Fully Automatic 3D/2D Subtracted Angiography Registration

Kerrien, Erwan; Berger, Marie-Odile; Maurincomme, Eric; Launay, Laurent; Vaillant, Régis; Picard, L

doi:10.1007/10704282_72

Cited by 30 publications

(20 citation statements)

References 15 publications

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“…These method differ with respect to the method of rendering simulated 2D projection images and with respect to the SM, which measures the degree of matching between real and simulated images. In this paper, the simulated 2D projection images were rendered using maximum intensity projection (MIP), 8 while either mutual information 9,24 (MI) and gradient correlation 9,25 (GC) were used as the SM; these two methods will be referred to as MIP-MI and MIP-GC, respectively. The third method is the 3D-2D back-projection gradient-based method (BGB) proposed by Tomaževič et al 18 , which is based on matching the 3D gradient vectors representing 3D surface normals and 2D-to-3D back-projected gradient vectors.…”

Section: State-of-the-art 3d-2d Registration Methodsmentioning

confidence: 99%

“…Furthermore, we hypothesize that by searching simultaneously for rigid-body and the C-arm's pose parameters we can improve the accuracy and success rate of the 3D-2D registration methods. Similar studies have already been performed, 8,21 however those studies lack a thorough and objective and evaluation, since only a limited number of 3D-2D registration methods were tested and only five or less clinical image datasets were used were used for evaluation. Here, we tested three state-of-the-art 3D-2D registration methods and performed quantitative and qualitative evaluation on twenty clinical image datasets containing different cerebrovascular pathologies.…”

Section: Pa Ssmentioning

confidence: 99%

“…2 left). 8 The coarse initialization can be refined by aligning the intensity centroids of the 3D and 2D images and , respectively ( Fig. 2 middle), followed by an exhaustive search of the in-plane translations so as to find the minimal/maximal values of the similarity measure (SM) of the 3D-2D registration method (Fig.…”

Section: Initializationmentioning

confidence: 99%

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Evaluation of 3D-2D registration methods for registration of 3D-DSA and 2D-DSA cerebral images

et al. 2013

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Recent C-arm systems used for endovascular image-guided interventions enable the acquisition of three-dimensional (3D) and dynamic two-dimensional (2D+t) images in the same interventional suite. The 3D images are used to observe the vascular morphology while the 2D+t images show the current state of the intervention. By spatial alignment of 3D and 2D+t images one can facilitate the endovascular interventions, e.g. by displaying the intra-interventional tools and contrast-agent flow in the augmented 3D+t images. To achieve the spatial alignment several 3D-2D registration methods were proposed that are concerned with finding the rigid-body parameters of the 3D image. Meanwhile, the pose of the Carm system is usually obtained through a dedicated C-arm calibration. In practice, the calibrated C-arm pose parameters are typically valid only if the imaged object is positioned in the C-arm's isocenter. To compensate this, the 3D-2D registration should search simultaneously for the rigid-body as well as the C-arm pose parameters. For verification, we tested three 3D-2D registration methods on real, clinical 3D and 2D+t angiographic images of twenty patients, ten of which were imaged with attached fiducial markers to obtain a "gold standard" registration. The results indicate that, compared to searching solely the rigid-body parameters, by searching simultaneously for rigid-body and the C-arm pose parameters significantly improves the accuracy and success rate of 3D-2D registration methods. Among the three tested methods the intensity-based method using mutual information was the most robust, as it successfully registered all clinical datasets, and highly accurate, as the maximal fiducial registration error was less or equal than 0.34 mm.

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Section: State-of-the-art 3d-2d Registration Methodsmentioning

confidence: 99%

Section: Pa Ssmentioning

confidence: 99%

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Evaluation of 3D-2D registration methods for registration of 3D-DSA and 2D-DSA cerebral images

et al. 2013

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“…We use the intensity-based 2-D/3-D registration method that iteratively maximizes the similarity measure between the DRR [4] and the Digitally Subtracted Angiography (DSA) [5]. The intensity-based method is shown to be very accurate for aorta/vessel registration [2].…”

Section: Automatic Compensation For Patient Movementmentioning

confidence: 99%

Visual check and automatic compensation for patient movement during image-guided Abdominal Aortic Aneurysm (AAA) stenting

Ding

Miao

et al. 2012

2012 5th International Conference on BioMedical Engineering and Informatics

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Abdominal Aortic Aneurysm (AAA) Stenting is an interventional repair to treat AAA. This procedure heavily relies on interventional imaging technology for navigation and guidance, typically by real time fluoroscopy with contrast medium injection. Recent advances of interventional imaging technology make it possible to register and fuse pre-operative volumetric data with the fluoroscopy to provide more detailed visualization. However, the registration can be invalidated by patient movement, and therefore needs to be refined once patient movement happens. The current AAA intervention workflow requires physicians to manually fade in and out the volume rendering to visually check the patient movement, and manually re-register the volumetric data if patient movement is observed. The presented paper proposes a pelvis boundary detection method that enables real time monitoring of patient movement and an automatic 2-D/3-D re-registration algorithm that compensates for it. By adding the two proposed components, the AAA intervention workflow becomes more seamless. Experiments on clinical data sets show the effectiveness of the proposed method.

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“…Registration of an X-ray computed tomography (CT) image to one or more X-ray projection images (e.g. simulator images, portal images, fluoroscopy images, amorphous silicon detector images) is a particularly interesting example of 2D-3D registration that has a number of possible applications, including patient placement for radiotherapy planning and treatment verification [1,4], radiosurgery [13], cranial neurosurgery [9], neurointerventions [6,7], spinal surgery [8,21], orthopedic surgery [5], and aortic stenting procedures [2,14,21].…”

Section: Introductionmentioning

confidence: 99%

Fast intensity-based 2D-3D image registration of clinical data using light

Russakoff

Rohlfing

Mäurer

2003

Proceedings Ninth IEEE International Conference on Computer Vision

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Registration of a preoperative CT (3D) image to one or more X-ray projection (2D) images, a special case of the pose estimation problem, has been attempted in a variety of ways with varying degrees of success. Recently, there has been a great deal of interest in intensity-based methods. One of the drawbacks to such methods is the need to create digitally reconstructed radiographs (DRRs) at each step of the optimization process. DRRs are typically generated by ray casting, an operation that requires Ç´Ò ¿ µ time, where we assume that Ò is approximately the size (in voxels) of one side of the DRR as well as one side of the CT volume. We address this issue by extending light field rendering techniques from the computer graphics community to generate DRRs instead of conventional rendered images. Using light fields allows most of the computation to be performed in a preprocessing step; after this precomputation, very accurate DRRs can be generated in Ç´Ò ¾ µ time. Another important issue for 2D-3D registration algorithms is validation. Previously reported 2D-3D registration algorithms were validated using synthetic data or phantoms but not clinical data. We present an intensity-based 2D-3D registration system that generates DRRs using light fields; we validate its performance using clinical data with a known gold standard transformation.

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Fully Automatic 3D/2D Subtracted Angiography Registration

Cited by 30 publications

References 15 publications

Evaluation of 3D-2D registration methods for registration of 3D-DSA and 2D-DSA cerebral images

Evaluation of 3D-2D registration methods for registration of 3D-DSA and 2D-DSA cerebral images

Visual check and automatic compensation for patient movement during image-guided Abdominal Aortic Aneurysm (AAA) stenting

Fast intensity-based 2D-3D image registration of clinical data using light

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