Interventional 4D motion estimation and reconstruction of cardiac vasculature without motion periodicity assumption

Rohkohl, Christopher; Lauritsch, Guenter; Biller, Lisa; Prümmer, Marcus; Boese, Jan; Hornegger, Joachim

doi:10.1016/j.media.2010.05.003

Cited by 35 publications

(31 citation statements)

References 22 publications

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“…However, the periodicity assumption is problematic for the cases where residual motion is strong or for the cases with arrhythmic heart motion (Rohkohl et al, 2009b). Because of this reason, the temporal component is sometimes parameterised by acquisition time (Rohkohl et al, 2009b(Rohkohl et al, ,a, 2010b. This strategy was shown to lead similar, if not superior, reconstructions.…”

Section: Motion Compensated Tomographic Reconstructionmentioning

confidence: 99%

“…Two-dimensional rigid (Movassaghi et al, 2003) and 2D elastic (Hansis et al, 2008d) transformation are employed. Other studies use either 3D (Rohkohl et al, 2008a;Bousse Ast et al, 2009) or 4D (Blondel et al, 2006;Hansis et al, 2009;Rohkohl et al, 2009bRohkohl et al, ,a, 2010b) B-spline transformation. B-spline transformations offer spatial (and temporal if 4D) smoothness and achieve better results at the extent of an increase in the number of parameters to be estimated.…”

Section: Motion Compensated Tomographic Reconstructionmentioning

confidence: 99%

“…These reconstructions are used to define an intensity based registration to estimate the parameters (Rohkohl et al, 2008a). Other possibility is to estimate the motion parameters jointly with the reconstruction (Figure 11b) (Hansis et al, 2009;Rohkohl et al, 2009bRohkohl et al, ,a, 2010b. To achieve this goal, the parameters of the transformation are directly embedded into the analytical (Rohkohl et al, 2009b,a, 2010b) or iterative (Hansis et al, 2009) reconstruction formulations.…”

Section: Motion Compensated Tomographic Reconstructionmentioning

confidence: 99%

“…The squared error between the projections of the reconstruction and X-ray images is used as the energy term in (Hansis et al, 2009;Rohkohl et al, 2009a). Voxelwise multiplication of the reconstruction by a binarized reference 3D reconstruction is another alternative, which require a reference reconstruction (Rohkohl et al, 2009b(Rohkohl et al, , 2010b. Starting from an initial set of parameters and reconstruction, parameters are updated by a gradient based optimization and reconstruction is updated according to the reconstruction formula in an iterative manner.…”

Section: Motion Compensated Tomographic Reconstructionmentioning

confidence: 99%

“…On the other hand, if a 3D/4D motion field is estimated, it can be incorporated into the reconstruction formulation. Section 4.3.5 details how to achieve this for iterative (Blondel et al, 2004(Blondel et al, , 2006Hansis et al, 2009;Bousse Ast et al, 2009) or analytical (Schäfer et al, 2006;Rohkohl et al, 2008aRohkohl et al, , 2009bRohkohl et al, ,a, 2010b formulations.…”

Section: Motion Compensated Tomographic Reconstructionmentioning

confidence: 99%

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Reconstruction of coronary arteries from X-ray angiography: A review

Çimen

Gooya

Graß

et al. 2016

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Despite continuous progress in X-ray angiography systems, X-ray coronary angiography is fundamentally limited by its 2D representation of moving coronary arterial trees, which can negatively impact assessment of coronary artery disease and guidance of percutaneous coronary intervention. To provide clinicians with 3D/3D+time information of coronary arteries, methods computing reconstructions of coronary arteries from X-ray angiography are required. Because of several aspects (e.g. cardiac and respiratory motion, type of X-ray system), reconstruction from X-ray coronary angiography has led to vast amount of research and it still remains as a challenging and dynamic research area. In this paper, we review the state-of-the-art approaches on reconstruction of high-contrast coronary arteries from X-ray angiography. We mainly focus on the theoretical features in model-based (modelling) and tomographic reconstruction of coronary arteries, and discuss the evaluation strategies. We also discuss the potential role of reconstructions in clinical decision making and interventional guidance, and highlight areas for future research.

