed at each iteration based on the set of currently overlapping voxels. This tissue-specific intensity correction is then used to estimate the registration output for that iteration and the process is repeated. The robustness of the method was tested in CBCT images of a cadaveric head exhibiting a broad range of simulated intensity variations associated with x-ray scatter, object truncation, and/or errors in the reconstruction algorithm. The accuracy of CT-CBCT registration was also measured in six real cases, exhibiting deformations ranging from simple to complex during surgery or radiotherapy guided by a CBCT-capable C-arm or linear accelerator, respectively. RESULTS The iterative intensity matching approach was robust against all levels of intensity variation examined, including spatially varying errors in voxel value of a factor of 2 or more, as can be encountered in cases of high x-ray scatter. Registration accuracy without intensity matching degraded severely with increasing magnitude of intensity error and introduced image distortion. A single histogram match performed prior to registration alleviated some of these effects but was also prone to image distortion and was quantifiably less robust and accurate than the iterative approach. Within the six case registration accuracy study, iterative intensity matching Demons reduced mean TRE to (2.5 +/- 2.8) mm compared to (3.5 +/- 3.0) mm with rigid registration. CONCLUSIONS A method was developed to iteratively correct CT-CBCT intensity disparity during Demons registration, enabling fast, intensity-based registration in CBCT-guided procedures such as surgery and radiotherapy, in which CBCT voxel values may be inaccurate. Accurate CT-CBCT registration in turn facilitates registration of multimodality preoperative image and planning data to intraoperative CBCT by way of the preoperative CT, thereby linking the intraoperative frame of reference to a wealth of preoperative information that could improve interventional guidance.},

keywords = {CBCT, Image Registration, Multimodality},

pubstate = {published},

tppubtype = {article}

}