Technology preview: X‐ray fused with magnetic resonance during invasive cardiovascular procedures

Gutiérrez, Luis; Silva, Ranil de; Ozturk, Cengizhan; Sonmez, Merdim; Stine, Annette M.; Raval, Amish N.; Raman, Venkatesh K.; Sachdev, Vandana; Aviles, Ronnier J.; Waclawiw, Myron A.; McVeigh, Elliot R.; Lederman, Robert J.

doi:10.1002/ccd.21352

Cited by 59 publications

(42 citation statements)

References 24 publications

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“…Although CMR requires a substantially longer image acquisition time than ACT, there is no ionizing radiation and the functional information obtained by CMR is available. Several investigators have described techniques for the registration of CMR images with biplane fluoroscopy to allow the use of this 3D dataset for roadmapping (46)(47)(48)(49). Much future work will be required to determine the optimal application of these new strategies in transcatheter evaluation and treatment of congenital heart disease.…”

Section: Discussionmentioning

confidence: 98%

Use of Angiographic CT Imaging in the Cardiac Catheterization Laboratory for Congenital Heart Disease

Glatz

Zhu

Gillespie

et al. 2010

JACC: Cardiovascular Imaging

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Section: Discussionmentioning

confidence: 98%

Use of Angiographic CT Imaging in the Cardiac Catheterization Laboratory for Congenital Heart Disease

Glatz

Zhu

Gillespie

et al. 2010

JACC: Cardiovascular Imaging

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“…Finally, the rigid transformation between the two sets of beads is achieved by a version of the well-known iterative closest point ͑ICP͒ algorithm 20 and is described in Sec. II D. While previous publications 9,12,13,21 have presented the XFM system as applied to endomyocardial injections, 9 mitral cerclage annuloplasty, 21 and the closure of ventricular septal defects 13 ͑VSDs͒ in a swine model, none have presented the computational method that achieves the automatic registration. In early works, 9,21 the procedure was minimally automated and required extensive, time-consuming, interaction by a skilled human operator to localize the MR and X-ray markers by manual examination of the data.…”

Section: Ia Xfm Guidance For Interventional Proceduresmentioning

confidence: 99%

“…The colored contours, representing the aorta ͑red͒, left ventricular epicardium ͑green͒, left ventricular endocardium ͑blue͒, and right ventricular endocardium ͑yel-low͒, extracted from the MR image are displayed on the grayscale XF projection image. The process of creating the contours from the MR image is outside the scope of this paper, but details can be found in the work of Ratanayak et al 12 Notice that the contours line up with the faint shadow of the heart in the XF image.…”

Section: Iiib In Vivo Experimentsmentioning

confidence: 99%

Robust automatic rigid registration of MRI and X‐ray using external fiducial markers for XFM‐guided interventional procedures

et al. 2010

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Purpose: In X-ray fused with MRI, previously gathered roadmap MRI volume images are overlaid on live X-ray fluoroscopy images to help guide the clinician during an interventional procedure. The incorporation of MRI data allows for the visualization of soft tissue that is poorly visualized under X-ray. The widespread clinical use of this technique will require fully automating as many components as possible. While previous use of this method has required time-consuming manual intervention to register the two modalities, in this article, the authors present a fully automatic rigid-body registration method. Methods: External fiducial markers that are visible under these two complimentary imaging modalities were used to register the X-ray images with the roadmap MR images. The method has three components: ͑a͒ The identification of the 3D locations of the markers from a full 3D MR volume, ͑b͒ the identification of the 3D locations of the markers from a small number of 2D X-ray fluoroscopy images, and ͑c͒ finding the rigid-body transformation that registers the two point sets in the two modalities. For part ͑a͒, the localization of the markers from MR data, the MR volume image was thresholded, connected voxels were segmented and labeled, and the centroids of the connected components were computed. For part ͑b͒, the X-ray projection images, produced by an image intensifier, were first corrected for distortions. Binary mask images of the markers were created from the distortion-corrected X-ray projection images by applying edge detection, pattern recognition, and image morphological operations. The markers were localized in the X-ray frame using an iterative backprojection-based method which segments voxels in the volume of interest, discards false positives based on the previously computed edge-detected projections, and calculates the locations of the true markers as the centroids of the clusters of voxels that remain. For part ͑c͒, a variant of the iterative closest point method was used to find correspondences between and register the two sets of points computed from MR and X-ray data. This knowledge of the correspondence between the two point sets was used to refine, first, the X-ray marker localization and then the total rigid-body registration between modalities. The rigid-body registration was used to overlay the roadmap MR image onto the X-ray fluoroscopy projections. Results: In 35 separate experiments, the markers were correctly registered to each other in 100% of the cases. When half the number of X-ray projections was used ͑10 X-ray projections instead of 20͒, the markers were correctly registered in all 35 experiments. The method was also successful in all 35 experiments when the number of markers was ͑retrospectively͒ halved ͑from 16 to 8͒. The target registration error was computed in a phantom experiment to be less than 2.4 mm. In two in vivo experiments, targets ͑interventional devices with pointlike metallic structures͒ inside the heart were successfully registered between the two modalities. Conclusio...

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“…It has good spatial and temporal resolution and clearly visualizes interventional devices and bones. However, the contrast of soft tissue is low and 3-D information is lost due to the transparent projection to 2-D. To remedy these drawbacks, fusion of the X-ray images with previously acquired overlays has been proposed [1]–[3], which is also known as augmented fluoroscopy. The overlays are rendered semi-transparently directly on top of the X-ray images, see Fig.…”

Section: Introductionmentioning

confidence: 99%

An MR-Based Model for Cardio-Respiratory Motion Compensation of Overlays in X-Ray Fluoroscopy

Fischer

Faranesh

Pohl

et al. 2018

IEEE Trans. Med. Imaging

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In X-ray fluoroscopy, static overlays are used to visualize soft tissue. We propose a system for cardiac and respiratory motion compensation of these overlays. It consists of a 3-D motion model created from real-time MR imaging. Multiple sagittal slices are acquired and retrospectively stacked to consistent 3-D volumes. Slice stacking considers cardiac information derived from the ECG and respiratory information extracted from the images. Additionally, temporal smoothness of the stacking is enhanced. Motion is estimated from the MR volumes using deformable 3-D/3-D registration. The motion model itself is a linear direct correspondence model using the same surrogate signals as slice stacking. In X-ray fluoroscopy, only the surrogate signals need to be extracted to apply the motion model and animate the overlay in real time. For evaluation, points are manually annotated in oblique MR slices and in contrast-enhanced X-ray images. The 2-D Euclidean distance of these points is reduced from 3.85 mm to 2.75 mm in MR and from 3.0 mm to 1.8 mm in X-ray compared to the static baseline. Furthermore, the motion-compensated overlays are shown qualitatively as images and videos.

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Technology preview: X‐ray fused with magnetic resonance during invasive cardiovascular procedures

Cited by 59 publications

References 24 publications

Use of Angiographic CT Imaging in the Cardiac Catheterization Laboratory for Congenital Heart Disease

Use of Angiographic CT Imaging in the Cardiac Catheterization Laboratory for Congenital Heart Disease

Robust automatic rigid registration of MRI and X‐ray using external fiducial markers for XFM‐guided interventional procedures

An MR-Based Model for Cardio-Respiratory Motion Compensation of Overlays in X-Ray Fluoroscopy

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