Evaluation of Intensity-Based 2D-3D Spine Image Registration Using Clinical Gold-Standard Data

Russakoff, Daniel B.; Rohlfing, Torsten; Ho, Anthony D.; Kim, Daniel H.; Shahidi, Ramin; Adler, John R.; Maurer, Calvin R.

doi:10.1007/978-3-540-39701-4_16

Cited by 35 publications

(33 citation statements)

References 12 publications

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“…Markerbased registered CT, MR and X-ray images of the spine can serve as a truly clinical "gold standard", but are very difficult to obtain. Russakoff et al [15] reported on the evaluation of intensity-based 3D/ 2D spine image registration using clinical "gold standard data". Unfortunately, to the best of our knowledge, their data are not publicly available.…”

Section: Discussionmentioning

confidence: 99%

“Gold standard” data for evaluation and comparison of 3D/2D registration methods

Tomaževič

Likar

Pernuš

2004

Computer Aided Surgery

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Evaluation and comparison of registration techniques for image-guided surgery is an important problem that has received little attention in the literature. In this paper we address the challenging problem of generating reliable "gold standard" data for use in evaluating the accuracy of 3D/2D registrations. We have devised a cadaveric lumbar spine phantom with fiducial markers and established highly accurate correspondences between 3D CT and MR images and 18 2D X-ray images. The expected target registration errors for target points on the pedicles are less than 0.26 mm for CT-to-X-ray registration and less than 0.42 mm for MR-to-X-ray registration. As such, the "gold standard" data, which has been made publicly available on the Internet (http://lit.fe.uni-lj.si/Downloads/downloads.asp), is useful for evaluation and comparison of 3D/2D image registration methods.

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Section: Discussionmentioning

confidence: 99%

“Gold standard” data for evaluation and comparison of 3D/2D registration methods

Tomaževič

Likar

Pernuš

2004

Computer Aided Surgery

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“…We used normalised cross-correlation (NCC) as the similarity measure, which is a common measure for multi-modality registration problems (Russakoff et al 2003). Purely intensity-based metrics like sum of squared differences do not work for this problem, since the grey values of p bgr (i, u) and p fwp (i, u) differ due to the gated reconstruction and the maximum intensity forward projection.…”

Section: Image Registrationmentioning

confidence: 99%

Residual motion compensation in ECG-gated interventional cardiac vasculature reconstruction

Schwemmer¹,

Rohkohl²,

Lauritsch³

et al. 2013

Phys. Med. Biol.

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Abstract. 3-D reconstruction of cardiac vasculature from angiographic C-arm CT (rotational angiography) data is a major challenge. Motion artefacts corrupt image quality, reducing usability for diagnosis and guidance. Many state-of-theart approaches depend on retrospective ECG-gating of projection data for image reconstruction. A trade-off has to be made regarding the size of the ECG-gating window. A large temporal window is desirable to avoid undersampling. However, residual motion will occur in a large window, causing motion artefacts.We present an algorithm to correct for residual motion. Our approach is based on a deformable 2-D-2-D registration between the forward projection of an initial, ECG-gated reconstruction, and the original projection data. The approach is fully automatic and does not require any complex segmentation of vasculature, or landmarks. The estimated motion is compensated for during the backprojection step of a subsequent reconstruction. We evaluated the method using the publicly available cavarev platform and on six human clinical datasets. We found a better visibility of structure, reduced motion artefacts, and increased sharpness of the vessels in the compensated reconstructions compared to the initial reconstructions. At the time of writing, our algorithm outperforms the leading result of the cavarev ranking list. For the clinical datasets, we found an average reduction of motion artefacts by 13±6%. Vessel sharpness was improved by 25±12% on average.

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“…Therefore, a value of -1 indicates a perfect positive linear relationship, a value of +1 a perfect negative linear relationship and values close to zero show no linear correlation between the volumes. The definition of the negative NCC [18], [19] combined with the computational formula for the variance [20] is given by…”

Section: Objective Functionmentioning

confidence: 99%

4-D motion field estimation by Combined Multiple Heart Phase Registration (CMHPR) for cardiac C-arm data

Müller¹,

Rohkohl

Lauritsch

et al. 2012

2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC)

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Abstract-In interventional cardiology three-dimensional anatomical and functional information of the cardiac chambers would have an important impact on diagnosis and therapy. With the technology of C-arm CT it is possible to reconstruct intraprocedural 3-D images from angiographic projection data. In order to generate accurate and artifact-free reconstructions from dynamic cardiac projections, the motion needs to be taken into account. We present the novel Combined Multiple Heart Phase Registration (CMHPR) method. CMHPR is an iterative motion estimation and compensation algorithm that uses projection data acquired during a single C-arm sweep. Filtered-backprojection (FBP) volumes from electrocardiogram (ECG)-gated data are reconstructed for different motion states of the heart. According to an unknown 4-D motion vector field the ECG-gated FBP images are deformed and accumulated to a sum volume for representing the status of a particular heart phase. In an iterative optimization procedure the 4-D motion vector field is computed by registering the sum volume to a reference volume of the same heart phase. The negative normalized cross correlation (NCC) of both volumes is used as a cost function. In this paper, the reference image is generated using the prior image constrained compressed sensing (PICCS) algorithm combined with the improved total variation (iTV). First preliminary experiments on clinical porcine data sets show promising results. CMHPR reduces streak artifacts and simultaneously preserves sharp edges without producing the artificial comic-like appearance of the PICCS + iTV reference volume.

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Evaluation of Intensity-Based 2D-3D Spine Image Registration Using Clinical Gold-Standard Data

Cited by 35 publications

References 12 publications

“Gold standard” data for evaluation and comparison of 3D/2D registration methods

“Gold standard” data for evaluation and comparison of 3D/2D registration methods

Residual motion compensation in ECG-gated interventional cardiac vasculature reconstruction

4-D motion field estimation by Combined Multiple Heart Phase Registration (CMHPR) for cardiac C-arm data

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