Registration of a CT-like atlas to fluoroscopic X-ray images using intensity correspondences

Hurvitz, Aviv; Joskowicz, Leo

doi:10.1007/s11548-008-0264-z

Cited by 49 publications

(18 citation statements)

References 25 publications

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“…We also note that changes associated with gross patient deformation (e.g., differences in spine curvature) due to posture difference or anatomical changes across large areas (e.g., disc resection) may also adversely affect the registration. In such cases, the LevelCheck algorithm is likely more useful at the beginning of the procedure when such changes are less prominent and introduce less mismatch between fluoroscopy and preoperative CT. Methods incorporating deformable models that encode shape deformation as the principal component modes of a statistical atlas (Sadowsky, Chintalapani, & Taylor, 2007)(Hurvitz & Joskowicz, 2008), control points in a free-form deformation (Qi, Gu, & Xu, 2008), and/or a piece-wise rigid deformation is subject of future work.…”

Section: Discussionmentioning

confidence: 99%

Automatic localization of vertebral levels in x-ray fluoroscopy using 3D-2D registration: a tool to reduce wrong-site surgery

Otake¹,

Schäfer²,

Stayman³

et al. 2012

Phys. Med. Biol.

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Surgical targeting of the incorrect vertebral level (“wrong-level” surgery) is among the more common wrong-site surgical errors, attributed primarily to a lack of uniquely identifiable radiographic landmarks in the mid-thoracic spine. 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 (viz., CT-to-fluoroscopy) registration. A gradient information (GI) similarity metric and CMA-ES optimizer were chosen due to their robustness and inherent suitability for parallelization. Simulation studies involved 10 patient CT datasets from which 50,000 simulated fluoroscopic images were generated from C-arm poses selected to approximate 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 (viz., mPD < 5mm). Simulation studies showed a success rate of 99.998% (1 failure in 50,000 trials) and computation time of 4.7 sec 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 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.

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Section: Discussionmentioning

confidence: 99%

Automatic localization of vertebral levels in x-ray fluoroscopy using 3D-2D registration: a tool to reduce wrong-site surgery

Otake¹,

Schäfer²,

Stayman³

et al. 2012

Phys. Med. Biol.

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show abstract

“…Intensity based methods rely on only information and intensity of the images. They optimize intensity similarity between the X-ray images and simulated X-ray projection images called digitally reconstructed radiographs (DRRs) which can be generated from CT-scan such as methods used in (Mahfouz et al ,2006;Yao et al, 2003;Hurvitz et al, 2008;Tang et al, 2005). The success of Edge-feature based methods depends on the quality of the segmentation.…”

Section: Evaluation Of Stereoradiographic 3d Reconstruion Methodsmentioning

confidence: 99%

3d Reconstruction From Multi-View Medical X-Ray Images – Review and Evaluation of Existing Methods

Hosseinian

Arefi

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The 3D concept is extremely important in clinical studies of human body. Accurate 3D models of bony structures are currently required in clinical routine for diagnosis, patient follow-up, surgical planning, computer assisted surgery and biomechanical applications. However, 3D conventional medical imaging techniques such as computed tomography (CT) scan and magnetic resonance imaging (MRI) have serious limitations such as using in non-weight-bearing positions, costs and high radiation dose(for CT). Therefore, 3D reconstruction methods from biplanar X-ray images have been taken into consideration as reliable alternative methods in order to achieve accurate 3D models with low dose radiation in weight-bearing positions. Different methods have been offered for 3D reconstruction from X-ray images using photogrammetry which should be assessed. In this paper, after demonstrating the principles of 3D reconstruction from X-ray images, different existing methods of 3D reconstruction of bony structures from radiographs are classified and evaluated with various metrics and their advantages and disadvantages are mentioned. Finally, a comparison has been done on the presented methods with respect to several metrics such as accuracy, reconstruction time and their applications. With regards to the research, each method has several advantages and disadvantages which should be considered for a specific application.

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“…Although each individual has different tissues, most of the tissues are in line with a statistics model. Therefore, some researchers have proposed non-linear methods of image registration based on the statistics model; these methods are based either on the distribution model [ 31 ] or the portfolio theory [ 32 ].…”

Section: Geometric Transformationmentioning

confidence: 99%

A Review on Medical Image Registration as an Optimization Problem

Song

Han

Zhao

et al. 2017

CMIR

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Objective: In the course of clinical treatment, several medical media are required by a phy-sician in order to provide accurate and complete information about a patient. Medical image registra-tion techniques can provide a richer diagnosis and treatment information to doctors and to provide a comprehensive reference source for the researchers involved in image registration as an optimization problem.Methods: The essence of image registration is associating two or more different images spatial asso-ciation, and getting the translation of their spatial relationship. For medical image registration, its pro-cess is not absolute. Its core purpose is finding the conversion relationship between different images.Result: The major step of image registration includes the change of geometrical dimensions, and change of the image of the combination, image similarity measure, iterative optimization and interpo-lation process.Conclusion: The contribution of this review is sort of related image registration research methods, can provide a brief reference for researchers about image registration.

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Registration of a CT-like atlas to fluoroscopic X-ray images using intensity correspondences

Cited by 49 publications

References 25 publications

Automatic localization of vertebral levels in x-ray fluoroscopy using 3D-2D registration: a tool to reduce wrong-site surgery

Automatic localization of vertebral levels in x-ray fluoroscopy using 3D-2D registration: a tool to reduce wrong-site surgery

3d Reconstruction From Multi-View Medical X-Ray Images – Review and Evaluation of Existing Methods

A Review on Medical Image Registration as an Optimization Problem

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