CArdiac VAsculature Reconstruction EValuation

The main goal of CAVAREV is to enable an easy and objective comparison of different dynamic reconstruction algorithms.

The area of application is the 3-D and 4-D reconstruction of contrasted cardiac vessels, e.g. the coronary arteries using C-arm CT (rotational angiography). Various methods exist in literature with lots of nice results. However, the results can vary significantly between phantom and real clinical data.

Therefore, we provide:

Open (i.e. public) and easy accessible projection data.
Anatomically and physiologically correct projection data based on patient data.
Two degrees of difficulty: (a) Strictly periodic cardiac motion and (b) an aperiodic combination of cardiac and breathing motion.

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Hall of Fame

Method	Description	Q3D (card)	Q3D (card+resp)	Q4D (card)	Q4D (card+resp)
Spatio-temporally Regularized ECG-Gated Reconstruction [1]	Iterative reconstruction minimizing spatial and temporal total variation using narrowest gating windows possible	0.87618	0	0	0
Motion Compensation for ECG-Gated Reconstruction with Large Window Sizes [2]	ECG-Gated reconstruction & 2-D--2-D registration-based motion compensation	0.822566	0.385929	0.319175	0.105012
Residual Motion Compensation for ECG-Gated Reconstruction [3]	ECG-Gated reconstruction & 2-D--2-D registration-based motion compensation	0.776345	0	0.279786	0
Streak-Reduced ECG-Gated FDK Reconstruction [4]	ECG-Gated FDK reconstruction using an additional weighting function for reducing streaks artefacts	0.743538	0.2084	0.617927	0.107976
Dynamic Level Set Reconstruction [5]	Symbolic 4D Reconstruction Using Variational Dynamic Level Sets	0.691807	0	0.605452	0
ECG-Gated FDK Reconstruction	ECG-Gated FDK reconstruction using a cosine-square based weighting function	0.594723	0.156411	0.500699	0.0965542
Standard FDK Reconstruction	Non-gated FDK-Reconstruction without Gating, i.e. all projection data is used without any correction	0.431399	0.206042	0.347023	0.105329

[1] O Taubmann et al. 2017: Spatio-temporally Regularized 4-D Cardiovascular C-arm CT Reconstruction Using a Proximal Algorithm. IEEE International Symposium on Biomedical Imaging (ISBI), Melbourne, Australia, pp. 52-55

[2] C Schwemmer et al. 2013: Opening Windows ‒ Increasing Window Size in Motion-Compensated ECG-gated Cardiac Vasculature Reconstruction. Proc. 12th intl. meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, Lake Tahoe, CA, USA, pp. 50-53

[3] C Schwemmer et al. 2013: Residual Motion Compensation in ECG-Gated Interventional Cardiac Vasculature Reconstruction. Phys. Med. Biol. 58(11), pp. 3717-3737

[4] C Rohkohl et al. 2008: C-Arm CT: Reconstruction of Dynamic High Contrast Objects Applied to the Coronary Sinus. IEEE NSS-MIC (Nuclear Science Symposium-Medical Imaging Conference), Dresden, Germany, pp. M10-328

[5] A Keil et al. 2009: Dynamic Cone-Beam Reconstruction Using a Variational Level Set Formulation. Fully3D, Beijing, China

Some results for iterative methods which were not made public on the web platform have been published in:

H Wu et al. 2011: Total Variation Regularization Method for 3-D Rotational Coronary Angiography. BVM (Bildverarbeitung für die Medizin), Lübeck, Germany, pp. 434-438