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Section: Motion Compensated Tomographic Reconstructionmentioning

confidence: 99%

Section: Motion Compensated Tomographic Reconstructionmentioning

confidence: 99%

Section: Motion Compensated Tomographic Reconstructionmentioning

confidence: 99%

Section: Motion Compensated Tomographic Reconstructionmentioning

confidence: 99%

Section: Motion Compensated Tomographic Reconstructionmentioning

confidence: 99%

See 3 more Smart Citations

Reconstruction of coronary arteries from X-ray angiography: A review

Çimen

Gooya

Graß

et al. 2016

Medical Image Analysis

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Symmetry, outliers, and geodesics in coronary artery centerline reconstruction from rotational angiography

Unberath¹,

Taubmann²,

Hell

et al. 2017

Medical Physics

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Deformable motion compensation in interventional cone‐beam CT with a context‐aware learned autofocus metric

Huang,

Liu,

Siewerdsen

et al. 2024

Medical Physics

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PurposeInterventional Cone‐Beam CT (CBCT) offers 3D visualization of soft‐tissue and vascular anatomy, enabling 3D guidance of abdominal interventions. However, its long acquisition time makes CBCT susceptible to patient motion. Image‐based autofocus offers a suitable platform for compensation of deformable motion in CBCT, but it relies on handcrafted motion metrics based on first‐order image properties and that lack awareness of the underlying anatomy. This work proposes a data‐driven approach to motion quantification via a learned, context‐aware, deformable metric, , that quantifies the amount of motion degradation as well as the realism of the structural anatomical content in the image.MethodsThe proposed was modeled as a deep convolutional neural network (CNN) trained to recreate a reference‐based structural similarity metric—visual information fidelity (VIF). The deep CNN acted on motion‐corrupted images, providing an estimation of the spatial VIF map that would be obtained against a motion‐free reference, capturing motion distortion, and anatomic plausibility. The deep CNN featured a multi‐branch architecture with a high‐resolution branch for estimation of voxel‐wise VIF on a small volume of interest. A second contextual, low‐resolution branch provided features associated to anatomical context for disentanglement of motion effects and anatomical appearance. The deep CNN was trained on paired motion‐free and motion‐corrupted data obtained with a high‐fidelity forward projection model for a protocol involving 120 kV and 9.90 mGy. The performance of was evaluated via metrics of correlation with ground truth and with the underlying deformable motion field in simulated data with deformable motion fields with amplitude ranging from 5 to 20 mm and frequency from 2.4 up to 4 cycles/scan. Robustness to variation in tissue contrast and noise levels was assessed in simulation studies with varying beam energy (90–120 kV) and dose (1.19–39.59 mGy). Further validation was obtained on experimental studies with a deformable phantom. Final validation was obtained via integration of on an autofocus compensation framework, applied to motion compensation on experimental datasets and evaluated via metric of spatial resolution on soft‐tissue boundaries and sharpness of contrast‐enhanced vascularity.ResultsThe magnitude and spatial map of showed consistent and high correlation levels with the ground truth in both simulation and real data, yielding average normalized cross correlation (NCC) values of 0.95 and 0.88, respectively. Similarly, achieved good correlation values with the underlying motion field, with average NCC of 0.90. In experimental phantom studies, properly reflects the change in motion amplitudes and frequencies: voxel‐wise averaging of the local across the full reconstructed volume yielded an average value of 0.69 for the case with mild motion (2 mm, 12 cycles/scan) and 0.29 for the case with severe motion (12 mm, 6 cycles/scan). Autofocus motion compensation using resulted in noticeable mitigation of motion artifacts and improved spatial resolution of soft tissue and high‐contrast structures, resulting in reduction of edge spread function width of 8.78% and 9.20%, respectively. Motion compensation also increased the conspicuity of contrast‐enhanced vascularity, reflected in an increase of 9.64% in vessel sharpness.ConclusionThe proposed , featuring a novel context‐aware architecture, demonstrated its capacity as a reference‐free surrogate of structural similarity to quantify motion‐induced degradation of image quality and anatomical plausibility of image content. The validation studies showed robust performance across motion patterns, x‐ray techniques, and anatomical instances. The proposed anatomy‐ and context‐aware metric poses a powerful alternative to conventional motion estimation metrics, and a step forward for application of deep autofocus motion compensation for guidance in clinical interventional procedures.

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Interventional 4D motion estimation and reconstruction of cardiac vasculature without motion periodicity assumption

Cited by 35 publications

References 22 publications

Reconstruction of coronary arteries from X-ray angiography: A review

Reconstruction of coronary arteries from X-ray angiography: A review

Symmetry, outliers, and geodesics in coronary artery centerline reconstruction from rotational angiography

Deformable motion compensation in interventional cone‐beam CT with a context‐aware learned autofocus metric

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